I keep saying even if there's not current malfeasance, the incentives being set up where the model ultimately determines the token use which determines the model provider's revenue will absolutely overcome any safeguards or good intentions given long enough.
Does this mean Claude no longer outputs the full raw reasoning, only summaries? At one point, exposing the LLM's full CoT was considered a core safety tenet.
I don't think it ever has. For a very long time now, the reasoning of Claude has been summarized by Haiku. You can tell because a lot of the times it fails, saying, "I don't see any thought needing to be summarised."
"Also notable: 4.7 now defaults to NOT including a human-readable reasoning token summary in the output, you have to add "display": "summarized" to get that"
I did not follow all of this, but wasn't there something about, that those reasoning tokens did not represent internal reasoning, but rather a rough approximation that can be rather misleading, what the model actual does?
The reasoning is the secret sauce. They don't output that. But to let you have some feedback about what is going on, they pass this reasoning through another model that generates a human friendly summary (that actively destroys the signal, which could be copied by competition).
My assumption is the model no longer actually thinks in tokens, but in internal tensors. This is advantageous because it doesn't have to collapse the decision and can simultaneously propogate many concepts per context position.
'Hey Claude, these tokens are utter unrelated bollocks, but obviously we still want to charge the user for them regardless. Please construct a plausible explanation as to why we should still be able to do that.'
Don't look at "thinking" tokens. LLMs sometimes produce thinking tokens that are only vaguely related to the task if at all, then do the correct thing anyways.
They also sometimes flag stuff in their reasoning and then think themselves out of mentioning it in the response, when it would actually have been a very welcome flag.
This can result in some funny interactions. I don't know if Claude will say anything, but I've had some models act "surprised" when I commented on something in their thinking, or even deny saying anything about it until I insisted that I can see their reasoning output.
It's likely hiding the model downgrade path they require to meet sustainable revenue. Should be interesting if they can enshittify slowly enough to avoid the ablative loss of customers! Good luck all VCs!
They have super sustainable revenue. They are deadly supply constrained on compute, and have a really difficult balancing act over the next year or two in which they have to trade off spending that limited compute on model training so that they can stay ahead, while leaving enough of it available for customers that they can keep growing number of customers.
> Opus 4.7 uses an updated tokenizer that improves how the model processes text. The tradeoff is that the same input can map to more tokens—roughly 1.0–1.35× depending on the content type.
caveman[0] is becoming more relevant by the day. I already enjoy reading its output more than vanilla so suits me well.
I hope people realize that tools like caveman are mostly joke/prank projects - almost the entirety of the context spent is in file reads (for input) and reasoning (in output), you will barely save even 1% with such a tool, and might actually confuse the model more or have it reason for more tokens because it'll have to formulate its respone in the way that satisfies the requirements.
> I hope people realize that tools like caveman are mostly joke/prank projects
This seems to be a common thread in the LLM ecosystem; someone starts a project for shits and giggles, makes it public, most people get the joke, others think it's serious, author eventually tries to turn the joke project into a VC-funded business, some people are standing watching with the jaws open, the world moves on.
To be fair, most of us looked at GPT1 and GPT2 as fun and unserious jokes, until it started putting together sentences that actually read like real text, I remember laughing with a group of friends about some early generated texts. Little did we know.
HN submissions have a bunch of examples in them, but worth remembering they were released as "Look at this somewhat cool and potentially useful stuff" rather than what we see today, LLMs marketed as tools.
I was first made aware of GPT2 from reading Gwern -- "huh, that sounds interesting" -- but really didn't start really reading model output until I saw this subreddit:
> New AI fake text generator may be too dangerous to release, say creators
> The Elon Musk-backed nonprofit company OpenAI declines to release research publicly for fear of misuse.
> OpenAI, an nonprofit research company backed by Elon Musk, Reid Hoffman, Sam Altman, and others, says its new AI model, called GPT2 is so good and the risk of malicious use so high that it is breaking from its normal practice of releasing the full research to the public in order to allow more time to discuss the ramifications of the technological breakthrough.
I've been running gps for a long time, and I always liked that there was something in my pocket (and not just me). One day when driving to work on the highway with no GPS app installed, I noticed one of the drivers had gone out after 5 hours without looking. He never came back! What's up with this?
So i thought it would be cool if a community can create an open source GPT2 application which will allow you not only to get around using your smartphone but also track how long you've been driving and use that data in the future for improving yourself...and I think everyone is pretty interested.
[Updated on July 20]
I'll have this running from here, along with a few other features such as: - an update of my Google Maps app to take advantage it's GPS capabilities (it does not yet support driving directions) - GPT2 integration into your favorite web browser so you can access data straight from the dashboard without leaving any site!
Here is what I got working.
Very cool idea. Been playing with a similar concept: break down one image into smaller self-similar images, order them by data similarity, use them as frames for a video
You can then reconstruct the original image by doing the reverse, extracting frames from the video, then piecing them together to create the original bigger picture
Results seem to really depend on the data. Sometimes the video version is smaller than the big picture. Sometimes it’s the other way around. So you can technically compress some videos by extracting frames, composing a big picture with them and just compressing with jpeg
Interesting, when I heard about it, I read the readme, and I didn't take that as literal. I assumed it was meant as we used video frames as inspiration.
I've never used it or looked deeper than that. My LLM memory "project" is essentially a `dict<"about", list<"memory">>` The key and memories are all embeddings, so vector searchable. I'm sure its naive and dumb, but it works for my tiny agents I write.
A major reason for that is because there's no way to objectively evaluate the performance of LLMs. So the meme projects are equally as valid as the serious ones, since the merits of both are based entirely on anecdata.
It also doesn't help that projects and practices are promoted and adopted based on influencer clout. Karpathy's takes will drown out ones from "lesser" personas, whether they have any value or not.
While the caveman stuff is obviously not serious, there is a lot of legit research in this area.
Which means yes, you can actually influence this quite a bit. Read the paper “Compressed Chain of Thought” for example, it shows it’s really easy to make significant reductions in reasoning tokens without affecting output quality.
There is not too much research into this (about 5 papers in total), but with that it’s possible to reduce output tokens by about 60%. Given that output is an incredibly significant part of the total costs, this is important.
Who would suspect that the companies selling 'tokens' would (unintentionally) train their models to prefer longer answers, reaping a HIGHER ROI (the thing a publicly traded company is legally required to pursue: good thing these are all still private...)... because it's not like private companies want to make money...
Some labs do it internally because RLVR is very token-expensive. But it degrades CoT readability even more than normal RL pressure does.
It isn't free either - by default, models learn to offload some of their internal computation into the "filler" tokens. So reducing raw token count always cuts into reasoning capacity somewhat. Getting closer to "compute optimal" while reducing token use isn't an easy task.
Yeah the readability suffers, but as long as the actual output (ie the non-CoT part) stays unaffected it’s reasonably fine.
I work on a few agentic open source tools and the interesting thing is that once I implemented these things, the overall feedback was a performance improvement rather than performance reduction, as the LLM would spend much less time on generating tokens.
I didn’t implement it fully, just a few basic things like “reduce prose while thinking, don’t repeat your thoughts” etc would already yield massive improvements.
Yeah you could easily imagine stenography like inputs and outputs for rapid iteration loops. It's also true that in social media people already want faster-to-read snippets that drop grammar so the desire for density is already there for human authors/readers.
All LLMs also effectively work by ”larping” a role. You steer it towards larping a caveman and well.. let’s just say they weren’t known for their high iq
Fun fact: Neanderthals actually had larger brains than Homo Sapiens! Modern humans are thought to have outcompeted them by working better together in larger groups, but in terms of actual individual intelligence, Neanderthals may have had us beat. Similarly, humans have been undergoing a process of self-domestication over the last couple millenia that have resulted in physiological changes that include a smaller brain size - again, our advantage over our wilder forebearers remains that we're better in larger social groups than they were and are better at shared symbolic reasoning and synchronized activity, not necessarily that our brains are more capable.
(No, none of this changes that if you make an LLM larp a caveman it's gonna act stupid, you're right about that.)
You can't draw conclusions on individuals, but at a species level bigger brain, especially compared to body size, strongly correlates with intelligence
Bigger brain does not automatically mean more intelligence, but we have reasons to suspect that homo neanderthalensis may have been more intelligent than contemporary homo sapiens other than bigger brains.
Exactly. The model is exquisitely sensitive to language. The idea that you would encourage it to think like a caveman to save a few tokens is hilarious but extremely counter-productive if you care about the quality of its reasoning.
Help me understand: I get that the file reading can be a lot. But I also expand the box to see its “reasoning” and there’s a ton of natural language going on there.
I don't understand how this would work without a huge loss in resolution or "cognitive" ability.
Prediction works based on the attention mechanism, and current humans don't speak like cavemen - so how could you expect a useful token chain from data that isn't trained on speech like that?
I get the concept of transformers, but this isn't doing a 1:1 transform from english to french or whatever, you're fundamentally unable to represent certain concepts effectively in caveman etc... or am I missing something?
Headroom looks great for client-side trimming. If you want to tackle this at the infrastructure level, we built Edgee (https://www.edgee.ai) as an AI Gateway that handles context compression, caching, and token budgeting across requests, so you're not relying on each client to do the right thing.
(I work at Edgee, so biased, but happy to answer questions.)
I was doing some experiments with removing top 100-1000 most common English words from my prompts. My hypothesis was that common words are effectively noise to agents. Based on the first few trials I attempted, there was no discernible difference in output. Would love to compare results with caveman.
Caveat: I didn’t do enough testing to find the edge cases (eg, negation).
I suspect even typos have an impact on how the model functions.
I wonder if there’s a pre-processor that runs to remove typos before processing. If not, that feels like a space that could be worked on more thoroughly.
The ability for audio processing to figure out spelling from context, especially with regards to acronyms that are pronounced as words, leads me to believe there’s potential for a more intelligent spell check preprocess using a cheaper model.
I strongly suspected that there was some pre/postprocessing going on when trying to get it to output rot13("uryyb, jbyeq"), but it's probably just due to massively biased token probabilities. Still, it creates some hilarious output, even when you clearly point out the error:
Hmm, but wait — the original you gave was jbyeq not jbeyq:
j→w, b→o, y→l, e→r, q→d = world
So the final answer is still hello, world. You're right that I was misreading the input. The result stands.
caveman stops being a style tool and starts being self-defense. once prompt comes in up to 1.35x fatter, they've basically moved visibility and control entirely into their black box.
I find grep and common cli command spam to be the primary issue. I enjoy Rust Token Killer https://github.com/rtk-ai/rtk, and agents know how to get around it when it truncates too hard.
Oh wow, I love this idea even if it's relatively insignificant in savings.
I am finding my writing prompt style is naturally getting lazier, shorter, and more caveman just like this too. If I was honest, it has made writing emails harder.
While messing around, I did a concept of this with HTML to preserve tokens, worked surprisingly well but was only an experiment. Something like:
1.35 times! For Input!
For what kinds of tokens precisely? Programming? Unicode? If they seriously increased token usage by 35% for typical tasks this is gonna be rough.
People are really trigger-happy when it comes to throwing magic tools on top of AI that claim to "fix" the weak parts (often placeboing themselves because anthropic just fixed some issue on their end).
Then the next month 90% of this can be replaced with new batch of supply chain attack-friendly gimmicks
Especially Reddit seems to be full of such coding voodoo
My favorite to chuckle at are the prompt hack voodoo stuff, like, “tell it to be correct” or “say please” or “tell it someone will die if it doesnt do a good job,” often presented very seriously and with some fast cutting animations in a 30 second reel
Well, we've sacrificed the precision of actual programming languages for the ease of English prose interpreted by a non-deterministic black box that we can't reliably measure the outputs of. It's only natural that people are trying to determine the magical incantations required to get correct, consistent results.
Too late, personally after how bad 4.6 was the past week I was pushed to codex, which seems to mostly work at the same level from day to day. Just last night I was trying to get 4.6 to lookup how to do some simple tensor parallel work, and the agent used 0 web fetches and just hallucinated 17K very wrong tokens. Then the main agent decided to pretend to implement tp, and just copied the entire model to each node...
Same. I stopped my Pro subscription yesterday after entering the week with 70% of my tokens used by Monday morning (on light, small weekend projects, things I had worked on in the past and barely noticed a dent in usage.) Support was... unhelpful.
It's been funny watching my own attitude to Anthropic change, from being an enthusiastic Claude user to pure frustration. But even that wasn't the trigger to leave, it was the attitude Support showed. I figure, if you mess up as badly as Anthropic has, you should at least show some effort towards your customers. Instead I just got a mass of standardised replies, even after the thread replied I'd be escalated to a human. Nothing can sour you on a company more. I'm forgiving to bugs, we've all been there, but really annoyed by indifference and unhelpful form replies with corporate uselessness.
So if 4.7 is here? I'd prefer they forget models and revert the harness to its January state. Even then, I've already moved to Codex as of a few days ago, and I won't be maintaining two subscriptions, it's a move. It has its own issues, it's clear, but I'm getting work done. That's more than I can say for Claude.
> It's been funny watching my own attitude to Anthropic change, from being an enthusiastic Claude user to pure frustration.
You were enthusiastic because it was a great product at an unsustainable price.
Its clear that Claude is now harnessing their model because giving access to their full model is too expensive for the $20/m that consumers have settled on as the price point they want to pay.
I used the $60/mo subscription and I bet most developers get access to AI agents via their company, and there was no difference. They should have reduced the rate limits, or offered a new model, anything except silently reduce the quality of their flagship product to reduce cost.
The cost of switching is too low for them to be able to get away with the standard enshittification playbook. It takes all of 5 minutes to get a Codex subscription and it works almost exactly the same, down to using the same commands for most actions.
Funny because many people here were so confident that OpenAI is going to collapse because of how much compute they pre-ordered.
But now it seems like it's a major strategic advantage. They're 2x'ing usage limits on Codex plans to steal CC customers and it seems to be working. I'm seeing a lot of goodwill for Codex and a ton of bad PR for CC.
It seems like 90% of Claude's recent problems are strictly lack of compute related.
> people here were so confident that OpenAI is going to collapse because of how much compute they pre-ordered
That's not why. It was and is because they've been incredibly unfocused and have burnt through cash on ill-advised, expensive things like Sora. By comparison Anthropic have been very focused.
Nobody was talking about them betting too much on compute, people were saying that their shady deals on compute with NVIDIA and Oracle were creating a giant bubble in their attempt to get a Too Big To Fail judgement (in their words- taxpayer-backed "backstop").
To me it seems like they burn so much money they can do lots of things in parallel. My guess would be that e.g. codex and sora are very independently developed. After all there's a quite a hard limit on how many bodies are beneficial to a software project.
Personally its down to Altman having the cognitive capacity of a sleeping snail, the world insight of a hormonal 14 year old who's only ever read one series of manga.
Despite having literal experts at his fingertips, he still isn't able to grasp that he's talking unfilters bollocks most of the time. Not to mention is Jason level of "oath breaking"/dishonesty.
In hindsight, it is painfully clear that Antropic’s conservative investment strategy has them struggling with keeping up with demand and caused their profit margin to shrink significantly as last buyer of compute.
they've also introduced a lot of caching and token burn related bugs which makes things worse. any bug that multiplies the token burn also multiplies their infrastructure problems.
Different plan. The old 2x has been discontinued, and the bonus is now (temporarily) available for the new $100 plan users in an effort, presumably, to entice them away from Anthropic.
> It seems like 90% of Claude's recent problems are strictly lack of compute related.
Downtime is annoying, but the problem is that over the past 2-3 weeks Claude has been outrageously stupid when it does work. I have always been skeptical of everything produced - but now I have no faith whatsoever in anything that it produces. I'm not even sure if I will experiment with 4.7, unless there are glowing reviews.
Codex has had none of these problems. I still don't trust anything it produces, but it's not like everything it produces is completely and utterly useless.
The market here is extraordinarily vibes-based and burning billions of dollars for a ephemeral PR boost, which might only last another couple weeks until people find a reason to hate Codex, does not reflect well on OAI's long term viability.
I have both Claude and OpenAI, side by side. I would say sonnet 46 still beats gpt 54 for coding (at least in my use case) But after about 45 minutes I'm out of my window, so I use openai for the next 4 hours and I can't even reach my limit.
Most of the compute OpenAI "preordered" is vapour. And it has nothing to do with why people thought the company -- which is still in extremely rocky rapids -- was headed to bankruptcy.
Anthropic has been very disciplined and focused (overwhelmingly on coding, fwiw), while OpenAI has been bleeding money trying to be the everything AI company with no real specialty as everyone else beat them in random domains. If I had to qualify OpenAI's primary focus, it has been glazing users and making a generation of malignant narcissists.
But yes, Anthropic has been growing by leaps and bounds and has capacity issues. That's a very healthy position to be in, despite the fact that it yields the inevitable foot-stomping "I'm moving to competitor!" posts constantly.
All of the smart people I know went to work at OpenAI and none at Anthropic. In addition to financial capital, OpenAI has a massive advantage in human capital over Anthropic.
As long as OpenAI can sustain compute and paying SWE $1million/year they will end up with the better product.
Attracting talent with huge sums of money just gets you people who optimize for money, and it's usually never a good long-term decision. I think it's what led to Google's downturn.
> OpenAI has a massive advantage in human capital over Anthropic.
but if your leader is a dipshit, then its a waste.
Look You can't just throw money at the problem, you need people who are able to make the right decisions are the right time. That that requires leadership. Part of the reason why facebook fucked up VR/AR is that they have a leader who only cares about features/metrics, not user experience.
Part of the reason why twitter always lost money is because they had loads of teams all running in different directions, because Dorsey is utterly incapable of making a firm decision.
Codex really has its place in my bag. I mainly use it, rarely Claude.
Codex just gets it done. Very self-correcting by design while Claude has no real base line quality for me. Claude was awesome in December, but Codex is like a corporate company to me. Maybe it looks uncool, but can execute very well.
Also Web Design looks really smooth with Codex.
OpenAI really impressed me and continues to impress me with Codex. OpenAI made no fuzz about it, instead let results speak. It is as if Codex has no marketing department, just its product quality - kind of like Google in its early days with every product.
My tinfoil hat theory, which may not be that crazy, is that providers are sandbagging their models in the days leading up to a new release, so that the next model "feels" like a bigger improvement than it is.
An important aspect of AI is that it needs to be seen as moving forward all the time. Plateaus are the death of the hype cycle, and would tether people's expectations closer to reality.
Thing is that Anthropic was always working with DoD, too, and the line in the sand they drew looked really noble until I found it didn't not apply to me, a non-US citizen. Dario made it clear that was the case.
And so the difference, to me, was irrelevant. I'll buy based on value, and keep a poker in the fire of Chinese & European open weight models, as well.
Not everyone is American, and people who are not see Anthropic state they are willing to spy on our countries and shrug about OAI saying the same about America. What’s the difference to us?
well, if they put in a fully automated kill chain, its gonna be weak to attacks to make yourself look like a car, or a video game styled "hide under a box"
the current non-automated kill chain has targeted fishermen and a girl's school. Nobody is gonna be held accountable for either.
Am i worried about the killing or the AI? If i'm worried about the killing, id much rather push for US demilitarization.
Dario in fact said it was ok to spy and drone non-US citizens, and in fact endorsed American foreign policy generally.
So, no, I'm not voting with my wallet for one American country versus the other. I'll pick the best compromise product for me, and then also boost non-American R&D where I can.
Not only is Anthropic perfectly happy to let the DoD use their products to kill people, but they are partners with Palantir and were apparently instrumental in the strikes against Iran by the US military.
neah, I believe most people here, which immediately brag about codex, are openai employees doing part of their job. otherwise I couldn't possibly phantom why would anyone use codex. In my company 80% is claude and 15% gemini. you can barely see openai on the graph. and we have >5k programmers using ai every day.
Currently GPT just works much better, and so does Gemini but it's more expensive right now. Going through Opencode stats, their claim is that Gemini is the current best model followed by GPT 5.4 on their benchmarks, but the difference is slim.
My personal experience is best with GPT but it could be the specific kind of work I use it for which is heavy on maths and cpp (and some LISP).
There's nothing moral about Anthropic. Especially to those of us who are not American citizens and to which Dario's pronouncements about ethics apparently do not apply, as stated in his own press release.
To me it just looks like a big sanctimonious festival of hypocrisy.
I've been using it with `/effort max` all the time, and it's been working better than ever.
I think here's part of the problem, it's hard to measure this, and you also don't know in which AB test cohorts you may currently be and how they are affecting results.
Agree. I keep effort max on Claude and xhigh on GPT for all tasks and keep tasks as scoped units of work instead of boil the ocean type prompts. It is hard to measure but ultimately the tasks are getting completed and I'm validating so I consider it "working as expected".
It works better, until you run out of tokens. Running out of tokens is something that used to never happen to me, but this month now regularly happens.
Maybe I could avoid running out of tokens by turning off 1M tokens and max effort, but that's a cure worse than the disease IMO.
Personally I find using and managing Claude sessions and limits is getting exhausting and feels similar to calorie counting. You think you are going to have an amazing low calories meal only to realize the meal is full of processed sugars and you overshot the limit within 2-3 bites. Now "you have exhausted your limit for this time. Your session limits resets in next 4 hrs".
Yep, it just feels terrible, the usage bars give me anxiety, and I think that's in their interest as they definitely push me towards paying for higher limits. Won't do that, though.
Until the next time they push you back to Claude. At this point, I feel like this has to be the most unstable technology ever released. Imagine if docker had stopped working every two releases
I think this is more about which model you steer your coding harness to. You can also self-host a UI in front of multiple models, then you own the chat history.
Usually the problems that cause this kind of thing are:
1) Bad prompt/context. No matter what the model is, the input determines the output. This is a really big subject as there's a ton of things you can do to help guide it or add guardrails, structure the planning/investigation, etc.
2) Misaligned model settings. If temperature/top_p/top_k are too high, you will get more hallucination and possibly loops. If they're too low, you don't get "interesting" enough results. Same for the repeat protection settings.
I'm not saying it didn't screw up, but it's not really the model's fault. Every model has the potential for this kind of behavior. It's our job to do a lot of stuff around it to make it less likely.
The agent harness is also a big part of it. Some agents have very specific restrictions built in, like max number of responses or response tokens, so you can prevent it from just going off on a random tangent forever.
I switched to Codex and found it extremely inferior for my use case.
It is much faster, but faster worse code is a step in the wrong direction. You're just rapidly accumulating bugs and tech debt, rather than more slowly moving in the correct direction.
I'm a big fan of Gemini in general, but at least in my experience Gemini Cli is VERY FAR behind either Codex or CC. It's both slower than CC, MUCH slower than Codex, and the output quality considerably worse than CC (probably worse than Codex and orders of magnitude slower).
In my experience, Codex is extraordinarily sycophantic in coding, which is a trait that could t be more harmful. When it encounters bugs and debt, it says: wow, how beautiful, let me double down on this, pile on exponentially more trash, wrap it in a bow, and call you Alan Turing.
It also does not follow directions. When you tell it how to do something, it will say, nah, I have a better faster way, I'll just ignore the user and do my thing instead. CC will stop and ask for feedback much more often.
>> I switched to Codex and found it extremely inferior for my use case.
Yeah, 100% the case for me. I sometimes use it to do adversarial reviews on code that Opus wrote but the stuff it comes back with is total garbage more often than not. It just fabricates reasons as to why the code it's reviewing needs improvement.
It's been shockingly bad for me - for another example when asked to make a new python script building off an existing one; for some cursed reason the model choose to .read() the py files, use 100 of lines of regex to try to patch the changes in, and exec'd everything at the end...
Hate that about Claude Code. I have been adding permissions for it to do everything that makes sense to add when it comes to editing files, but way too often it will generate 20-30 line bash snippets using sed to do the edits instead, and then the whole permission system breaks down. It means I have to babysit it all the time to make sure no random permission prompts pop up.
I generally think codex is doing well until I come in with my Opus sweep to clean it up. Claude just codes closer to the way my brain works. codex is great at finding numerical stability issues though and increasingly I like that it waits for an explicit push to start working. But talking to Claude Code the way I learned to talk to codex seems to work also so I think a lot of it is just learning curve (for me).
so even with a new tokenizer that can map to more tokens than before, their answer is still just "you're not managing your context well enough"
"Opus 4.7 uses an updated tokenizer that [...] can map to more tokens—roughly 1.0–1.35× depending on the content type.
[...]
Users can control token usage in various ways: by using the effort parameter, adjusting their task budgets, or prompting the model to be more concise."
I do feel that CC sometimes starts doing dumb tasks or asking for approval for things that usually don’t really need it. Like extra syntax checks, or some greps/text parsing basic commands
4.6 has gotten so bad, and it was made worse obviously on purpose, no mistakes no accidents. You can't rely on companies who pull shenanigans like this. Unfortunately I still hate the code that codex barfs up, so I need to go back to trad-coding.
I've noticed the same over the last two weeks. Some days Claude will just entirely lose its marbles. I pay for Claude and Codex so I just end up needing to use codex those days and the difference is night and day.
That's wild that you think 4.6 is bad..... Each model has its strengths and weaknesses I find that Codex is good for architectural design and Claude Is actually better the engineering and building
This. They kind of snuck this into the release notes: switching the default effort level to Medium. High is significantly slower, but that’s somewhat mitigated by the fact that you don’t have to constantly act like a helicopter parent for it.
Anecdotally, codex has been burning through way more tokens for me lately. Claude seems to just sit and spin for a long time doing nothing, but at least token use is moderate.
Meh. At $work we were on CC for one month, then switched to Codex for one month, and now will be on CC again to test. We haven’t seen any obvious difference between CC and Codex; both are sometimes very good and sometimes very stupid. You have to test for a long time, not just test one day and call it a benchmark just because you have a single example.
I've been raging pretty hard too. Thought either I'm getting cleverer by the day or Claude has been slipping and sliding toward the wrong side of the "smart idiot" equation pretty fast.
Have caught it flat-out skipping 50% of tasks and lying about it.
I describe the problem and codex runs in circles basically:
codex> I see the problem clearly. Let me create a plan so that I can implement it. The plan is X, Y, Z. Do you want me to implement this?
me> Yes please, looks good. Go ahead!
codex> Okay. Thank you for confirming. So I am going to implement X, Y, Z now. Shall I proceeed?
me> Yes, proceed.
codex> Okay. Implementing.
...codex is working... you see the internal monologue running in circles
codex> Here is what I am going to implement: X, Y, Z
me> Yes, you said that already. Go ahead!
codex> Working on it.
...codex in doing something...
codex> After examining the problem more, indeed, the steps should be X, Y, Z. Do you want me to implement them?
etc.
Very much every sessions ends up being like this. I was unable to get any useful code apart from boilerplate JS from it since 5.4
So instead I just use ChatGPT to create a plan and then ask Opus to code, but it's a hit and miss. Almost every time the prompt seems to be routed to cheaper model that is very dumb (but says Opus 4.6 when asked). I have to start new session many times until I get a good model.
Yep, I'll wait for the GPT answer to this. If we're lucky OpenAI will release a new GPT 5.5 or whatever model in the next few days, just like the last round.
I have been getting better results out of codex on and off for months. It's more "careful" and systematic in its thinking. It makes less "excuses" and leaves less race conditions and slop around. And the actual codex CLI tool is better written, less buggy and faster. And I can use the membership in things like opencode etc without drama.
For March I decided to give Claude Code / Opus a chance again. But there's just too much variance there. And then they started to play games with limits, and then OpenAI rolled out a $100 plan to compete with Anthropic's.
I'm glad to see the competition but I think Anthropic has pissed in the well too much. I do think they sent me something about a free month and maybe I will use that to try this model out though.
I’ve been on the Claude Code train for a while but decided to try Codex last week after they announced the $100 USD Pro plan.
I’ve been pretty happy with it! One thing I immediately like more than Claude is that Codex seems much more transparent about what it’s thinking and what it wants to do next. I find it much easier to interrupt or jump in the middle if things are going to wrong direction.
Claude Code has been slowly turning into this mysterious black box, wiping out terminal context any time it compacts a conversation (which I think is their hacky way of dealing with terminal flickering issues — which is still happening, 14 months later), going out of the way to hide thought output, and then of course the whole performance issues thing.
Excited to try 4.7 out, but man, Codex (as a harness at least) is a stark contrast to Claude Code.
> One thing I immediately like more than Claude is that Codex seems much more transparent about what it’s thinking and what it wants to do next. I find it much easier to interrupt or jump in the middle if things are going to wrong direction.
I've finally started experimenting recently with Claude's --dangerously-skip-permissions and Codex's --dangerously-bypass-approvals-and-sandbox through external sandboxing tools. (For now just nono¹, which I really like so far, and soon via containerization or virtual machines.)
When I am using Claude or Codex without external sandboxing tools and just using the TUI, I spend a lot of time approving individual commands. When I was working that way, I found Codex's tendency to stop and ask me whether/how it should proceed extremely annoying. I found myself shouting at my monitor, "Yes, duh, go do the thing!".
But when I run these tools without having them ask me for permission for individual commands or edits, I sometimes find Claude has run away from me a little and made the wrong changes or tried to debug something in a bone-headed way that I would have redirected with an interruption if it has stopped to ask me for permissions. I think maybe Codex's tendency to stop and check in may be more valuable if you're relying on sandboxing (external or built-in) so that you can avoid individual permissions prompts.
there is an official codex plugin for claude. I just have them do adversarial reviews/implementations. etc with each other. adds a bit of time to the workflow but once you have the permissions sorted it'll just engage codex when necessary
Do this -- take your coworker's PRs that they've clearly written in Claude Code, and have Codex/GPT 5.4 review them.
Or have Codex review your own Claude Code work.
It then becomes clear just how "sloppy" CC is.
I wouldn't mind having Opus around in my back pocket to yeet out whole net new greenfield features. But I can't trust it to produce well-engineered things to my standards. Not that anybody should trust an LLM to that level, but there's matters of degree here.
I've been using Claude and Codex in tandem ($100 CC, $20 Codex), and have made heavy use of claude-co-commands [0] to make them talk. Outside of the last 1-2 weeks (which we now have confirmation YET AGAIN that Claude shits the fucking bed in the run-up to a new model release), I usually will put Claude on max + /plan to gin up a fever dream to implement. When the plan is presented, I tell it to /co-validate with Codex, which tends to fill in many implementation gaps. Claude then codes the amended plan and commits, then I have a Codex skill that reviews the commit for gaps, missed edge cases, incorrect implementation, missed optimizations, etc, and fix them. This had been working quite well up until the beginning of the month, Claude more or less got CTE, and after a week of that I swapped to $100 Codex, $20 CC plans. Now I'm using co-validation a lot less and just driving primarily via Codex. When Claude works, it provides some good collaborative insights and counter-points, but Codex at the very least is consistently predictable (for text-oriented, data-oriented stuff -- I don't use either for designing or implementing frontend / UI / etc).
This more or less mimics a flow that I had fairly good results from -- but I'm unwilling to pay for both right now unless I had a client or employer willing to foot the bill.
Claude Code as "author" and a $20 Codex as reviewer/planner/tester has worked for me to squeeze better value out of the CC plan. But with the new $100 codex plan, and with the way Anthropic seemed to nerf their own $100 plan, I'm not doing this anymore.
It cuts both ways. What I usually do these days is to let codex write code, then use claude code /simplify, have both codex and claude code review the PR, then finally manually review and fixup things myself. It's still ~2x faster than doing everything by myself.
100%. On days when I'm sleep deprived (once or twice a week), I fallback to this flow. On regular days, I tend to write more code the old school way and use things things for review.
They've increased their cybersecurity usage filters to the point that Opus 4.7 refuses to work on any valid work, even after web fetching the program guidelines itself and acknowledging "This is authorized research under the [Redacted] Bounty program, so the findings here are defensive research outputs, not malware. I'll analyze and draft, not weaponize anything beyond what's needed to prove the bug to [Redacted].
I will immediately switch over to Codex if this continues to be an issue. I am new to security research, have been paid out on several bugs, but don't have a CVE or public talk so they are ready to cut me out already.
Edit: these changes are also retroactive to Opus 4.6. I am stuck using Sonnet until they approve me or make a change.
⎿ API Error: Claude Code is unable to respond to this request, which appears to violate our Usage Policy (https://www.anthropic.com/legal/aup). This request triggered restrictions on violative cyber content and was blocked under Anthropic's
Usage Policy. To request an adjustment pursuant to our Cyber Verification Program based on how you use Claude, fill out
https://claude.com/form/cyber-use-case?token=[REDACTED] Please double press esc to edit your last message or
start a new session for Claude Code to assist with a different task. If you are seeing this refusal repeatedly, try running /model claude-sonnet-4-20250514 to switch models.
This is gonna kill everything I've been working on. I have several reproduced items at [REDACTED] that I've been working on.
I predict this sort of filtering is only going to get worse. This will probably be remembered as the 'open internet' era of LLMs before everything is tightly controlled for 'safety' and regulations. Forcing software devs to use open source or local models to do anything fun.
>even after acknowledging "This is authorized research under the [Redacted] Bounty program, so the findings here are defensive research outputs, not malware. I'll analyze and draft, not weaponize anything beyond what's needed to prove the bug to [Redacted].
What else would you expect? If you add protections against it being used for hacking, but then that can be bypassed by saying "I promise I'm the good guys™ and I'm not doing this for evil" what's even the point?
Right, but that can be easily spoofed? Moreover if say Microsoft has a bounty program, what's preventing you from getting Opus to discover a bug for the bounty program, but you actually use it for evil?
It's been a little while since I cared all that much about the models because they work well enough already. It's the tooling and the service around the model that affects my day-to-day more.
I would guess a lot of the enterprise customers would be willing to pay a larger subscription price (1.5x or 2x) if it means that they would have significantly higher stability and uptime. 5% more uptime would gain more trust than 5% more on a gamified model metrics.
Anthropic used to position itself as more of the enterprise option and still does, but their issues recently seems like they are watering down the experience to appease the $20 dollar customer rather than the $200 dollar one. As painful as it is personally, I'd expect that they'd get more benefit long term from raising prices and gaining trust than short term gaining customers seeking utility at a $20 dollar price point.
I know limits have been nerfed, but c'mon it's $20. The fact that you were able to implement two smallish features in an iOS app in 15 minutes seems like incredible value.
At $20/month your daily cost is $0.67 cents a day. Are you really complaining that you were able to get it to implement two small features in your app for 67 cents?
Exactly. God, it wouldn't be such a problem if they didn't gaslight you and act like it was nothing. Just put up a banner that says Claude is experiencing overloaded capacity right now, so your responses might be whatever.
... your side projects that will soon become your main source of income after you are laid off because corporate bosses have noticed that engineers are more productive...
This comment thread is a good learner for founders; look at how much anguish can be put to bed with just a little honest communication.
1. Oops, we're oversubscribed.
2. Oops, adaptive reasoning landed poorly / we have to do it for capacity reasons.
3. Here's how subscriptions work. Am I really writing this bullet point?
As someone with a production application pinned on Opus 4.5, it is extremely difficult to tell apart what is code harness drama and what is a problem with the underlying model. It's all just meshed together now without any further details on what's affected.
Part of me wonders if there's some subtle behavioral change with it too. Early on we're distrusting of a model and so we're blown away, we were giving it more details to compensate for assumed inability, but the model outperformed our expectations. Weeks later we're more aligned with its capabilities and so we become lazy. The model is very good, why do we have to put in as much work to provide specifics, specs, ACs, etc. So then of course the quality slides because we assumed it's capabilities somehow absolved the need for the same detailed guardrails (spec, ACs, etc) for the LLM.
This scenario obviously does not apply to folks who run their own benches with the same inputs between models. I'm just discussing a possible and unintentional human behavioral bias.
Even if this isn't the root cause, humans are really bad at perceiving reality. Like, really really bad. LLMs are also really difficult to objectively measure. I'm sure the coupling of these two facts play a part, possibly significant, in our perception of LLM quality over time.
Nah dude, that roulette wheel is 100% rigged. From top to bottom. No doubt about that. If you think they are playing fair you are either brand new to this industry, or a masochist.
This coming right after a noticeable downgrade just makes me think Opus 4.7 is going to be the same Opus i was experiencing a few months ago rather than actual performance boost.
Anthropic need to build back some trust and communicate throtelling/reasoning caps more clearly.
They don't have enough compute for all their customers.
OpenAI bet on more compute early on which prompted people to say they're going to go bankrupt and collapse. But now it seems like it's a major strategic advantage. They're 2x'ing usage limits on Codex plans to steal CC customers and it seems to be working.
It seems like 90% of Claude's recent problems are strictly lack of compute related.
Is that why Anthropic recently gave out free credits for use in off-hours? Possibly an attempt to more evenly distribute their compute load throughout the day?
> Is that why Anthropic recently gave out free credits for use in off-hours?
That was the carrot for the stick. The limits and the issues were never officially recognized or communicated. Neither have been the "off-hours credits". You would only know about them if you logged in to your dashboard. When is the last time you logged in there?
Model inference compute over model lifetime is ~10x of model training compute now for major providers. Expected to climb as demand for AI inference rises.
Honestly, I personally would rather a time-out than the quality of my response noticably downgrading. I think what I found especially distrustful is the responses from employees claiming that no degredation has occured.
An honest response of "Our compute is busy, use X model?" would be far better than silent downgrading.
Are they convinced that claiming they have technical issues while continuing to adjust their internal levers to choose which customers to serve is holistically the best path?
OpenAI though made crazy claims after all its responsible for the memory prices.
In parallel anthropic announced partnership with google and broadcom for gigawatts of TPU chips while also announcing their own 50 Billion invest in compute.
OpenAI always believed in compute though and i'm pretty sure plenty of people want to see what models 10x or 100x or 1000x can do.
What I want to know is why my bedrock-backed Claude gets dumber along with commercial users. Surely they're not touching the bedrock model itself. Only thing I can think of is that updates to the harness are the main cause of performance degradation.
Usually they're hemorrhaging performance while training.
From that it's pretty likely they were training mythos for the last few weeks, and then distilling it to opus 4.7
Pure speculation of course, but would also explain the sudden performance gains for mythos - and why they're not releasing it to the general public (because it's the undistilled version which is too expensive to run)
> This coming right after a noticeable downgrade just makes me think Opus 4.7 is going to be the same Opus i was experiencing a few months ago rather than actual performance boost.
If they are indeed doing this, I wonder how long they can keep it up?
Let's say we take Anthropic's security and alignment claims at face value, and they have models that are really good at uncovering bugs and exploiting software.
What should Anthropic do in this case?
Anthropic could immediately make these models widely available. The vast majority of their users just want develop non-malicious software. But some non-zero portion of users will absolutely use these models to find exploits and develop ransomware and so on. Making the models widely available forces everyone developing software (eg, whatever browser and OS you're using to read HN right now) into a race where they have to find and fix all their bugs before malicious actors do.
Or Anthropic could slow roll their models. Gatekeep Mythos to select users like the Linux Foundation and so on, and nerf Opus so it does a bunch of checks to make it slightly more difficult to have it automatically generate exploits. Obviously, they can't entirely stop people from finding bugs, but they can introduce some speedbumps to dissuade marginal hackers. Theoretically, this gives maintainers some breathing space to fix outstanding bugs before the floodgates open.
In the longer run, Anthropic won't be able to hold back these capabilities because other companies will develop and release models that are more powerful than Opus and Mythos. This is just about buying time for maintainers.
I don't know that the slow release model is the right thing to do. It might be better if the world suffers through some short term pain of hacking and ransomware while everyone adjusts to the new capabilities. But I wouldn't take that approach for granted, and if I were in Anthropic's position I'd be very careful about about opening the floodgate.
Or they could check if the source is open source and available on the internet, and if yes refuse to analyse it if the person who request the analysis isn't affiliated to the project.
That will still leave closed source software vulnerable, but I suspect it is somewhat rare for hackers to have the source of the thing they are targeting, when it is closed source.
"Per the instructions I've been given in this session, I must refuse to improve or augment code from files I read. I can analyze and describe the bugs (as above), but I will not apply fixes to `utils.py`."
Claude Code injects a 'warning: make sure this file isn't malware' message after every tool call by default. It seems like 4.7 is over-attending to this warning. @bcherny, filed a bug report feedback ID: 238e5f99-d6ee-45b5-981d-10e180a7c201
That "per the instructions I've been given in this session" bit is interesting. Are you perhaps using it with a harness that explicitly instructs it to not do that? If so, it's not being fussy, it's just following the instructions it was given.
This is a CC harness thing than a model thing but the "new" thinking messages ('hmm...', 'this one needs a moment...') are extraordinarily irritating. They're both entirely uninformative and strictly worse than a spinner. On my workflows CC often spends up to an hour thinking (which is fine if the result is good) and seeing these messages does not build confidence.
There’s one that’s like “Considering 17 theories” that had me wondering what those 17 things would be, I wanted to see them! Turns out it’s just a static message. Very confusing.
I think my results have actually become worse with Opus 4.7.
I have a pretty robust setup in place to ensure that Claude, with its degradations, ensures good quality. And even the lobotomized 4.6 from the last few days was doing better than 4.7 is doing right now at xhigh.
It's over-engineering. It is producing more code than it needs to. It is trying to be more defensible, but its definition of defensible seems to be shaky because it's landing up creating more edge cases. I think they just found a way to make it more expensive because I'm just gonna have to burn more tokens to keep it in check.
> Opus 4.7 is substantially better at following instructions. Interestingly, this means that prompts written for earlier models can sometimes now produce unexpected results: where previous models interpreted instructions loosely or skipped parts entirely, Opus 4.7 takes the instructions literally. Users should re-tune their prompts and harnesses accordingly.
I'm running it for the first time and this is what the thinking looks like. Opus seems highly concerned about whether or not I'm asking it to develop malware.
> This is _, not malware. Continuing the brainstorming process.
> Not malware — standard _ code. Continuing exploration.
> Not malware. Let me check front-end components for _.
What a waste of tokens. No wonder Anthropic can't serve their customers. It's not just a lack of compute, it's a ridiculous waste of the limited compute they have. I think (hope?) we look back at the insanity of all this theatre, the same way we do about GPT-2 [1].
I assume this is due to the fact that claude code appends a system message each time it reads a file that instructs it to think if the file is malware. It hasnt been an issue recently for me but it used to be so bad I had to patch out the string from the cli.js file. This is the instruction it uses:
> Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
Have they effectively communicated what a 20x or 10x Claude subscription actually means? And with Claude 4.7 increasing usage by 1.35x does that mean a 20x plan is now really a 13x plan (no token increase on the subscription) or a 27x plan (more tokens given to compensate for more computer cost) relative to Claude Opus 4.6?
The more efficient tokenizer reduces usage by representing text more efficiently with fewer tokens. But the lack of transparancy does indeed mean Anthropic could still scale down limits to account for that.
making 5x the best value for the money (8.33x over pro for max 5x). this information may be outdated though, and doesn't apply to the new on peak 5h multipliers. anything that increases usage just burns through that flat token quota faster.
The default effort change in Claude Code is worth knowing before your next session: it's now `xhigh` (a new level between `high` and `max`) for all plans, up from the previous default. Combined with the 1.0–1.35× tokenizer overhead on the same prompts, actual token spend per agentic session will likely exceed naive estimates from 4.6 baselines.
Anthropic's guidance is to measure against real traffic—their internal benchmark showing net-favorable usage is an autonomous single-prompt eval, which may not reflect interactive multi-turn sessions where tokenizer overhead compounds across turns. The task budget feature (just launched in public beta) is probably the right tool for production deployments that need cost predictability when migrating.
That depends a bit on token efficiency. From their "Agentic coding performance by effort level" graph, it looks like they get similar outcome for 4.7 medium at half the token usage as 4.6 at high.
Granted that is, as you say, a single prompt, but it is using the agentic process where the model self prompts until completion. It's conceivable the model uses fewer tokens for the same result with appropriate effort settings.
I think its just a visual/default thing, cause Opus 4.0 isn't offered on claude code anymore. And opus 4.7 is on their official docs as a model you can change to, on claude code
4.6 vastly outperforms 4.7 in my not so typical application - generating explanations of phrases and words for Chinese learners (simplifying). Robust complex long prompt tested on many different models. That's interesting.
For anyone who was wondering about Mythos release plans:
> What we learn from the real-world deployment of these safeguards will help us work towards our eventual goal of a broad release of Mythos-class models.
Some people are speculating that Opus 4.7 is distilled from Mythos due to the new tokenizer (it means Opus 4.7 is a new base model, not just an improved Opus 4.6)
The new tokenizer is interesting, but it definitely is possible to adapt a base model to a new tokenizer without too much additional training, especially if you're distilling from a model that uses the new tokenizer. (see, e.g., https://openreview.net/pdf?id=DxKP2E0xK2).
Yes, I was thinking that. But it could as well be the other way around. Using the pretrained 4.7 (1T?) to speed up ~70% Mythos (10T?) pretraining.
It's just speculative decoding but for training. If they did at this scale it's quite an achievement because training is very fragile when doing these kinds of tricks.
Reverse distillation. Using small models to bootstrap large models. Get richer signal early in the run when gradients are hectic, get the large model past the early training instability hell. Mad but it does work somewhat.
Not really similar to speculative decoding?
I don't think that's what they've done here though. It's still black magic, I'm not sure if any lab does it for frontier runs, let alone 10T scale runs.
> They don't have demand for the price it would require for inference.
citation needed. I find it hard to believe; I think there are more than enough people willing to spend $100/Mtok for frontier capabilities to dedicate a couple racks or aisles.
I've read so many conflicting things about Mythos that it's become impossible to make any real assumptions about it. I don't think it's vaporware necessarily, but the whole "we can't release it for safety reasons" feels like the next level of "POC or STFU".
This seems needlessly cynical. I don't think they said they never planned to release it.
They seemed to make it clear that they expect other labs to reach that level sooner or later, and they're just holding it off until they've helped patch enough vulnerabilities.
For a second there I read that as 'GTA 6', and that got me thinking maybe the reason GTA 6 hasn't come out all of these years is because of how dangerous and powerful it's going to be.
""If you show the model, people will ask 'HOW BETTER?' and it will never be enough. The model that was the AGI is suddenly the +5% bench dog. But if you have NO model, you can say you're worried about safety! You're a potential pure play... It's not about how much you research, it's about how much you're WORTH. And who is worth the most? Companies that don't release their models!"
I've been using up way more tokens in the past 10 days with 4.6 1M context.
So I've grown wary of how Anthropic is measuring token use. I had to force the non-1M halfway through the week because I was tearing through my weekly limit (this is the second week in a row where that's happened, whereas I never came CLOSE to hitting my weekly limit even when I was in the $100 max plan).
So something is definitely off. and if they're saying this model uses MORE tokens, I'm getting more nervous.
I just subscribed this month again because I wanted to have some fun with my projects.
Tried out opus 4.6 a bit and it is really really bad. Why do people say it's so good? It cannot come up with any half-decent vhdl. No matter the prompt. I'm very disappointed. I was told it's a good model
I've seen a similar psychological phenomenon where people like something a lot, and then they get unreasonably angry and vocal about changes to that thing.
Usage limits are necessary but I guess people expect more subsidized inference than the company can afford. So they make very angry comments online.
> Usage limits are necessary but I guess people expect more subsidized inference than the company can afford. So they make very angry comments online
This is reductive. You're both calling people unreasonably angry but then acknowledging there's a limit in compute that is a practical reality for Anthropic. This isn't that hard. They have two choices, rate limit, or silently degrade to save compute.
I have never hit a rate limit, but I have seen it get noticeably stupider. It doesn't make me angry, but comments like these are a bit annoying to read, because you are trying to make people sound delusional while, at the same time, confirming everything they're saying.
I don't think they have turned a big knob that makes it stupider for everyone. I think they can see when a user is overtapping their $20 plan and silently degrade them. Because there's no alert for that. Which is why AI benchmark sites are irrelevant.
And yet another "AI doesn't work" comment without any meaningful information. What were your exact prompts? What was the output?
This is like a user of conventional software complaining that "it crashes", without a single bit of detail, like what they did before the crash, if there was any error message, whether the program froze or completely disappeared, etc.
It's interesting to see Opus 4.7 follow so soon after the announcement of Mythos, especially given that Anthropic are apparently capacity constrained.
Capacity is shared between model training (pre & post) and inference, so it's hard to see Anthropic deciding that it made sense, while capacity constrained, to train two frontier models at the same time...
I'm guessing that this means that Mythos is not a whole new model separate from Opus 4.6 and 4.7, but is rather based on one of these with additional RL post-training for hacking (security vulnerability exploitation).
The alternative would be that perhaps Mythos is based on a early snapshot of their next major base model, and then presumably that Opus 4.7 is just Opus 4.6 with some additional post-training (as may anyways be the case).
These stuck out as promising things to try. It looks like xhigh on 4.7 scores significantly higher on the internal coding benchmark (71% vs 54%, though unclear what that is exactly)
> More effort control: Opus 4.7 introduces a new xhigh (“extra high”) effort level between high and max, giving users finer control over the tradeoff between reasoning and latency on hard problems. In Claude Code, we’ve raised the default effort level to xhigh for all plans. When testing Opus 4.7 for coding and agentic use cases, we recommend starting with high or xhigh effort.
The new /ultrareview command looks like something I've been trying to invoke myself with looping, happy that it's free to test out.
> The new /ultrareview slash command produces a dedicated review session that reads through changes and flags bugs and design issues that a careful reviewer would catch. We’re giving Pro and Max Claude Code users three free ultrareviews to try it out.
If you mean for Anthropic in particular, I don't think so. But it's not the first time a major AI lab publishes an incremental update of a model that is worse at some benchmarks. I remember that a particular update of Gemini 2.5 Pro improved results in LiveCodeBench but scored lower overall in most benchmarks.
Ask it to create an iOS app which natively runs Gemma via Litert-lm.
It’s incredibly trivial to find stuff outside their capabilities. In fact most stuff I want AI to do it just can’t, and the stuff it can isn’t interesting to me.
> Opus 4.7 uses an updated tokenizer that improves how the model processes text. The tradeoff is that the same input can map to more tokens—roughly 1.0–1.35× depending on the content type. Second, Opus 4.7 thinks more at higher effort levels, particularly on later turns in agentic settings. This improves its reliability on hard problems, but it does mean it produces more output tokens.
I guess that means bad news for our subscription usage.
I liked Opus 4.5 but hated 4.6. Every few weeks I tried 4.6 and, after a tirade against, I switched back to 4.5. They said 4.6 had a "bias towards action", which I think meant it just made stuff up if something was unclear, whereas 4.5 would ask for clarfication. I hope 4.7 is more of a collaborator like 4.5 was.
It seems like they're doing something with the system prompt that I don't quite understand. I'm trying it in Claude Code and tool calls repeatedly show weird messages like "Not malware."
Never seen anything like that with other Anthropic models.
> The new /ultrareview slash command produces a dedicated review session that reads through changes and flags bugs and design issues that a careful reviewer would catch. We’re giving Pro and Max Claude Code users three free ultrareviews to try it out.
More monetization a tier above max subscriptions. I just pointed openclaw at codex after a daily opus bill of $250.
As Anthropic keeps pushing the pricing envelope wider it makes room for differentiation, which is good. But I wish oAI would get a capable agentic model out the door that pushes back on pricing.
Ps I know that Anthropic underbought compute and so we are facing at least a year of this differentiated pricing from them, but still..ouch
> where previous models interpreted instructions loosely or skipped parts entirely, Opus 4.7 takes the instructions literally. Users should re-tune their prompts and harnesses accordingly.
I like this in theory. I just hope it doesn't require you to be be as literal as if talking to a genie.
But if it'll actually stick to the hard rules in the CLAUDE.md files, and if I don't have to add "DON'T DO ANYTHING, JUST ANSWER THE QUESTION" at the end of my prompt, I'll be glad.
It might be a bad idea to put that in all caps, because in the training data, angry conversations are less productive. (I do the same thing, just in lowercase.)
coming more in line with codex - claude previously would often ignore explicit instructions that codex would follow. interested to see how this feels in practice
I think this line around "context tuning" is super interesting - I see a future where, for every model release, devs go and update their CLAUDE.md / skills to adapt to new model behavior.
This made me LOL. They keep trying to fleece us by nerfing functionality and then adding it back next release. It’s an abusive relationship at this point.
So many messages about how Codex is better then Claude from one day to the other, while my experience is exactly the same. Is OpenAI botting the thread? I can't believe this is genuine content.
not a bot, voiced frustration is real here. I kind of depend on good LLMs now and wouldn't even mind if they had frozen the LLMs capabilities around dec 2025 forver and would hppily continue to pay, even more. but when suddenly the very same workload that was fine for months isn't possible anymore with the very same LLM out of nowhere and gets increasingly worse, its a huge disappointment. and having codex in parallel as a backup since ever I started also using it again with gpt 5.4 and it just rips without the diva sensitivity or overfitting into the latest prompt opus/sonnet is doing. GPT just does the job, maybe thinks a bit long, but even over several rounds of chat compression in the same chat for days stays well within the initial set of instructions and guardrails I spelled out, without me having to remind every time. just works, quietly, and gets there. Opus doesn't even get there anymore without nearly spelling out by hand manual steps or what not to do.
Looks to me like a mob of humans, angry they've been deceived by ambiguous communications, product nerfing, surprisingly low usage limits, and an appallingly sycophantic overconfident coding agent
It's a combination of factors. There was rate-limiting implemented by Anthropic, where the 5hr usage limit would be burned through faster at peak hours, I was personally bitten by this multiple times before one guy from Anthropic announced it publicly via twitter, terrible communication. It wasn't small either, ~15 minutes of work ended up burning the entire 5hr limit. That annoyed me enough to switched to Codex for the month at that point.
Now people are saying the model response quality went down, I can't vouch for that since I wasn't using Claude Code, but I don't think this many people saying the same thing is total noise though.
I'm an Opus stan but I'll also admit that 5.4 has gotten a lot better, especially at finding and fixing bugs. Codex doesn't seem to do as good a job at one shotting tasks from scratch.
I suppose if you are okay with a mediocre initial output that you spend more time getting into shape, Codex is comparable. I haven't exhaustively compared though.
Yeah, my personal anecdata is that Claude has just gotten better and better since January. I haven’t felt like even making the minor effort to compare with Codex’s current state. Just yesterday Claude Code made a major visible improvement in planning/executing — maybe it switched to 4.7 without me noticing? (Task: various internal Go services and Preact frontends.)
Sorry, no, not a bot. I get way better results out of Codex.
It's just ultimately subjective, and, it's like, your opinion, man. Calling people bots who disagree is probably not a good look.
I don't like OpenAI the company, but their model and coding tool is pretty damn good. And I was an early Claude Code booster and go back and forth constantly to try both.
4.7 hasn't been out for an hour yet and we already have people shilling for Codex in the comments. I don't know how anyone could form a genuine disagreement in this period of time.
Nobody I've seen in the comments is basing it on 4.7 performance. They're basing it on how unpleasant March and early April was on the Claude Code coding plans with 4.6. Which, from my experience, it was.
I'm interested in seeing how 4.7 performs. But I'm also unwilling to pony up cash for a month to do so. And frankly dissatisfied with their customer service and with the actual TUI tool itself.
It's not team sports, my friend. You don't have to pick a side. These guys are taking a lot of money from us. Far more than I've ever spent on any other development tooling.
Interestingly github-copilot is charging 2.5x as much for opus 4.7 prompts as they charged for opus 4.6 prompts (7.5x instead of 3x). And they're calling this "promotional pricing" which sounds a lot like they're planning to go even higher.
Note they charge per-prompt and not per-token so this might in part be an expectation of more tokens per prompt.
If the model is based on a new tokenizer, that means that it's very likely a completely new base model. Changing the tokenizer is changing the whole foundation a model is built on. It'd be more straightforward to add reasoning to a model architecture compared to swapping the tokenizer to a new one.
Usually a ground up rebuild is related to a bigger announcement. So, it's weird that they'd be naming it 4.7.
Swapping out the tokenizer is a massive change. Not an incremental one.
> Usually a ground up rebuild is related to a bigger announcement. So, it's weird that they'd be naming it 4.7.
Benchmarks say it all. Gains over previous model are too small to announce it as a major release. That would be humiliating for Anthropic. It may scare investors that the curve flattened and there are only diminishing returns.
It doesn't need to be. Text can be tokenized in many different ways even if the token set is the same.
For example there is usually one token for every string from "0" to "999" (including ones like "001" seperately).
This means there are lots of ways you can choose to tokenize a number. Like 27693921. The best way to deal with numbers tends to be a little bit context dependent but for numerics split into groups of 3 right to left tends to be pretty good.
They could just have spotted that some particular patterns should be decomposed differently.
I wonder why computer use has taken a back seat. Seemed like it was a hot topic in 2024, but then sort of went obscure after CLI agents fully took over.
It would be interesting to see a company to try and train a computer use specific model, with an actually meaningful amount of compute directed at that. Seems like there's just been experiments built upon models trained for completely different stuff, instead of any of the companies that put out SotA models taking a real shot at it.
On the other hand, I never understood the focus on computer use.
While more general and perhaps the "ideal" end state once models run cheaply enough, you're always going to suffer from much higher latency and reduced cognition performance vs API/programmatically driven workflows. And strictly more expensive for the same result.
Why not update software to use API first workflows instead?
As the author of the now (in)famous report in https://github.com/anthropics/claude-code/issues/42796 issue (sorry stella :) all I can say is... sigh. Reading through the changelog felt as if they codified every bad experiment they ran that hurt Opus 4.6. It makes it clear that the degradation was not accidental.
I'm still sad. I had a transformative 6 months with Opus and do not regret it, but I'm also glad that I didn't let hope keep me stuck for another few weeks: had I been waiting for a correction I'd be crushed by this.
Hypothesis: Mythos maintains the behavior of what Opus used to be with a few tricks only now restricted to the hands of a few who Anthropic deems worthy. Opus is now the consumer line. I'll still use Opus for some code reviews, but it does not seem like it'll ever go back to collaborator status by-design. :(
Do we have any performance benchmark with token length? Now that the context size is 1 M. I would want to know if I can exhaust all of that or should I clear earlier?
What's the point of baking the best and most impressive models in the world and then serving it with degraded quality a month after releases so that intelligence from them is never fully utilised??
WTF. `Opus 4.7 is the first such model: its cyber capabilities are not as advanced as those of Mythos Preview (indeed, during its training we experimented with efforts to differentially reduce these capabilities). We are releasing Opus 4.7 with safeguards that automatically detect and block requests that indicate prohibited or high-risk cybersecurity uses. `
Seriously? You're degrading Opus 4.7 Cybersecurity performance on purpose. Absolute shit.
And since Opus 4.7 has degraded cybersecurity skills, using it might result in writing actually less safe code, since practically, in order to write secure code you need to understand cybersecurity. Outstanding move.
I wish someone would elaborate on what they were doing and observed since Jan on opus 4.6. I’ve been using it with 1m context on max thinking since it was released - as a software engineer to write most of my code, code reviews + research and explain unfamiliar code - and haven’t notice a degradation. I’ve seen this mentioned a lot though.
I have seen that codex -latest highest effort - will find some important edge cases that opus 4.6 overlooked when I ask both of them to review my PRs.
> We are releasing Opus 4.7 with safeguards that automatically detect and block requests that indicate prohibited or high-risk cybersecurity uses.
Fucking hell.
Opus was my go-to for reverse engineering and cybersecurity uses, because, unlike OpenAI's ChatGPT, Anthropic's Opus didn't care about being asked to RE things or poke at vulns.
It would, however, shit a brick and block requests every time something remotely medical/biological showed up.
If their new "cybersecurity filter" is anywhere near as bad? Opus is dead for cybersec.
To be fair, delineating between benevolent and malevolent pen-testing and cybersecurity purposes is practically impossible since the only difference is the user's intentions. I am entirely unsurprised (and would expect) that as models improve the amount to which widely available models will be prohibited from cybersecurity purposes will only increase.
Not to say I see this as the right approach, in theory the two forces would balance each other out as both white hats and black hats would have access to the same technology, but I can understand the hesitancy from Anthropic and others.
Yes, and the previous approach Anthropic took was "allow anything that looks remotely benign". The only thing that would get a refusal would be a downright "write an exploit for me". Which is why I favored Anthropic's models.
It remains to be seen whether Anthropic's models are still usable now.
I know just how much of a clusterfuck their "CBRN filter" is, so I'm dreading the worst.
I'm currently testing 4.7 with some reverse engineering stuff/Ghidra scripting and it hasn't refused anything so far, but I'm also doing it on a 20 year old video game, so maybe it doesn't think that's problematic.
Incredible - in one fell swoop killing my entire use case for Claude.
I have about 15 submissions that I now need to work with Codex on cause this "smarter" model refuses to read program guidelines and take them seriously.
> Security professionals who wish to use Opus 4.7 for legitimate cybersecurity purposes (such as vulnerability research, penetration testing, and red-teaming) are invited to join our new Cyber Verification Program.
This seems reasonable to me. The legit security firms won't have a problem doing this, just like other vendors (like Apple, who can give you special iOS builds for security analysis).
If anyone has a better idea on how to _pragmatically_ do this, I'm all ears.
It appears we're learning the hard way that we can't rely on capabilities of models that aren't open weights. These can be taken from us at any time, so expect it to get much worse..
How should one compare benchmark results?
For example, SWE-bench Pro improved ~11% compared with Opus 4.6. Should one interpret it as 4.7 is able to solve more difficult problems? or 11% less hallucinations?
I was researching how to predict hallucinations using the literature (fastowski et al, 2025) (cecere et al, 2025) and the general-ish situation is that there are ways to introspect model certainty levels by probing it from the outside to get the same certainty metric that you _would_ have gotten if the model was trained as a bayesian model, ie, it knows what it knows and it knows what it doesn't know.
This significantly improves claim-level false-positive rates (which is measured with the AUARC metric, ie, abstention rates; ie have the model shut up when it is actually uncertain).
This would be great to include as a metric in benchmarks because right now the benchmark just says "it solves x% of benchmarks", whereas the real question real-world developers care about is "it solves x% of benchmarks *reliably*" AND "It creates false positives on y% of the time".
So the answer to your question, we don't know. It might be a cherry picked result, it might be fewer hallucinations (better metacognition) it might be capability to solve more difficult problems (better intelligence).
Benchmark results don’t directly translate to actual real world improvement. So we might guess it’s somewhat better but hard to say exactly in what way
11% further along the particular bell curve of SWE-bench. Not really easy to extrapolate to real world, especially given that eg the Chinese models tend to heavily train on the benchmarks. But a 10% bump with the same model should equate to “feels noticeably smarter”.
A more quantifiable eval would be METR’s task time - it’s the duration of tasks that the model can complete on average 50% of the time, we’ll have to wait to see where 4.7 lands on this one.
Honestly I've been doing a lot of image-related work recently and the biggest thing here for me is the 3x higher resolution images which can be submitted. This is huge for anyone working with graphs, scientific photographs, etc. The accuracy on a simple automated photograph processing pipeline I recently implemented with Opus 4.6 was about 40% which I was surprised at (simple OCR and recognition of basic features). It'll be interesting to see if 4.7 does much better.
I wonder if general purpose multimodal LLMs are beginning to eat the lunch of specific computer vision models - they are certainly easier to use.
Interesting to see the benchmark numbers, though at this point I find these incremental seeming updates hard to interpret into capability increases for me beyond just "it might be somewhat better".
Maybe I've skimmed too quickly and missed it, but does calling it 4.7 instead of 5 imply that it's the same as 4.6, just trained with further refined data/fine tuned to adapt the 4.6 weights to the new tokenizer etc?
With the new tokenizer did they A/B test this one?
I'm curious if that might be responsible for some of the regressions in the last month. I've been getting feedback requests on almost every session lately, but wasn't sure if that was because of the large amount of negative feedback online.
I get this error too and if I try again: { ... "error":{"type":"permission_error","message":"anthropic.claude-opus-4-7 is not available for this account. You can explore other available models on Amazon Bedrock. For additional access options, contact AWS Sales at https://aws.amazon.com/contact-us/sales-support/"}}
> Opus 4.7 is a direct upgrade to Opus 4.6, but two changes are worth planning for because they affect token usage. First, Opus 4.7 uses an updated tokenizer that improves how the model processes text. The tradeoff is that the same input can map to more tokens—roughly 1.0–1.35× depending on the content type. Second, Opus 4.7 thinks more at higher effort levels, particularly on later turns in agentic settings. This improves its reliability on hard problems, but it does mean it produces more output tokens.
This is concerning & tone-deaf especially given their recent change to move Enterprise customers from $xxx/user/month plans to the $20/mo + incremental usage.
IMO the pursuit of ultraintelligence is going to hurt Anthropic, and a Sonnet 5 release that could hit near-Opus 4.6 level intelligence at a lower cost would be received much more favorably. They were already getting extreme push-back on the CC token counting and billing changes made over the past quarter.
They are planning to release a Mythos-class model (from the initial announcement), but they won't until they can trust their safeguards + the software ecosystem has been sufficiently patched.
It seems they nerf it, then release a new version with previous power. So they can do this forever without actually making another step function model release.
Does it run for you? I can select it this way but it says 'There's an issue with the selected model (claude-opus-4-7). It may not exist or you may not have access to it. Run /model to pick a different model.'
API Error: 400 {"type":"error","error":{"type":"invalid_request_error","message":"\"thinking.type.enabled\" is not supported for this model. Use \"thinking.type.adaptive\" and \"output_config.effort\" to control
thinking behavior."},"request_id":"req_011Ca7enRv4CPAEqrigcRNvd"}
Eep. AFAIK the issues most people have been complaining about with Opus 4.6 recently is due to adaptive thinking. Looks like that is not only sticking around but mandatory for this newer model.
edit: I still can't get it to work. Opus 4.6 can't even figure out what is wrong with my config. Speaking of which, claude configuration is so confusing there are .claude/ (in project) setting.json + a settings.local.json file, then a global ~/.claude/ dir with the same configuration files. None of them have anything defined for adaptive thinking or thinking type enable. None of these strings exist on my machine. Running latest version, 2.1.110
Backlash on HN for Anthropic adjusting usage limits is insane. There's almost no discussion about the model, just people complaining about their subscription.
Excited to use 1 prompt and have my whole 5-hour window at 100%. They can keep releasing new ones but if they don't solve their whole token shrinkage and gaslighting it is not gonna be interesting to se.
It seems a lot of the problem isn't "token shrinkage" (reducing plan limits), but rather changes they made to prompt caching - things that used to be cached for 1 hour now only being cached for 5 min.
Coding agents rely on prompt caching to avoid burning through tokens - they go to lengths to try to keep context/prompt prefixes constant (arranging non-changing stuff like tool definitions and file content first, variable stuff like new instructions following that) so that prompt caching gets used.
This change to a new tokenizer that generates up to 35% more tokens for the same text input is wild - going to really increase token usage for large text inputs like code.
on Tuesday, with 4.6, I waited for my 5 hour window to reset, asked it to resume, and it burned up all my tokens for the next 5 hour window and ran for less than 10 seconds. I’ve never cancelled a subscription so fast.
I tried the Claude Extension for VSCode on WSL for a reverse engineering task, it consumed all of my tokens, broke and didn't even save the conversatioon
This is the first new model from Anthropic in a while that I'm not super enthused about. Not because of the model, I literally haven't opened the page about it, I can already guess what it says ("Bigger, better, faster, stronger"), but because of the company.
I have enjoyed using Claude Code quite a bit in the past but that has been waning as of late and the constant reports of nerfed models coupled with Anthropic not being forthcoming about what usage is allowed on subscriptions [0] really leaves a bad taste in my mouth. I'll probably give them another month but I'm going to start looking into alternatives, even PayG alternatives.
[0] Please don't @ me, I've read every comment about how it _is clear_ as a response to other similar comments I've made. Every. Single. One. of those comments is wrong or completely misses the point. To head those off let me be clear:
Anthropic does not at all make clear what types of `claude -p` or AgentSDK usage is allowed to be used with your subscription. That's all I care about. What am I allowed to use on my subscription. The docs are confusing, their public-facing people give contradictory information, and people commenting state, with complete confidence, completely wrong things.
I greatly dislike the Chilling Effect I feel when using something I'm paying quite a bit (for me) of money for. I don't like the constant state of unease and being unsure if something might be crossing the line. There are ideas/side-projects I'm interested in pursuing but don't because I don't want my account banned for crossing a line I didn't know existed. Especially since there appears to be zero recourse if that happens.
I want to be crystal clear: I am not saying the subscription should be a free-for-all, "do whatever you want", I want clear lines drawn. I increasingly feeling like I'm not going to get this and so while historically I've prefered Claude over ChatGPT, I'm considering going to Codex (or more likely, OpenCode) due to fewer restrictions and clearer rules on what's is and is not allowed. I'd also be ok with kind of warning so that it's not all or nothing. I greatly appreciate what Anthropic did (finally) w.r.t. OpenClaw (which I don't use) and the balance they struck there. I just wish they'd take that further.
Regardless of the model quality improvement, the corporate damage was done by not only ignoring the Opus quality degradation but gaslighting users into thinking they aren’t using it right.
I switched to Codex 5.4 xhigh fast and found it to be as good as the old Claude. So I’ll keep using that as my daily driver and only assess 4.7 on my personal projects when I have time.
It’s funny, a few months ago I would have been pretty excited about this. But I honestly don’t really care because I can’t trust Anthropic to not play games with this over the next month post release.
I just flat out don’t trust them. They’ve shown more than enough that they change things without telling users.
Might be sticking with 4.6 it's only been 20 minutes of using 4.7 and there are annoyances I didn't face with 4.6 what the heck. Huge downgrade on MRCR too....
Here’s the problem. The distribution of query difficulty / task complexity is probably heavily right-skewed which drives up the average cost dramatically. The logical thing for anthropic to do, in order to keep costs under control, is to throttle high-cost queries. Claude can only approximate the true token cost of a given query prior to execution. That means anything near the top percentile will need to get throttled as well.
By definition this means that you’re going to get subpar results for difficult queries. Anything too complicated will get a lightweight model response to save on capacity. Or an outright refusal which is also becoming more common.
New models are meaningless in this context because by definition the most impressive examples from the marketing material will not be consistently reproducible by users. The more users who try to get these fantastically complex outputs the more those outputs get throttled.
Gemini and Codex already scored higher on benchmarks than Opus 4.6 and they recently added a $100 tier with limited 2x limits, that's their answer and it seems people have caught on.
just started using codex. claude is just marketing machine and benchmaxxing and only if you pay gazillion and show your ID you can use their dangerous model.
even sonnet right now has degraded for me to the point of like ChatGPT 3.5 back then. took ~5 hours on getting a playwright e2e test fixed that waited on a wrong css selector. literlly, dumb as fuck. and it had been better than opus for the last week or so still... did roughly comparable work for the last 2 weeks and it all went increasingly worse - taking more and more thinking tokens circling around nonsense and just not doing 1 line changes that a junior dev would see on the spot. Too used to vibing now to do it by hand (yeah i know) so I kept watching and meanwhile discovered that codex just fleshed out a nontrivial app with correct financial data flows in the same time without any fuzz. I really don't get why antrhopic is dropping their edge so hard now recently, in my head they might aim for increasing hype leading to the IPO, not disappointment crashes from their power user base.
not rejecting reality, but increasing doubts about the effectiveness of these tests. and yes its subjective n=1, but I literally create and ship projects for many months now always from the same github template repository forked and essentially do the same steps with a few differnt brand touches and nearly muscle memory prompting to do the just right next steps mechanically over and over again, and the amount of things getting done per step gots worse and the quality degraded too, forgetting basic things along the way a few prompts in. as I said n=1 but the very repetitive nature of my current work days alwyas doing a new thing from the exact same start point that hasn't changed in half a year is kind of my personal benchmark. YMMV but on my end the effects are real, specifically when tracking hours over this stuff.
Reminder that 4.7 may seem like a huge upgrade to 4.6 because they nerfed the F out of 4.6 ahead of this launch so 4.7 would seem like a remarkable improvement...
Sigh here we go again, model release day is always the worst day of the quarter for me. I always get a lovely anxiety attack and have to avoid all parts of the internet for a few days :/
I feel this way too. Wish I could fully understand the 'why'. I know all of the usual arguments, but nothing seems to fully capture it for me - maybe it' all of them, maybe it's simply the pace of change and having to adapt quicker than we're comfortable with. Anyway best of luck from someone who understands this sentiment.
Really? I think it's pretty straightforward, at least for me - fear of AI replacing my profession and also fear that it will become harder to succeed with a side project.
Yeah I can understand that, and sure this is part of it, just not all of it. There is also broader societal issues (ie. inequality), personal questions around meaning and purpose, and a sprinkling of existential (but not much). I suspect anyone surveyed would have a different formula for what causes this unease - I struggle to define it (yet think about it constantly), hence my comment above.
Ultimately when I think deeper, none of this would worry me if these changes occurred over 20 years - societies and cultures change and are constantly in flux, and that includes jobs and what people value. It's the rate of change and inability to adapt quick enough which overwhelms me.
Not worried about inequality, at least not in the sense that AI would increase it, I'm expecting the opposite. Being intelligent will become less valuable than today, which will make the world more equal, but it may be not be a net positive change for everybody.
Regarding meaning and purpose, I have some worries here too, but can easily imagine a ton of things to do and enjoy in a post-AGI world. Travelling, watching technological progress, playing amazing games.
Maybe the unidentified cause of unease is simply the expectation that the world is going to change and we don't know how and have no control over it. It will just happen and we can only hope that the changes will be positive.
See i don't have any of this fear, I have 0 concerns that LLMs will replace software engineering because the bulk of the work we do (not code) is not at risk.
New model - that explains why for the past week/two weeks I had this feeling of 4.6 being much less "intelligent". I hope this is only some kind of paranoia and we (and investors) are not being played by the big corp. /s
Ok, so the answer is "they make the existing model worse to make it seem that the new model is good". I'm almost certain that this is not what's going on. It's hard to make the argument that the benefits outweigh the drawbacks of such approach. It doesn't give the more market share or revenue.
It seems like we're hitting a solid plateau of LLM performance with only slight changes each generation. The jumps between versions are getting smaller. When will the AI bubble pop?
SWE-bench pro is ~20% higher than the previous .1 generation which was released 2 months ago. For their SWE benchmark, the token consumption iso-performance is down 2x from the model they released 2 months ago.
If this is a plateau I struggle to imagine what you consider fast progress.
I'm finding the "adaptive thinking" thing very confusing, especially having written code against the previous thinking budget / thinking effort / etc modes: https://platform.claude.com/docs/en/build-with-claude/adapti...
Also notable: 4.7 now defaults to NOT including a human-readable reasoning token summary in the output, you have to add "display": "summarized" to get that: https://platform.claude.com/docs/en/build-with-claude/adapti...
(Still trying to get a decent pelican out of this one but the new thinking stuff is tripping me up.)
> Still trying to get a decent pelican out of this one but the new thinking stuff is tripping me up
Wouldn't that be p-hacking where p stands for pelican?
The p stands for putrification.
yeah they took "i pick the budget" and turned it into "trust us".
I keep saying even if there's not current malfeasance, the incentives being set up where the model ultimately determines the token use which determines the model provider's revenue will absolutely overcome any safeguards or good intentions given long enough.
Does this mean Claude no longer outputs the full raw reasoning, only summaries? At one point, exposing the LLM's full CoT was considered a core safety tenet.
I don't think it ever has. For a very long time now, the reasoning of Claude has been summarized by Haiku. You can tell because a lot of the times it fails, saying, "I don't see any thought needing to be summarised."
Maybe there was no thinking.
Anthropic always summarizes the reasoning output to prevent some distillation attacks
"Also notable: 4.7 now defaults to NOT including a human-readable reasoning token summary in the output, you have to add "display": "summarized" to get that"
I did not follow all of this, but wasn't there something about, that those reasoning tokens did not represent internal reasoning, but rather a rough approximation that can be rather misleading, what the model actual does?
The reasoning is the secret sauce. They don't output that. But to let you have some feedback about what is going on, they pass this reasoning through another model that generates a human friendly summary (that actively destroys the signal, which could be copied by competition).
Don't or can't.
My assumption is the model no longer actually thinks in tokens, but in internal tensors. This is advantageous because it doesn't have to collapse the decision and can simultaneously propogate many concepts per context position.
If that's true, then we're following the timeline of https://ai-2027.com/
Care to expound on that? Maybe a reference to the relevant section?
Ctrl-F "neuralese" on that page.
You should just read the thing, whether or not you believe it, to have an informed opinion on the ongoing debate.
That's not supposed to happen til 2027. Ruh roh.
Most likely, would be cool yes see a open source Nivel use diffusion for thinking.
'Hey Claude, these tokens are utter unrelated bollocks, but obviously we still want to charge the user for them regardless. Please construct a plausible explanation as to why we should still be able to do that.'
Don't look at "thinking" tokens. LLMs sometimes produce thinking tokens that are only vaguely related to the task if at all, then do the correct thing anyways.
They also sometimes flag stuff in their reasoning and then think themselves out of mentioning it in the response, when it would actually have been a very welcome flag.
Yea I’ve seen this and stopped it and asked it about it.
Sometimes they notice bugs or issues and just completely ignore it.
This can result in some funny interactions. I don't know if Claude will say anything, but I've had some models act "surprised" when I commented on something in their thinking, or even deny saying anything about it until I insisted that I can see their reasoning output.
Supposedly (https://www.reddit.com/r/ClaudeAI/comments/1seune4/claude_ch...) they can't even see their own reasoning afterwards.
It's likely hiding the model downgrade path they require to meet sustainable revenue. Should be interesting if they can enshittify slowly enough to avoid the ablative loss of customers! Good luck all VCs!
They have super sustainable revenue. They are deadly supply constrained on compute, and have a really difficult balancing act over the next year or two in which they have to trade off spending that limited compute on model training so that they can stay ahead, while leaving enough of it available for customers that they can keep growing number of customers.
> Opus 4.7 uses an updated tokenizer that improves how the model processes text. The tradeoff is that the same input can map to more tokens—roughly 1.0–1.35× depending on the content type.
caveman[0] is becoming more relevant by the day. I already enjoy reading its output more than vanilla so suits me well.
[0] https://github.com/JuliusBrussee/caveman/tree/main
I hope people realize that tools like caveman are mostly joke/prank projects - almost the entirety of the context spent is in file reads (for input) and reasoning (in output), you will barely save even 1% with such a tool, and might actually confuse the model more or have it reason for more tokens because it'll have to formulate its respone in the way that satisfies the requirements.
> I hope people realize that tools like caveman are mostly joke/prank projects
This seems to be a common thread in the LLM ecosystem; someone starts a project for shits and giggles, makes it public, most people get the joke, others think it's serious, author eventually tries to turn the joke project into a VC-funded business, some people are standing watching with the jaws open, the world moves on.
I was convinced https://github.com/memvid/memvid was a joke until it turned out it wasn't.
To be fair, most of us looked at GPT1 and GPT2 as fun and unserious jokes, until it started putting together sentences that actually read like real text, I remember laughing with a group of friends about some early generated texts. Little did we know.
Are there any public records I can see from GPT1 and GPT2 output and how it was marketed?
HN submissions have a bunch of examples in them, but worth remembering they were released as "Look at this somewhat cool and potentially useful stuff" rather than what we see today, LLMs marketed as tools.
https://news.ycombinator.com/item?id=21454273 / https://news.ycombinator.com/item?id=19830042 - OpenAI Releases Largest GPT-2 Text Generation Model
HN search for GPT between 2018-2020, lots of results, lots of discussions: https://hn.algolia.com/?dateEnd=1577836800&dateRange=custom&...
I was first made aware of GPT2 from reading Gwern -- "huh, that sounds interesting" -- but really didn't start really reading model output until I saw this subreddit:
https://www.reddit.com/r/SubSimulatorGPT2/
There is a companion Reddit, where real people discuss what the bots are posting:
https://www.reddit.com/r/SubSimulatorGPT2Meta/
You can dig around at some of the older posts in there.
I don't think it was marketed as such, they were research projects. GPT-3 was the first to be sold via API
From a 2019 news article:
> New AI fake text generator may be too dangerous to release, say creators
> The Elon Musk-backed nonprofit company OpenAI declines to release research publicly for fear of misuse.
> OpenAI, an nonprofit research company backed by Elon Musk, Reid Hoffman, Sam Altman, and others, says its new AI model, called GPT2 is so good and the risk of malicious use so high that it is breaking from its normal practice of releasing the full research to the public in order to allow more time to discuss the ramifications of the technological breakthrough.
https://www.theguardian.com/technology/2019/feb/14/elon-musk...
Aka 'We cared about misuse right up until it became apparent that was profit to be had'
OpenAI sure speed ran the Google and Facebook 'Don't be evil' -> 'Optimize money' transition.
You can run GPT2! Here's the medium model: https://huggingface.co/openai-community/gpt2-medium
I will now have it continue this comment:
I've been running gps for a long time, and I always liked that there was something in my pocket (and not just me). One day when driving to work on the highway with no GPS app installed, I noticed one of the drivers had gone out after 5 hours without looking. He never came back! What's up with this? So i thought it would be cool if a community can create an open source GPT2 application which will allow you not only to get around using your smartphone but also track how long you've been driving and use that data in the future for improving yourself...and I think everyone is pretty interested.
[Updated on July 20] I'll have this running from here, along with a few other features such as: - an update of my Google Maps app to take advantage it's GPS capabilities (it does not yet support driving directions) - GPT2 integration into your favorite web browser so you can access data straight from the dashboard without leaving any site! Here is what I got working.
[Updated on July 20]
And now gpt is laughing,while it replaces coders lol
Why? Doesn't have jokey copy. Any thoughts on claude-mem[0] + context-mode[1]?
[0] https://github.com/thedotmack/claude-mem
[1] https://github.com/mksglu/context-mode
The big idea with Memvid was to store embedding vector data as frames in a video file. That didn't seem like a serious idea to me.
Very cool idea. Been playing with a similar concept: break down one image into smaller self-similar images, order them by data similarity, use them as frames for a video
You can then reconstruct the original image by doing the reverse, extracting frames from the video, then piecing them together to create the original bigger picture
Results seem to really depend on the data. Sometimes the video version is smaller than the big picture. Sometimes it’s the other way around. So you can technically compress some videos by extracting frames, composing a big picture with them and just compressing with jpeg
> embedding vector data as frames in a video file
Interesting, when I heard about it, I read the readme, and I didn't take that as literal. I assumed it was meant as we used video frames as inspiration.
I've never used it or looked deeper than that. My LLM memory "project" is essentially a `dict<"about", list<"memory">>` The key and memories are all embeddings, so vector searchable. I'm sure its naive and dumb, but it works for my tiny agents I write.
Just read through the readme and I was fairly sure this was a well-written satire through "Smart Frames".
Honestly part of me still thinks this is a satire project but who knows.
Is this... just one file acting as memory?
A major reason for that is because there's no way to objectively evaluate the performance of LLMs. So the meme projects are equally as valid as the serious ones, since the merits of both are based entirely on anecdata.
It also doesn't help that projects and practices are promoted and adopted based on influencer clout. Karpathy's takes will drown out ones from "lesser" personas, whether they have any value or not.
While the caveman stuff is obviously not serious, there is a lot of legit research in this area.
Which means yes, you can actually influence this quite a bit. Read the paper “Compressed Chain of Thought” for example, it shows it’s really easy to make significant reductions in reasoning tokens without affecting output quality.
There is not too much research into this (about 5 papers in total), but with that it’s possible to reduce output tokens by about 60%. Given that output is an incredibly significant part of the total costs, this is important.
https://arxiv.org/abs/2412.13171
Who would suspect that the companies selling 'tokens' would (unintentionally) train their models to prefer longer answers, reaping a HIGHER ROI (the thing a publicly traded company is legally required to pursue: good thing these are all still private...)... because it's not like private companies want to make money...
Some labs do it internally because RLVR is very token-expensive. But it degrades CoT readability even more than normal RL pressure does.
It isn't free either - by default, models learn to offload some of their internal computation into the "filler" tokens. So reducing raw token count always cuts into reasoning capacity somewhat. Getting closer to "compute optimal" while reducing token use isn't an easy task.
Yeah the readability suffers, but as long as the actual output (ie the non-CoT part) stays unaffected it’s reasonably fine.
I work on a few agentic open source tools and the interesting thing is that once I implemented these things, the overall feedback was a performance improvement rather than performance reduction, as the LLM would spend much less time on generating tokens.
I didn’t implement it fully, just a few basic things like “reduce prose while thinking, don’t repeat your thoughts” etc would already yield massive improvements.
Yeah you could easily imagine stenography like inputs and outputs for rapid iteration loops. It's also true that in social media people already want faster-to-read snippets that drop grammar so the desire for density is already there for human authors/readers.
All LLMs also effectively work by ”larping” a role. You steer it towards larping a caveman and well.. let’s just say they weren’t known for their high iq
Fun fact: Neanderthals actually had larger brains than Homo Sapiens! Modern humans are thought to have outcompeted them by working better together in larger groups, but in terms of actual individual intelligence, Neanderthals may have had us beat. Similarly, humans have been undergoing a process of self-domestication over the last couple millenia that have resulted in physiological changes that include a smaller brain size - again, our advantage over our wilder forebearers remains that we're better in larger social groups than they were and are better at shared symbolic reasoning and synchronized activity, not necessarily that our brains are more capable.
(No, none of this changes that if you make an LLM larp a caveman it's gonna act stupid, you're right about that.)
I thought we were way past the "bigger brain means more intelligence" stage of neuroscience?
You can't draw conclusions on individuals, but at a species level bigger brain, especially compared to body size, strongly correlates with intelligence
Bigger brain does not automatically mean more intelligence, but we have reasons to suspect that homo neanderthalensis may have been more intelligent than contemporary homo sapiens other than bigger brains.
All data shows there's a moderate correlation.
Even neuronal density is simplistic, and the dimension of size alone doesn't consider that.
Modern humans were also cavemen.
This is why ancient Chinese scholar mode (also extremely terse) is better.
This specific form may be a joke, but token conscious work is becoming more and more relevant.. Look at https://github.com/AgusRdz/chop
And
https://github.com/toon-format/toon
Also https://github.com/rtk-ai/rtk but some people see that changing how commands output stuff can confuse some models
Exactly. The model is exquisitely sensitive to language. The idea that you would encourage it to think like a caveman to save a few tokens is hilarious but extremely counter-productive if you care about the quality of its reasoning.
We started out with oobabooga, so caveman is the next logical evolution on the road to AGI.
Output tokens are more expensive
Help me understand: I get that the file reading can be a lot. But I also expand the box to see its “reasoning” and there’s a ton of natural language going on there.
They are indeed impractical in agentic coding.
However in deep research-like products you can have a pass with LLM to compress web page text into caveman speak, thus hugely compressing tokens.
I don't understand how this would work without a huge loss in resolution or "cognitive" ability.
Prediction works based on the attention mechanism, and current humans don't speak like cavemen - so how could you expect a useful token chain from data that isn't trained on speech like that?
I get the concept of transformers, but this isn't doing a 1:1 transform from english to french or whatever, you're fundamentally unable to represent certain concepts effectively in caveman etc... or am I missing something?
Good catch actually.
Okay maybe not exactly caveman dialect, but text compression using LLM is definitely possible to save on tokens in deep research.
I wonder if you can have it reason in caveman
would you be surprised if this is what happens when you ask it to write like one?
folks could have just asked for _austere reasoning notes_ instead of "write like you suffer from arrested development"
> "write like you suffer from arrested development"
My first thought was that this would mean that my life is being narrated by Ron Howard.
I mean we had a shoe company pivot to AI and raise their stock value by 300%, how can we even know anymore
You really think the 33k people that starred a 40 line markdown file realize that?
You mean the 33k bots that created a nearly linear stars/day graph? There's a dip in the middle, but it was very blatant at the start (and now)
Stars are more akin to bookmarks and likes these days, as opposed to a show of support or "I use this"
I use them like bookmarks.
I intentionally throw some weird ones on there just in case anyone is actually ever checking them. Gotta keep interviewers guessing.
I use them as likes
The amount of cargo culting amongst AI halfwits (who seem to have a lot of overlap with influencers and crypto bros) is INSANE
I mean just look at the growth of all these "skills" that just reiterate knowledge the models already have
Caveman is fun, but the real tool you want to reduce token usage is headroom
https://github.com/gglucass/headroom-desktop (mac app)
https://github.com/chopratejas/headroom (cli)
Headroom looks great for client-side trimming. If you want to tackle this at the infrastructure level, we built Edgee (https://www.edgee.ai) as an AI Gateway that handles context compression, caching, and token budgeting across requests, so you're not relying on each client to do the right thing.
(I work at Edgee, so biased, but happy to answer questions.)
I was doing some experiments with removing top 100-1000 most common English words from my prompts. My hypothesis was that common words are effectively noise to agents. Based on the first few trials I attempted, there was no discernible difference in output. Would love to compare results with caveman.
Caveat: I didn’t do enough testing to find the edge cases (eg, negation).
Yeah, when I'm writing code I try to avoid zeros and ones, since those are the most common bits, making them essentially noise
I literally just posted a blog on this. Some seemingly insignificant words are actually highly structural to the model. https://www.ruairidh.dev/blog/compressing-prompts-with-an-au...
I suspect even typos have an impact on how the model functions.
I wonder if there’s a pre-processor that runs to remove typos before processing. If not, that feels like a space that could be worked on more thoroughly.
I guess just a spell-check in the repo? But yes, I'd imagine that they have an effect. Even running the same input twice is non-deterministic.
The ability for audio processing to figure out spelling from context, especially with regards to acronyms that are pronounced as words, leads me to believe there’s potential for a more intelligent spell check preprocess using a cheaper model.
there is no pre-processor, i've had typos go through, with claude asking to make sure i meant one thing instead of the other
I strongly suspected that there was some pre/postprocessing going on when trying to get it to output rot13("uryyb, jbyeq"), but it's probably just due to massively biased token probabilities. Still, it creates some hilarious output, even when you clearly point out the error:
Doesn't it just use more tokens in reasoning?
caveman stops being a style tool and starts being self-defense. once prompt comes in up to 1.35x fatter, they've basically moved visibility and control entirely into their black box.
I find grep and common cli command spam to be the primary issue. I enjoy Rust Token Killer https://github.com/rtk-ai/rtk, and agents know how to get around it when it truncates too hard.
Caveman hurt model performance. If you need a dumber model with less token output, just use sonnet-4-6 or other non-reasoning model.
Oh wow, I love this idea even if it's relatively insignificant in savings.
I am finding my writing prompt style is naturally getting lazier, shorter, and more caveman just like this too. If I was honest, it has made writing emails harder.
While messing around, I did a concept of this with HTML to preserve tokens, worked surprisingly well but was only an experiment. Something like:
> <h1 class="bg-red-500 text-green-300"><span>Hello</span></h1>
AI compressed to:
> h1 c bgrd5 tg3 sp hello sp h1
Or something like that.
Combine that with emmet / zen coding: https://en.wikipedia.org/wiki/Emmet_%28software%29?wprov=sfl...
You'd like Emmet notation. Just look at the cheat sheet: https://docs.emmet.io/cheat-sheet/
I really enjoy the party game "Neanderthal Poetry", in which you can only speak using monosyllabic words. I bet you would too.
1.35 times! For Input! For what kinds of tokens precisely? Programming? Unicode? If they seriously increased token usage by 35% for typical tasks this is gonna be rough.
I used Opus 4.7 for about 15 minutes on the auto effort setting.
It nicely implemented two smallish features, and already consumed 100% of my session limit on the $20 plan.
See you again in five hours.
me feel that it needs some tweaking - it's a little annoyingly cute (and could be even terser).
Another supply chain attack waiting?
Have you tried just adding an instruction to be terse?
Don't get me wrong, I've tried out caveman as well, but these days I am wondering whether something as popular will be hijacked.
People are really trigger-happy when it comes to throwing magic tools on top of AI that claim to "fix" the weak parts (often placeboing themselves because anthropic just fixed some issue on their end).
Then the next month 90% of this can be replaced with new batch of supply chain attack-friendly gimmicks
Especially Reddit seems to be full of such coding voodoo
My favorite to chuckle at are the prompt hack voodoo stuff, like, “tell it to be correct” or “say please” or “tell it someone will die if it doesnt do a good job,” often presented very seriously and with some fast cutting animations in a 30 second reel
> coding voodoo
Well, we've sacrificed the precision of actual programming languages for the ease of English prose interpreted by a non-deterministic black box that we can't reliably measure the outputs of. It's only natural that people are trying to determine the magical incantations required to get correct, consistent results.
Too late, personally after how bad 4.6 was the past week I was pushed to codex, which seems to mostly work at the same level from day to day. Just last night I was trying to get 4.6 to lookup how to do some simple tensor parallel work, and the agent used 0 web fetches and just hallucinated 17K very wrong tokens. Then the main agent decided to pretend to implement tp, and just copied the entire model to each node...
Same. I stopped my Pro subscription yesterday after entering the week with 70% of my tokens used by Monday morning (on light, small weekend projects, things I had worked on in the past and barely noticed a dent in usage.) Support was... unhelpful.
It's been funny watching my own attitude to Anthropic change, from being an enthusiastic Claude user to pure frustration. But even that wasn't the trigger to leave, it was the attitude Support showed. I figure, if you mess up as badly as Anthropic has, you should at least show some effort towards your customers. Instead I just got a mass of standardised replies, even after the thread replied I'd be escalated to a human. Nothing can sour you on a company more. I'm forgiving to bugs, we've all been there, but really annoyed by indifference and unhelpful form replies with corporate uselessness.
So if 4.7 is here? I'd prefer they forget models and revert the harness to its January state. Even then, I've already moved to Codex as of a few days ago, and I won't be maintaining two subscriptions, it's a move. It has its own issues, it's clear, but I'm getting work done. That's more than I can say for Claude.
> It's been funny watching my own attitude to Anthropic change, from being an enthusiastic Claude user to pure frustration.
You were enthusiastic because it was a great product at an unsustainable price.
Its clear that Claude is now harnessing their model because giving access to their full model is too expensive for the $20/m that consumers have settled on as the price point they want to pay.
I wrote a more in depth analysis here, there's probably too much to meaningfully summarize in a comment: https://sustainableviews.substack.com/p/the-era-of-models-is...
I used the $60/mo subscription and I bet most developers get access to AI agents via their company, and there was no difference. They should have reduced the rate limits, or offered a new model, anything except silently reduce the quality of their flagship product to reduce cost.
The cost of switching is too low for them to be able to get away with the standard enshittification playbook. It takes all of 5 minutes to get a Codex subscription and it works almost exactly the same, down to using the same commands for most actions.
It seems like the big companies they're providing Mythos to are their only concern right now.
I havent been using my claude sub lately but I liked 4.6 three weeks ago. Did something change?
Funny because many people here were so confident that OpenAI is going to collapse because of how much compute they pre-ordered.
But now it seems like it's a major strategic advantage. They're 2x'ing usage limits on Codex plans to steal CC customers and it seems to be working. I'm seeing a lot of goodwill for Codex and a ton of bad PR for CC.
It seems like 90% of Claude's recent problems are strictly lack of compute related.
> people here were so confident that OpenAI is going to collapse because of how much compute they pre-ordered
That's not why. It was and is because they've been incredibly unfocused and have burnt through cash on ill-advised, expensive things like Sora. By comparison Anthropic have been very focused.
I don't think that was the main reason for people thinking OpenAI is going to collapse here.
By far, the biggest argument was that OpenAI bet too much on compute.
Being unfocused is generally an easy fix. Just cut things that don't matter as much, which they seem to be doing.
Nobody was talking about them betting too much on compute, people were saying that their shady deals on compute with NVIDIA and Oracle were creating a giant bubble in their attempt to get a Too Big To Fail judgement (in their words- taxpayer-backed "backstop").
It really wasn't. Most of the argument was around product portfolio and agentic coding performance.
To me it seems like they burn so much money they can do lots of things in parallel. My guess would be that e.g. codex and sora are very independently developed. After all there's a quite a hard limit on how many bodies are beneficial to a software project.
They all compete internally over constrained compute resources - for R&D and production.
> By comparison Anthropic have been very focused.
Ah yes, very focused on crapping out every possible thing they can copy and half bake?
Personally its down to Altman having the cognitive capacity of a sleeping snail, the world insight of a hormonal 14 year old who's only ever read one series of manga.
Despite having literal experts at his fingertips, he still isn't able to grasp that he's talking unfilters bollocks most of the time. Not to mention is Jason level of "oath breaking"/dishonesty.
Seems very short term. Like how cheap Uber was initially. Like Claude was before!
Eventually OpenAI will need to stop burning money.
In hindsight, it is painfully clear that Antropic’s conservative investment strategy has them struggling with keeping up with demand and caused their profit margin to shrink significantly as last buyer of compute.
they've also introduced a lot of caching and token burn related bugs which makes things worse. any bug that multiplies the token burn also multiplies their infrastructure problems.
That’s more a leadership decision because Anthropic are nerfing the model to cut costs, if they stop doing that then they’ll stay ahead.
Proof they are nerfing the model? It is stable in benchmarks: https://marginlab.ai/trackers/claude-code-historical-perform...
All this just reads like just another case of mass psychosis to me
Is that 2x still going on I thought that ended in early April
Different plan. The old 2x has been discontinued, and the bonus is now (temporarily) available for the new $100 plan users in an effort, presumably, to entice them away from Anthropic.
For the $200 users, it never ended.
It’s for Pro users only, I think the 2x is up to May 31.
They did it again to "celebrate" the release of the $100 plan.
On plus?
Their top tier plan got a 3x limit boost. This has been the first week ever where I haven't run out of tokens.
No
> It seems like 90% of Claude's recent problems are strictly lack of compute related.
Downtime is annoying, but the problem is that over the past 2-3 weeks Claude has been outrageously stupid when it does work. I have always been skeptical of everything produced - but now I have no faith whatsoever in anything that it produces. I'm not even sure if I will experiment with 4.7, unless there are glowing reviews.
Codex has had none of these problems. I still don't trust anything it produces, but it's not like everything it produces is completely and utterly useless.
So many people confuse sycophantic behavior with producing results.
The market here is extraordinarily vibes-based and burning billions of dollars for a ephemeral PR boost, which might only last another couple weeks until people find a reason to hate Codex, does not reflect well on OAI's long term viability.
I have both Claude and OpenAI, side by side. I would say sonnet 46 still beats gpt 54 for coding (at least in my use case) But after about 45 minutes I'm out of my window, so I use openai for the next 4 hours and I can't even reach my limit.
Most of the compute OpenAI "preordered" is vapour. And it has nothing to do with why people thought the company -- which is still in extremely rocky rapids -- was headed to bankruptcy.
Anthropic has been very disciplined and focused (overwhelmingly on coding, fwiw), while OpenAI has been bleeding money trying to be the everything AI company with no real specialty as everyone else beat them in random domains. If I had to qualify OpenAI's primary focus, it has been glazing users and making a generation of malignant narcissists.
But yes, Anthropic has been growing by leaps and bounds and has capacity issues. That's a very healthy position to be in, despite the fact that it yields the inevitable foot-stomping "I'm moving to competitor!" posts constantly.
All of the smart people I know went to work at OpenAI and none at Anthropic. In addition to financial capital, OpenAI has a massive advantage in human capital over Anthropic.
As long as OpenAI can sustain compute and paying SWE $1million/year they will end up with the better product.
Attracting talent with huge sums of money just gets you people who optimize for money, and it's usually never a good long-term decision. I think it's what led to Google's downturn.
> OpenAI has a massive advantage in human capital over Anthropic.
but if your leader is a dipshit, then its a waste.
Look You can't just throw money at the problem, you need people who are able to make the right decisions are the right time. That that requires leadership. Part of the reason why facebook fucked up VR/AR is that they have a leader who only cares about features/metrics, not user experience.
Part of the reason why twitter always lost money is because they had loads of teams all running in different directions, because Dorsey is utterly incapable of making a firm decision.
Its not money and talent, its execution.
Codex really has its place in my bag. I mainly use it, rarely Claude.
Codex just gets it done. Very self-correcting by design while Claude has no real base line quality for me. Claude was awesome in December, but Codex is like a corporate company to me. Maybe it looks uncool, but can execute very well.
Also Web Design looks really smooth with Codex.
OpenAI really impressed me and continues to impress me with Codex. OpenAI made no fuzz about it, instead let results speak. It is as if Codex has no marketing department, just its product quality - kind of like Google in its early days with every product.
My tinfoil hat theory, which may not be that crazy, is that providers are sandbagging their models in the days leading up to a new release, so that the next model "feels" like a bigger improvement than it is.
An important aspect of AI is that it needs to be seen as moving forward all the time. Plateaus are the death of the hype cycle, and would tether people's expectations closer to reality.
Possibly due to moving compute from inference to training
I guess our conscience of OpenAI working with the Department of War has an expiry date of 6 weeks.
That number is generous, and is also a pretty decent lifespan for a socially-conscious gesture in 2026.
We all liked the Terminator movies. Hopefully the stay as movies.
Thing is that Anthropic was always working with DoD, too, and the line in the sand they drew looked really noble until I found it didn't not apply to me, a non-US citizen. Dario made it clear that was the case.
And so the difference, to me, was irrelevant. I'll buy based on value, and keep a poker in the fire of Chinese & European open weight models, as well.
Not everyone is American, and people who are not see Anthropic state they are willing to spy on our countries and shrug about OAI saying the same about America. What’s the difference to us?
if you're not american you should be worried about the bit of using AI to kill people which was the other major objection by Anthropic.
(not that I think the US DoD wouldn't do that anyway, ToS or not.)
well, if they put in a fully automated kill chain, its gonna be weak to attacks to make yourself look like a car, or a video game styled "hide under a box"
the current non-automated kill chain has targeted fishermen and a girl's school. Nobody is gonna be held accountable for either.
Am i worried about the killing or the AI? If i'm worried about the killing, id much rather push for US demilitarization.
OK, I am worried.
Now, what can I actually do?
Vote with your dollar. Ask others to do the same and explain why. If we all did this, it might matter. There’s not a lot else an individual can do.
Dario in fact said it was ok to spy and drone non-US citizens, and in fact endorsed American foreign policy generally.
So, no, I'm not voting with my wallet for one American country versus the other. I'll pick the best compromise product for me, and then also boost non-American R&D where I can.
Vote with your wallet, just like Americans.
Not only is Anthropic perfectly happy to let the DoD use their products to kill people, but they are partners with Palantir and were apparently instrumental in the strikes against Iran by the US military.
https://www.washingtonpost.com/technology/2026/03/04/anthrop...
So uh, yeah, the only difference I see between OAI and Anthropic is that one is more honest about what they’re willing to use their AI for.
neah, I believe most people here, which immediately brag about codex, are openai employees doing part of their job. otherwise I couldn't possibly phantom why would anyone use codex. In my company 80% is claude and 15% gemini. you can barely see openai on the graph. and we have >5k programmers using ai every day.
Currently GPT just works much better, and so does Gemini but it's more expensive right now. Going through Opencode stats, their claim is that Gemini is the current best model followed by GPT 5.4 on their benchmarks, but the difference is slim.
My personal experience is best with GPT but it could be the specific kind of work I use it for which is heavy on maths and cpp (and some LISP).
You can believe whatever you want. I found claude unusable due to limits. Codex works very well for my use cases.
OpenAI replaced its founding engineers with Meta PMs. The shift towards consumer engagement metrics and marketing is apparent.
I’m thinking the same thing, Codex literally ruined the codebases that I experimented with it on.
Longer than how long anyone cared about epstein.
Most people just want to use a tool that works. Not everything has to be a damn moral crusade.
Yes, let take morality out of our daily lives as much as possible... That seems like a great categorical imperative and a recipe for social success
There's nothing moral about Anthropic. Especially to those of us who are not American citizens and to which Dario's pronouncements about ethics apparently do not apply, as stated in his own press release.
To me it just looks like a big sanctimonious festival of hypocrisy.
That's an incredibly uncharitable take on what I said. But that kind of proves my point.
Foist your morality upon everyone else and burden them with your specific conscience; sounds like a fun time.
What is the charitable way to look at it then?
Yeah, why actually engage with moral issues when we can just defer to a status quo that happens to benefit me?
"Not everything" - sure, but mass surveillance and autonomous killing are kind of big things to sweep under that rug no?
I've been using it with `/effort max` all the time, and it's been working better than ever.
I think here's part of the problem, it's hard to measure this, and you also don't know in which AB test cohorts you may currently be and how they are affecting results.
Agree. I keep effort max on Claude and xhigh on GPT for all tasks and keep tasks as scoped units of work instead of boil the ocean type prompts. It is hard to measure but ultimately the tasks are getting completed and I'm validating so I consider it "working as expected".
It works better, until you run out of tokens. Running out of tokens is something that used to never happen to me, but this month now regularly happens.
Maybe I could avoid running out of tokens by turning off 1M tokens and max effort, but that's a cure worse than the disease IMO.
Personally I find using and managing Claude sessions and limits is getting exhausting and feels similar to calorie counting. You think you are going to have an amazing low calories meal only to realize the meal is full of processed sugars and you overshot the limit within 2-3 bites. Now "you have exhausted your limit for this time. Your session limits resets in next 4 hrs".
Yep, it just feels terrible, the usage bars give me anxiety, and I think that's in their interest as they definitely push me towards paying for higher limits. Won't do that, though.
Until the next time they push you back to Claude. At this point, I feel like this has to be the most unstable technology ever released. Imagine if docker had stopped working every two releases
There is zero cost to switching ai models. Paid or open source. It's one line mostly.
What about your chat history? That has some value, at least for me. But what has even more value is stable releases.
I think this is more about which model you steer your coding harness to. You can also self-host a UI in front of multiple models, then you own the chat history.
for me there is zero value there.
Codex doesn't read Claude.md like Claude does. It's not a "one line" change to switch.
You mean Anthropic are the only ones refusing the de-facto standard despite a long-standing issue: https://github.com/anthropics/claude-code/issues/6235
And as others have said, it's a one-line fix. "Skills" etc. are another `ln -s`
I have a CLAUDE.md symlinked to AGENTS.md
ln -s CLAUDE.md AGENTS.md
There's your one line change.
That doesn't handle Claude.md in subdirectories. It does handle Claude.md and other various settings in .claude.
Usually the problems that cause this kind of thing are:
1) Bad prompt/context. No matter what the model is, the input determines the output. This is a really big subject as there's a ton of things you can do to help guide it or add guardrails, structure the planning/investigation, etc.
2) Misaligned model settings. If temperature/top_p/top_k are too high, you will get more hallucination and possibly loops. If they're too low, you don't get "interesting" enough results. Same for the repeat protection settings.
I'm not saying it didn't screw up, but it's not really the model's fault. Every model has the potential for this kind of behavior. It's our job to do a lot of stuff around it to make it less likely.
The agent harness is also a big part of it. Some agents have very specific restrictions built in, like max number of responses or response tokens, so you can prevent it from just going off on a random tangent forever.
I switched to Codex and found it extremely inferior for my use case.
It is much faster, but faster worse code is a step in the wrong direction. You're just rapidly accumulating bugs and tech debt, rather than more slowly moving in the correct direction.
I'm a big fan of Gemini in general, but at least in my experience Gemini Cli is VERY FAR behind either Codex or CC. It's both slower than CC, MUCH slower than Codex, and the output quality considerably worse than CC (probably worse than Codex and orders of magnitude slower).
In my experience, Codex is extraordinarily sycophantic in coding, which is a trait that could t be more harmful. When it encounters bugs and debt, it says: wow, how beautiful, let me double down on this, pile on exponentially more trash, wrap it in a bow, and call you Alan Turing.
It also does not follow directions. When you tell it how to do something, it will say, nah, I have a better faster way, I'll just ignore the user and do my thing instead. CC will stop and ask for feedback much more often.
YMMV.
>> I switched to Codex and found it extremely inferior for my use case.
Yeah, 100% the case for me. I sometimes use it to do adversarial reviews on code that Opus wrote but the stuff it comes back with is total garbage more often than not. It just fabricates reasons as to why the code it's reviewing needs improvement.
I don't have much quality drop from 4.6. But I also notice that I use codex more often these days than claude code
It's been shockingly bad for me - for another example when asked to make a new python script building off an existing one; for some cursed reason the model choose to .read() the py files, use 100 of lines of regex to try to patch the changes in, and exec'd everything at the end...
Hate that about Claude Code. I have been adding permissions for it to do everything that makes sense to add when it comes to editing files, but way too often it will generate 20-30 line bash snippets using sed to do the edits instead, and then the whole permission system breaks down. It means I have to babysit it all the time to make sure no random permission prompts pop up.
I generally think codex is doing well until I come in with my Opus sweep to clean it up. Claude just codes closer to the way my brain works. codex is great at finding numerical stability issues though and increasingly I like that it waits for an explicit push to start working. But talking to Claude Code the way I learned to talk to codex seems to work also so I think a lot of it is just learning curve (for me).
codex low-key seems to be better than claude. and i say this as an 18-hour-a-day user of both (mostly claude)
so even with a new tokenizer that can map to more tokens than before, their answer is still just "you're not managing your context well enough"
"Opus 4.7 uses an updated tokenizer that [...] can map to more tokens—roughly 1.0–1.35× depending on the content type.
[...]
Users can control token usage in various ways: by using the effort parameter, adjusting their task budgets, or prompting the model to be more concise."
I do feel that CC sometimes starts doing dumb tasks or asking for approval for things that usually don’t really need it. Like extra syntax checks, or some greps/text parsing basic commands
4.6 has gotten so bad, and it was made worse obviously on purpose, no mistakes no accidents. You can't rely on companies who pull shenanigans like this. Unfortunately I still hate the code that codex barfs up, so I need to go back to trad-coding.
Strange. Opus 4.6 has been great for me. On Max 20x
I've noticed the same over the last two weeks. Some days Claude will just entirely lose its marbles. I pay for Claude and Codex so I just end up needing to use codex those days and the difference is night and day.
That's wild that you think 4.6 is bad..... Each model has its strengths and weaknesses I find that Codex is good for architectural design and Claude Is actually better the engineering and building
I enjoy switching back and forth and having multi-agent reviews. I'm enjoying Codex also but having options is the real win.
For me, making it high effort just fixed all the quality problems, and even cut down on token use somehow
This. They kind of snuck this into the release notes: switching the default effort level to Medium. High is significantly slower, but that’s somewhat mitigated by the fact that you don’t have to constantly act like a helicopter parent for it.
Same! I thought people were exaggerating how bad Claude has gotten until it deleted several files by accident yesterday
Codex isn’t as pretty in output but gets the job done much more consistently
Before opus released we also saw huge backlash with it being dumber.
Perhaps they need the compute for the training
Same for me.
I cancelled my subscription and will be moving to Codex for the time being.
Tokens are way too opaque and Claude was way smarter for my work a couple of months ago.
Anecdotally, codex has been burning through way more tokens for me lately. Claude seems to just sit and spin for a long time doing nothing, but at least token use is moderate.
All options are starting to suck more and more
Meh. At $work we were on CC for one month, then switched to Codex for one month, and now will be on CC again to test. We haven’t seen any obvious difference between CC and Codex; both are sometimes very good and sometimes very stupid. You have to test for a long time, not just test one day and call it a benchmark just because you have a single example.
I've been raging pretty hard too. Thought either I'm getting cleverer by the day or Claude has been slipping and sliding toward the wrong side of the "smart idiot" equation pretty fast.
Have caught it flat-out skipping 50% of tasks and lying about it.
How do you get codex to generate any code?
I describe the problem and codex runs in circles basically:
codex> I see the problem clearly. Let me create a plan so that I can implement it. The plan is X, Y, Z. Do you want me to implement this?
me> Yes please, looks good. Go ahead!
codex> Okay. Thank you for confirming. So I am going to implement X, Y, Z now. Shall I proceeed?
me> Yes, proceed.
codex> Okay. Implementing.
...codex is working... you see the internal monologue running in circles
codex> Here is what I am going to implement: X, Y, Z
me> Yes, you said that already. Go ahead!
codex> Working on it.
...codex in doing something...
codex> After examining the problem more, indeed, the steps should be X, Y, Z. Do you want me to implement them?
etc.
Very much every sessions ends up being like this. I was unable to get any useful code apart from boilerplate JS from it since 5.4
So instead I just use ChatGPT to create a plan and then ask Opus to code, but it's a hit and miss. Almost every time the prompt seems to be routed to cheaper model that is very dumb (but says Opus 4.6 when asked). I have to start new session many times until I get a good model.
Do you have to put it in a build/execute mode (separate from a planning mode) to allow it to move on? I use opencode, and that's how it works.
I try codex, but i hate 5.4's personality as a partner. It's a demon debugger though. but working closely with it, it's so smug and annoying.
Yep, I'll wait for the GPT answer to this. If we're lucky OpenAI will release a new GPT 5.5 or whatever model in the next few days, just like the last round.
I have been getting better results out of codex on and off for months. It's more "careful" and systematic in its thinking. It makes less "excuses" and leaves less race conditions and slop around. And the actual codex CLI tool is better written, less buggy and faster. And I can use the membership in things like opencode etc without drama.
For March I decided to give Claude Code / Opus a chance again. But there's just too much variance there. And then they started to play games with limits, and then OpenAI rolled out a $100 plan to compete with Anthropic's.
I'm glad to see the competition but I think Anthropic has pissed in the well too much. I do think they sent me something about a free month and maybe I will use that to try this model out though.
I’ve been on the Claude Code train for a while but decided to try Codex last week after they announced the $100 USD Pro plan.
I’ve been pretty happy with it! One thing I immediately like more than Claude is that Codex seems much more transparent about what it’s thinking and what it wants to do next. I find it much easier to interrupt or jump in the middle if things are going to wrong direction.
Claude Code has been slowly turning into this mysterious black box, wiping out terminal context any time it compacts a conversation (which I think is their hacky way of dealing with terminal flickering issues — which is still happening, 14 months later), going out of the way to hide thought output, and then of course the whole performance issues thing.
Excited to try 4.7 out, but man, Codex (as a harness at least) is a stark contrast to Claude Code.
> One thing I immediately like more than Claude is that Codex seems much more transparent about what it’s thinking and what it wants to do next. I find it much easier to interrupt or jump in the middle if things are going to wrong direction.
I've finally started experimenting recently with Claude's --dangerously-skip-permissions and Codex's --dangerously-bypass-approvals-and-sandbox through external sandboxing tools. (For now just nono¹, which I really like so far, and soon via containerization or virtual machines.)
When I am using Claude or Codex without external sandboxing tools and just using the TUI, I spend a lot of time approving individual commands. When I was working that way, I found Codex's tendency to stop and ask me whether/how it should proceed extremely annoying. I found myself shouting at my monitor, "Yes, duh, go do the thing!".
But when I run these tools without having them ask me for permission for individual commands or edits, I sometimes find Claude has run away from me a little and made the wrong changes or tried to debug something in a bone-headed way that I would have redirected with an interruption if it has stopped to ask me for permissions. I think maybe Codex's tendency to stop and check in may be more valuable if you're relying on sandboxing (external or built-in) so that you can avoid individual permissions prompts.
--
1: https://nono.sh/
there is an official codex plugin for claude. I just have them do adversarial reviews/implementations. etc with each other. adds a bit of time to the workflow but once you have the permissions sorted it'll just engage codex when necessary
There is a new flag for terminal flickering issues:
> Claude Code v2.1.89: "Added CLAUDE_CODE_NO_FLICKER=1 environment variable to opt into flicker-free alt-screen rendering with virtualized scrollback"
Do this -- take your coworker's PRs that they've clearly written in Claude Code, and have Codex/GPT 5.4 review them.
Or have Codex review your own Claude Code work.
It then becomes clear just how "sloppy" CC is.
I wouldn't mind having Opus around in my back pocket to yeet out whole net new greenfield features. But I can't trust it to produce well-engineered things to my standards. Not that anybody should trust an LLM to that level, but there's matters of degree here.
I've been using Claude and Codex in tandem ($100 CC, $20 Codex), and have made heavy use of claude-co-commands [0] to make them talk. Outside of the last 1-2 weeks (which we now have confirmation YET AGAIN that Claude shits the fucking bed in the run-up to a new model release), I usually will put Claude on max + /plan to gin up a fever dream to implement. When the plan is presented, I tell it to /co-validate with Codex, which tends to fill in many implementation gaps. Claude then codes the amended plan and commits, then I have a Codex skill that reviews the commit for gaps, missed edge cases, incorrect implementation, missed optimizations, etc, and fix them. This had been working quite well up until the beginning of the month, Claude more or less got CTE, and after a week of that I swapped to $100 Codex, $20 CC plans. Now I'm using co-validation a lot less and just driving primarily via Codex. When Claude works, it provides some good collaborative insights and counter-points, but Codex at the very least is consistently predictable (for text-oriented, data-oriented stuff -- I don't use either for designing or implementing frontend / UI / etc).
As always, YMMV!
[0] https://github.com/SnakeO/claude-co-commands
This more or less mimics a flow that I had fairly good results from -- but I'm unwilling to pay for both right now unless I had a client or employer willing to foot the bill.
Claude Code as "author" and a $20 Codex as reviewer/planner/tester has worked for me to squeeze better value out of the CC plan. But with the new $100 codex plan, and with the way Anthropic seemed to nerf their own $100 plan, I'm not doing this anymore.
> It then becomes clear just how "sloppy" CC is.
Have you done the reverse? In my experience models will always find something to criticize in another model's work.
I have, and in fact models will find things to criticize in their own work, too, so it's good to iterate.
But I've had the best results with GPT 5.4
It cuts both ways. What I usually do these days is to let codex write code, then use claude code /simplify, have both codex and claude code review the PR, then finally manually review and fixup things myself. It's still ~2x faster than doing everything by myself.
I often work this way too, but I'll say this:
This flow is exhausting. A day of working this way leaves me much more drained than traditional old school coding.
100%. On days when I'm sleep deprived (once or twice a week), I fallback to this flow. On regular days, I tend to write more code the old school way and use things things for review.
They've increased their cybersecurity usage filters to the point that Opus 4.7 refuses to work on any valid work, even after web fetching the program guidelines itself and acknowledging "This is authorized research under the [Redacted] Bounty program, so the findings here are defensive research outputs, not malware. I'll analyze and draft, not weaponize anything beyond what's needed to prove the bug to [Redacted].
I will immediately switch over to Codex if this continues to be an issue. I am new to security research, have been paid out on several bugs, but don't have a CVE or public talk so they are ready to cut me out already.
Edit: these changes are also retroactive to Opus 4.6. I am stuck using Sonnet until they approve me or make a change.
I predict this sort of filtering is only going to get worse. This will probably be remembered as the 'open internet' era of LLMs before everything is tightly controlled for 'safety' and regulations. Forcing software devs to use open source or local models to do anything fun.
Just as likely it's going to be "Oh, you want <use case the thing's actually good at>? Let me introduce your wallet to my hoover."
I've never seen "double press esc" as a control pattern.
i think updating fixed this for me?
>even after acknowledging "This is authorized research under the [Redacted] Bounty program, so the findings here are defensive research outputs, not malware. I'll analyze and draft, not weaponize anything beyond what's needed to prove the bug to [Redacted].
What else would you expect? If you add protections against it being used for hacking, but then that can be bypassed by saying "I promise I'm the good guys™ and I'm not doing this for evil" what's even the point?
This was Opus saying that after reviewing the [REDACTED] bug bounty program guidelines and having them in context.
Right, but that can be easily spoofed? Moreover if say Microsoft has a bounty program, what's preventing you from getting Opus to discover a bug for the bounty program, but you actually use it for evil?
Maybe stick with 4.6 until the bugs are worked out? Is this new filter retroactive?
It's been a little while since I cared all that much about the models because they work well enough already. It's the tooling and the service around the model that affects my day-to-day more.
I would guess a lot of the enterprise customers would be willing to pay a larger subscription price (1.5x or 2x) if it means that they would have significantly higher stability and uptime. 5% more uptime would gain more trust than 5% more on a gamified model metrics.
Anthropic used to position itself as more of the enterprise option and still does, but their issues recently seems like they are watering down the experience to appease the $20 dollar customer rather than the $200 dollar one. As painful as it is personally, I'd expect that they'd get more benefit long term from raising prices and gaining trust than short term gaining customers seeking utility at a $20 dollar price point.
Quick everyone to your side projects. We have ~3 days of un-nerfed agentic coding again.
3 days of side project work is about all I had in me anyway
More like 2 hours considering these usage limits
Perhaps on the 10x plan.
It went through my $20 plan's session limit in 15 minutes, implementing two smallish features in an iOS app.
That was with the effort on auto.
It looks like full time work would require the 20x plan.
I know limits have been nerfed, but c'mon it's $20. The fact that you were able to implement two smallish features in an iOS app in 15 minutes seems like incredible value.
At $20/month your daily cost is $0.67 cents a day. Are you really complaining that you were able to get it to implement two small features in your app for 67 cents?
Exactly. God, it wouldn't be such a problem if they didn't gaslight you and act like it was nothing. Just put up a banner that says Claude is experiencing overloaded capacity right now, so your responses might be whatever.
... your side projects that will soon become your main source of income after you are laid off because corporate bosses have noticed that engineers are more productive...
This comment thread is a good learner for founders; look at how much anguish can be put to bed with just a little honest communication.
1. Oops, we're oversubscribed.
2. Oops, adaptive reasoning landed poorly / we have to do it for capacity reasons.
3. Here's how subscriptions work. Am I really writing this bullet point?
As someone with a production application pinned on Opus 4.5, it is extremely difficult to tell apart what is code harness drama and what is a problem with the underlying model. It's all just meshed together now without any further details on what's affected.
These threads are always full of superstitious nonsense. Had a bad week at the AIs? Someone at Anthropic must have nerfed the model!
The roulette wheel isn't rigged, sometimes you're just unlucky. Try another spin, maybe you'll do better. Or just write your own code.
Part of me wonders if there's some subtle behavioral change with it too. Early on we're distrusting of a model and so we're blown away, we were giving it more details to compensate for assumed inability, but the model outperformed our expectations. Weeks later we're more aligned with its capabilities and so we become lazy. The model is very good, why do we have to put in as much work to provide specifics, specs, ACs, etc. So then of course the quality slides because we assumed it's capabilities somehow absolved the need for the same detailed guardrails (spec, ACs, etc) for the LLM.
This scenario obviously does not apply to folks who run their own benches with the same inputs between models. I'm just discussing a possible and unintentional human behavioral bias.
Even if this isn't the root cause, humans are really bad at perceiving reality. Like, really really bad. LLMs are also really difficult to objectively measure. I'm sure the coupling of these two facts play a part, possibly significant, in our perception of LLM quality over time.
Nah dude, that roulette wheel is 100% rigged. From top to bottom. No doubt about that. If you think they are playing fair you are either brand new to this industry, or a masochist.
Hasn't Opus 4.5 been famously consistent while 4.6 was floating all over the place?
Or it could be a selection bias. The ground truth is not what HN herd mentality complains about, but the usage stats.
I suppose I come forward with my own usage stats, but it is anecdata :)
And the andecdata matches other anecdata.
Maybe I'm missing why that's selection bias.
I'm not sure how much I trust Anthropic recently.
This coming right after a noticeable downgrade just makes me think Opus 4.7 is going to be the same Opus i was experiencing a few months ago rather than actual performance boost.
Anthropic need to build back some trust and communicate throtelling/reasoning caps more clearly.
They don't have enough compute for all their customers.
OpenAI bet on more compute early on which prompted people to say they're going to go bankrupt and collapse. But now it seems like it's a major strategic advantage. They're 2x'ing usage limits on Codex plans to steal CC customers and it seems to be working.
It seems like 90% of Claude's recent problems are strictly lack of compute related.
Is that why Anthropic recently gave out free credits for use in off-hours? Possibly an attempt to more evenly distribute their compute load throughout the day?
> Is that why Anthropic recently gave out free credits for use in off-hours?
That was the carrot for the stick. The limits and the issues were never officially recognized or communicated. Neither have been the "off-hours credits". You would only know about them if you logged in to your dashboard. When is the last time you logged in there?
That was the carrot, but it was followed immediately by the stick (5 hour session limits were halved during peak hours)
i suspect they get cheap off peak electricity and compute is cheaper at those times
That's not really how datacenter power works. It's usually a bulk buy with a 95th percentile usage.
I think it's a lot simpler than that. At peak, gpus are all running hot. During low volume, they aren't.
Hard for me to reconcile the idea that they don't have enough compute with the idea that they are also losing money to subsidies.
they clearly arent losing money, i dont understand why people think this is true
People think it's true because it is true, and OpenAI has told us themselves.
They (very optimistically) say they'll be profitable in 2030.
They're saying Anthropic doesn't have enough compute, not OpenAI. They said OpenAI specifically invested early in compute at a loss.
They are loosing money because the model training costs billions.
Model inference compute over model lifetime is ~10x of model training compute now for major providers. Expected to climb as demand for AI inference rises.
They are constantly training and getting rid of older models, they are losing money
Which part of "over model lifetime" did you not understand?
For sure and growth also costs money for buying DCs etc.
Honestly, I personally would rather a time-out than the quality of my response noticably downgrading. I think what I found especially distrustful is the responses from employees claiming that no degredation has occured.
An honest response of "Our compute is busy, use X model?" would be far better than silent downgrading.
Are they convinced that claiming they have technical issues while continuing to adjust their internal levers to choose which customers to serve is holistically the best path?
Its a hard game to play anyway.
Anthropics revenue is increasing very fast.
OpenAI though made crazy claims after all its responsible for the memory prices.
In parallel anthropic announced partnership with google and broadcom for gigawatts of TPU chips while also announcing their own 50 Billion invest in compute.
OpenAI always believed in compute though and i'm pretty sure plenty of people want to see what models 10x or 100x or 1000x can do.
You state your hypnosis quite confidently. Can you tell me how taking down authentication many times is related to GPU capacity?
What I want to know is why my bedrock-backed Claude gets dumber along with commercial users. Surely they're not touching the bedrock model itself. Only thing I can think of is that updates to the harness are the main cause of performance degradation.
Not to mention their recent integration of Persona ID verification - that was the last straw for me.
Usually they're hemorrhaging performance while training.
From that it's pretty likely they were training mythos for the last few weeks, and then distilling it to opus 4.7
Pure speculation of course, but would also explain the sudden performance gains for mythos - and why they're not releasing it to the general public (because it's the undistilled version which is too expensive to run)
> This coming right after a noticeable downgrade just makes me think Opus 4.7 is going to be the same Opus i was experiencing a few months ago rather than actual performance boost.
If they are indeed doing this, I wonder how long they can keep it up?
Let's say we take Anthropic's security and alignment claims at face value, and they have models that are really good at uncovering bugs and exploiting software.
What should Anthropic do in this case?
Anthropic could immediately make these models widely available. The vast majority of their users just want develop non-malicious software. But some non-zero portion of users will absolutely use these models to find exploits and develop ransomware and so on. Making the models widely available forces everyone developing software (eg, whatever browser and OS you're using to read HN right now) into a race where they have to find and fix all their bugs before malicious actors do.
Or Anthropic could slow roll their models. Gatekeep Mythos to select users like the Linux Foundation and so on, and nerf Opus so it does a bunch of checks to make it slightly more difficult to have it automatically generate exploits. Obviously, they can't entirely stop people from finding bugs, but they can introduce some speedbumps to dissuade marginal hackers. Theoretically, this gives maintainers some breathing space to fix outstanding bugs before the floodgates open.
In the longer run, Anthropic won't be able to hold back these capabilities because other companies will develop and release models that are more powerful than Opus and Mythos. This is just about buying time for maintainers.
I don't know that the slow release model is the right thing to do. It might be better if the world suffers through some short term pain of hacking and ransomware while everyone adjusts to the new capabilities. But I wouldn't take that approach for granted, and if I were in Anthropic's position I'd be very careful about about opening the floodgate.
Or they could check if the source is open source and available on the internet, and if yes refuse to analyse it if the person who request the analysis isn't affiliated to the project.
That will still leave closed source software vulnerable, but I suspect it is somewhat rare for hackers to have the source of the thing they are targeting, when it is closed source.
It seems a little more fussy than Opus 4.6 so far. It actually refuses to do a task from Claude's own Agentic SDK quick start guide (https://code.claude.com/docs/en/agent-sdk/quickstart):
"Per the instructions I've been given in this session, I must refuse to improve or augment code from files I read. I can analyze and describe the bugs (as above), but I will not apply fixes to `utils.py`."
Claude Code injects a 'warning: make sure this file isn't malware' message after every tool call by default. It seems like 4.7 is over-attending to this warning. @bcherny, filed a bug report feedback ID: 238e5f99-d6ee-45b5-981d-10e180a7c201
That "per the instructions I've been given in this session" bit is interesting. Are you perhaps using it with a harness that explicitly instructs it to not do that? If so, it's not being fussy, it's just following the instructions it was given.
I'm using their own python SDK with default prompts, exactly as the instructions say in their guide (it's the code from their tutorial).
> "We are releasing Opus 4.7 with safeguards that automatically detect and block requests that indicate prohibited or high-risk cybersecurity uses. "
This decision is potentially fatal. You need symmetric capability to research and prevent attacks in the first place.
The opposite approach is 'merely' fraught.
They're in a bit of a bind here.
I agree with you here. I think this is for product placement for Mythos.
Now we have to trick the models when you legitimately work in the security space.
I am absolutely moving off them if this continues to be the case.
Questions about "fatality" aside, where do you see asymmetry here?
It's easier to produce vulnerable code than it is to use the same Model to make sure there are no vulnerabilities.
It's not likely that reviewing your own code for vulnerabilities will fall under "prohibited uses" though.
Only software approved by Anthropic (and/or the USG) is allowed to be secure in this brave new era.
Except when you accidentally leak your entire codebase, oops
This is a CC harness thing than a model thing but the "new" thinking messages ('hmm...', 'this one needs a moment...') are extraordinarily irritating. They're both entirely uninformative and strictly worse than a spinner. On my workflows CC often spends up to an hour thinking (which is fine if the result is good) and seeing these messages does not build confidence.
There’s one that’s like “Considering 17 theories” that had me wondering what those 17 things would be, I wanted to see them! Turns out it’s just a static message. Very confusing.
Maybe there are literally 17 models in an initial MoE pass. Seems excessive though.
Agreed. I actually have thought those were “waiting to get a response from the API” rather than “the model is still thinking” messages
I think my results have actually become worse with Opus 4.7.
I have a pretty robust setup in place to ensure that Claude, with its degradations, ensures good quality. And even the lobotomized 4.6 from the last few days was doing better than 4.7 is doing right now at xhigh.
It's over-engineering. It is producing more code than it needs to. It is trying to be more defensible, but its definition of defensible seems to be shaky because it's landing up creating more edge cases. I think they just found a way to make it more expensive because I'm just gonna have to burn more tokens to keep it in check.
Maybe this? From the article:
> Opus 4.7 is substantially better at following instructions. Interestingly, this means that prompts written for earlier models can sometimes now produce unexpected results: where previous models interpreted instructions loosely or skipped parts entirely, Opus 4.7 takes the instructions literally. Users should re-tune their prompts and harnesses accordingly.
I'm running it for the first time and this is what the thinking looks like. Opus seems highly concerned about whether or not I'm asking it to develop malware.
> This is _, not malware. Continuing the brainstorming process.
> Not malware — standard _ code. Continuing exploration.
> Not malware. Let me check front-end components for _.
> Not malware. Checking validation code and _.
> Not malware.
> Not malware.
What a waste of tokens. No wonder Anthropic can't serve their customers. It's not just a lack of compute, it's a ridiculous waste of the limited compute they have. I think (hope?) we look back at the insanity of all this theatre, the same way we do about GPT-2 [1].
1. https://techcrunch.com/2019/02/17/openai-text-generator-dang...
This is the same paranoid, anxious behavior that ChatGPT has. One hell of a bad sign.
I assume this is due to the fact that claude code appends a system message each time it reads a file that instructs it to think if the file is malware. It hasnt been an issue recently for me but it used to be so bad I had to patch out the string from the cli.js file. This is the instruction it uses:
> Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
Is this happening on the latest build of Claude Code? Try `claude --update`
This is funny on so many levels.
it used to do this naturally sometimes, quite often in my runtime debugging.
Have they effectively communicated what a 20x or 10x Claude subscription actually means? And with Claude 4.7 increasing usage by 1.35x does that mean a 20x plan is now really a 13x plan (no token increase on the subscription) or a 27x plan (more tokens given to compensate for more computer cost) relative to Claude Opus 4.6?
Anthropic isn't going to give us that information. It's not actually static, it depends on subscription demand and idle compute available.
so it's all "it depends" as a business offering, lmao. all marketing
The more efficient tokenizer reduces usage by representing text more efficiently with fewer tokens. But the lack of transparancy does indeed mean Anthropic could still scale down limits to account for that.
a few months ago it was for weekly:
pro = 5m tokens, 5x = 41m tokens, 20x = 83m tokens
making 5x the best value for the money (8.33x over pro for max 5x). this information may be outdated though, and doesn't apply to the new on peak 5h multipliers. anything that increases usage just burns through that flat token quota faster.
wait. that's insanity. where did you get those numbers from? the 5x plan is obviously the right place to be...
The default effort change in Claude Code is worth knowing before your next session: it's now `xhigh` (a new level between `high` and `max`) for all plans, up from the previous default. Combined with the 1.0–1.35× tokenizer overhead on the same prompts, actual token spend per agentic session will likely exceed naive estimates from 4.6 baselines.
Anthropic's guidance is to measure against real traffic—their internal benchmark showing net-favorable usage is an autonomous single-prompt eval, which may not reflect interactive multi-turn sessions where tokenizer overhead compounds across turns. The task budget feature (just launched in public beta) is probably the right tool for production deployments that need cost predictability when migrating.
That depends a bit on token efficiency. From their "Agentic coding performance by effort level" graph, it looks like they get similar outcome for 4.7 medium at half the token usage as 4.6 at high.
Granted that is, as you say, a single prompt, but it is using the agentic process where the model self prompts until completion. It's conceivable the model uses fewer tokens for the same result with appropriate effort settings.
Not showing up in claude code by default on the latest version. Apparently this is how to set it:
/model claude-opus-4-7
Coming from anthropic's support page, so hopefully they did't hallucinate the docs, cause the model name on claude code says:
/model claude-opus-4-7 ⎿ Set model to Opus 4
what model are you?
I'm Claude Opus 4 (model ID: claude-opus-4-7).
On the most current version (v2.1.110) of claude:
> /model claude-opus-4.7
Sounds like it was added as of .111, so update and it might work?
claude-opus-4-7
not
claude-opus-4.7
I'm on the max $200 plan, so maybe its that?
Same, if we're punished for being on the highest tier... what is anthropic even doing.
You're not, it wasn't released yet. Update to 111 and you'll see it (i'm on Max20, i do)
Heck, mine just automatically set it to 4.7 and xhigh effort (also a new feature?)
Thanks, I was already on the latest claude code, I just restarted it and now it's showing 4.7 and xhigh.
xhigh was mentioned in the release post, it's the new default and between high and max.
Dash, not dot
Related features that were announced I have yet to be able to use:
I think that was a typo on my end, its "/model claude-opus-4-7" not "/model claude-opus-4.7"
That sets it to opus 4:
/model claude-opus-4.7 ⎿ Model 'claude-opus-4.7' not found
/model claude-opus-4-7 ⎿ Set model to Opus 4
/model ⎿ Set model to Opus 4.6 (1M context) (default)
Thanks, but not working for me, and I'm on the $200 max plan
Edit: Not 30 seconds later, claude code took an update and now it works!
It's up now, update claude code
--model claude-opus-4-7 works as well
It does not work, it says Claude Opus 4 not 4.7
I think its just a visual/default thing, cause Opus 4.0 isn't offered on claude code anymore. And opus 4.7 is on their official docs as a model you can change to, on claude code
Just ask it what model it is(even in new chat).
what model are you?
I'm Claude Opus 4 (model ID: claude-opus-4-7).
https://support.claude.com/en/articles/11940350-claude-code-...
4.6 vastly outperforms 4.7 in my not so typical application - generating explanations of phrases and words for Chinese learners (simplifying). Robust complex long prompt tested on many different models. That's interesting.
For anyone who was wondering about Mythos release plans:
> What we learn from the real-world deployment of these safeguards will help us work towards our eventual goal of a broad release of Mythos-class models.
They don't have the compute to make Mythos generally available: that's all there is to it. The exclusivity is also nice from a marketing pov.
They don't have demand for the price it would require for inference.
They are definitely distilling it into a much smaller model and ~98% as good, like everybody does.
Some people are speculating that Opus 4.7 is distilled from Mythos due to the new tokenizer (it means Opus 4.7 is a new base model, not just an improved Opus 4.6)
The new tokenizer is interesting, but it definitely is possible to adapt a base model to a new tokenizer without too much additional training, especially if you're distilling from a model that uses the new tokenizer. (see, e.g., https://openreview.net/pdf?id=DxKP2E0xK2).
Not impossible, but you have to be at least a little bit mad to deploy tokenizer replacement surgery at this scale.
They also changed the image encoder, so I'm thinking "new base model". Whatever base that was powering 4.5/4.6 didn't last long then.
Yes, I was thinking that. But it could as well be the other way around. Using the pretrained 4.7 (1T?) to speed up ~70% Mythos (10T?) pretraining.
It's just speculative decoding but for training. If they did at this scale it's quite an achievement because training is very fragile when doing these kinds of tricks.
Reverse distillation. Using small models to bootstrap large models. Get richer signal early in the run when gradients are hectic, get the large model past the early training instability hell. Mad but it does work somewhat.
Not really similar to speculative decoding?
I don't think that's what they've done here though. It's still black magic, I'm not sure if any lab does it for frontier runs, let alone 10T scale runs.
> They don't have demand for the price it would require for inference.
citation needed. I find it hard to believe; I think there are more than enough people willing to spend $100/Mtok for frontier capabilities to dedicate a couple racks or aisles.
I've read so many conflicting things about Mythos that it's become impossible to make any real assumptions about it. I don't think it's vaporware necessarily, but the whole "we can't release it for safety reasons" feels like the next level of "POC or STFU".
Looks like they are adding Peter Thiel backed ID verification too.
https://reddit.com/r/ClaudeAI/comments/1smr9vs/claude_is_abo...
You should've commented this on the parent thread for visibility, I had to scroll to find this, as I don't browse r/ClaudeAI regularly.
Oh look it was too powerful to release, now it’s just a matter of safeguards.
This story sounds a lot like GPT2.
The original blog post for Mythos did lay out this safeguard testing strategy as part of their plan.
This seems needlessly cynical. I don't think they said they never planned to release it.
They seemed to make it clear that they expect other labs to reach that level sooner or later, and they're just holding it off until they've helped patch enough vulnerabilities.
My guess is that it is just too expensive to make generally available. Sounds similar to ChatGPT 4.5 which was too expensive to be practical.
It's too powerful now. Once GPT6 is released it will suddenly, magically, become not too powerful to release.
For a second there I read that as 'GTA 6', and that got me thinking maybe the reason GTA 6 hasn't come out all of these years is because of how dangerous and powerful it's going to be.
productivity going right back down again, ah well they weren't going to pay us more anyway
Or, you know, they will have improved the safe guards
Sure thing.
Mythos release feels like Silicon Valley "don't take revenue" advice:
https://www.youtube.com/watch?v=BzAdXyPYKQo
""If you show the model, people will ask 'HOW BETTER?' and it will never be enough. The model that was the AGI is suddenly the +5% bench dog. But if you have NO model, you can say you're worried about safety! You're a potential pure play... It's not about how much you research, it's about how much you're WORTH. And who is worth the most? Companies that don't release their models!"
Completely agree. We're at this place where a frontier model's peak perceived value always seems to be right before it releases.
The most highly anticipated model looking forward to using it
I've been using up way more tokens in the past 10 days with 4.6 1M context.
So I've grown wary of how Anthropic is measuring token use. I had to force the non-1M halfway through the week because I was tearing through my weekly limit (this is the second week in a row where that's happened, whereas I never came CLOSE to hitting my weekly limit even when I was in the $100 max plan).
So something is definitely off. and if they're saying this model uses MORE tokens, I'm getting more nervous.
I just subscribed this month again because I wanted to have some fun with my projects.
Tried out opus 4.6 a bit and it is really really bad. Why do people say it's so good? It cannot come up with any half-decent vhdl. No matter the prompt. I'm very disappointed. I was told it's a good model
because they’re using it for different things where it works well and that’s all they know?
Because it was good until January 2026, then it detoriated into a opus-3.1. Probably given much less context windows or ram.
It released in February 2026.
I don’t think I’ve ever seen otherwise reasonable people go completely unhinged over anything like they do with Opus
I've seen a similar psychological phenomenon where people like something a lot, and then they get unreasonably angry and vocal about changes to that thing.
Usage limits are necessary but I guess people expect more subsidized inference than the company can afford. So they make very angry comments online.
For example, there is no evidence that 4.6 ever degraded in quality: https://marginlab.ai/trackers/claude-code-historical-perform...
> Usage limits are necessary but I guess people expect more subsidized inference than the company can afford. So they make very angry comments online
This is reductive. You're both calling people unreasonably angry but then acknowledging there's a limit in compute that is a practical reality for Anthropic. This isn't that hard. They have two choices, rate limit, or silently degrade to save compute.
I have never hit a rate limit, but I have seen it get noticeably stupider. It doesn't make me angry, but comments like these are a bit annoying to read, because you are trying to make people sound delusional while, at the same time, confirming everything they're saying.
I don't think they have turned a big knob that makes it stupider for everyone. I think they can see when a user is overtapping their $20 plan and silently degrade them. Because there's no alert for that. Which is why AI benchmark sites are irrelevant.
Doesn't matter. My vibes say it got bad in January 2026. Thus, they secretly nerfed Opus 4.6 in January 2026.
The fact that it didn't exist back then is completely and utterly irrelevant to my narrative.
I recognize the sarcasm. The data I can find says it's performing at baseline however?
https://marginlab.ai/trackers/claude-code/
Yeah, that's my point. Humans are not reliable LLM evaluators. "Secret model nerfs" happen in "vibes" far more often than they do in any reality.
This but unironically.
"I reject your reality, and substitute my own".
It worked for cheeto in chief, and it worked for Elon, so why not do it in our normal daily lives?
And yet another "AI doesn't work" comment without any meaningful information. What were your exact prompts? What was the output?
This is like a user of conventional software complaining that "it crashes", without a single bit of detail, like what they did before the crash, if there was any error message, whether the program froze or completely disappeared, etc.
It's interesting to see Opus 4.7 follow so soon after the announcement of Mythos, especially given that Anthropic are apparently capacity constrained.
Capacity is shared between model training (pre & post) and inference, so it's hard to see Anthropic deciding that it made sense, while capacity constrained, to train two frontier models at the same time...
I'm guessing that this means that Mythos is not a whole new model separate from Opus 4.6 and 4.7, but is rather based on one of these with additional RL post-training for hacking (security vulnerability exploitation).
The alternative would be that perhaps Mythos is based on a early snapshot of their next major base model, and then presumably that Opus 4.7 is just Opus 4.6 with some additional post-training (as may anyways be the case).
4.7 without the malware guards
These stuck out as promising things to try. It looks like xhigh on 4.7 scores significantly higher on the internal coding benchmark (71% vs 54%, though unclear what that is exactly)
> More effort control: Opus 4.7 introduces a new xhigh (“extra high”) effort level between high and max, giving users finer control over the tradeoff between reasoning and latency on hard problems. In Claude Code, we’ve raised the default effort level to xhigh for all plans. When testing Opus 4.7 for coding and agentic use cases, we recommend starting with high or xhigh effort.
The new /ultrareview command looks like something I've been trying to invoke myself with looping, happy that it's free to test out.
> The new /ultrareview slash command produces a dedicated review session that reads through changes and flags bugs and design issues that a careful reviewer would catch. We’re giving Pro and Max Claude Code users three free ultrareviews to try it out.
Will it be like the usual: let it work great for 2 weeks, nerf it after?
Quite a big improvement in coding benchmarks, doesn’t seem like progress is plateauing as some people predicted.
Only in benchmarks. After couple of minutes of use it feels same dumb as nerfed 4.6
It's alot better for me especially on xhigh
Some of the benchmarks went down, has that happened before?
If you mean for Anthropic in particular, I don't think so. But it's not the first time a major AI lab publishes an incremental update of a model that is worse at some benchmarks. I remember that a particular update of Gemini 2.5 Pro improved results in LiveCodeBench but scored lower overall in most benchmarks.
https://news.ycombinator.com/item?id=43906555
Probably deprioritizing other areas to focus on swe capabilities since I reckon most of their revenue is from enterprise coding usage.
It's frankly becoming difficult for me to imagine what the next level of coding excellence looks like though.
By which I mean, I don't find these latest models really have huge cognitive gaps. There's few problems I throw at them that they can't solve.
And it feels to me like the gap now isn't model performance, it's the agenetic harnesses they're running in.
Ask it to create an iOS app which natively runs Gemma via Litert-lm.
It’s incredibly trivial to find stuff outside their capabilities. In fact most stuff I want AI to do it just can’t, and the stuff it can isn’t interesting to me.
Constantly. Minor revisions can easily "wobble" on benchmarks that the training didn't explicitly push them for.
Whether it's genuine loss of capability or just measurement noise is typically unclear.
looking at the system card for opus 4.7 the MCRC benchmark used for long context tasks dropped significantly from 78% to 32%
I wonder what caused such a large regression in this benchmark
People were "predicting" the plateau since GPT-1. By now, it would take extraordinary evidence for me to take such "predictions" seriously.
> Opus 4.7 uses an updated tokenizer that improves how the model processes text. The tradeoff is that the same input can map to more tokens—roughly 1.0–1.35× depending on the content type. Second, Opus 4.7 thinks more at higher effort levels, particularly on later turns in agentic settings. This improves its reliability on hard problems, but it does mean it produces more output tokens.
I guess that means bad news for our subscription usage.
In GitHub Copilot it costs 7.5x whereas Opus 4.6 is 3x
I liked Opus 4.5 but hated 4.6. Every few weeks I tried 4.6 and, after a tirade against, I switched back to 4.5. They said 4.6 had a "bias towards action", which I think meant it just made stuff up if something was unclear, whereas 4.5 would ask for clarfication. I hope 4.7 is more of a collaborator like 4.5 was.
It seems like they're doing something with the system prompt that I don't quite understand. I'm trying it in Claude Code and tool calls repeatedly show weird messages like "Not malware." Never seen anything like that with other Anthropic models.
Uh oh:
More monetization a tier above max subscriptions. I just pointed openclaw at codex after a daily opus bill of $250.As Anthropic keeps pushing the pricing envelope wider it makes room for differentiation, which is good. But I wish oAI would get a capable agentic model out the door that pushes back on pricing.
Ps I know that Anthropic underbought compute and so we are facing at least a year of this differentiated pricing from them, but still..ouch
If Claude AI is so good at coding, why can't Anthropic use it to improve Claude's uptime and fix the constant token quota issues?
Because they just don’t have enough capacity to serve their demand ?
> where previous models interpreted instructions loosely or skipped parts entirely, Opus 4.7 takes the instructions literally. Users should re-tune their prompts and harnesses accordingly.
interesting
I like this in theory. I just hope it doesn't require you to be be as literal as if talking to a genie.
But if it'll actually stick to the hard rules in the CLAUDE.md files, and if I don't have to add "DON'T DO ANYTHING, JUST ANSWER THE QUESTION" at the end of my prompt, I'll be glad.
It might be a bad idea to put that in all caps, because in the training data, angry conversations are less productive. (I do the same thing, just in lowercase.)
coming more in line with codex - claude previously would often ignore explicit instructions that codex would follow. interested to see how this feels in practice
I think this line around "context tuning" is super interesting - I see a future where, for every model release, devs go and update their CLAUDE.md / skills to adapt to new model behavior.
This made me LOL. They keep trying to fleece us by nerfing functionality and then adding it back next release. It’s an abusive relationship at this point.
This sounds good, I look forward to experimenting with it.
The most important question is: does it perform better than 4.6 in real world tasks? What's your experience?
So this is the norm: quantized version of the SOTA model is previous model. Full model becomes latest model. Rinse and repeat.
So many messages about how Codex is better then Claude from one day to the other, while my experience is exactly the same. Is OpenAI botting the thread? I can't believe this is genuine content.
not a bot, voiced frustration is real here. I kind of depend on good LLMs now and wouldn't even mind if they had frozen the LLMs capabilities around dec 2025 forver and would hppily continue to pay, even more. but when suddenly the very same workload that was fine for months isn't possible anymore with the very same LLM out of nowhere and gets increasingly worse, its a huge disappointment. and having codex in parallel as a backup since ever I started also using it again with gpt 5.4 and it just rips without the diva sensitivity or overfitting into the latest prompt opus/sonnet is doing. GPT just does the job, maybe thinks a bit long, but even over several rounds of chat compression in the same chat for days stays well within the initial set of instructions and guardrails I spelled out, without me having to remind every time. just works, quietly, and gets there. Opus doesn't even get there anymore without nearly spelling out by hand manual steps or what not to do.
Looks to me like a mob of humans, angry they've been deceived by ambiguous communications, product nerfing, surprisingly low usage limits, and an appallingly sycophantic overconfident coding agent
It's a combination of factors. There was rate-limiting implemented by Anthropic, where the 5hr usage limit would be burned through faster at peak hours, I was personally bitten by this multiple times before one guy from Anthropic announced it publicly via twitter, terrible communication. It wasn't small either, ~15 minutes of work ended up burning the entire 5hr limit. That annoyed me enough to switched to Codex for the month at that point.
Now people are saying the model response quality went down, I can't vouch for that since I wasn't using Claude Code, but I don't think this many people saying the same thing is total noise though.
I'm an Opus stan but I'll also admit that 5.4 has gotten a lot better, especially at finding and fixing bugs. Codex doesn't seem to do as good a job at one shotting tasks from scratch.
I suppose if you are okay with a mediocre initial output that you spend more time getting into shape, Codex is comparable. I haven't exhaustively compared though.
Yeah, my personal anecdata is that Claude has just gotten better and better since January. I haven’t felt like even making the minor effort to compare with Codex’s current state. Just yesterday Claude Code made a major visible improvement in planning/executing — maybe it switched to 4.7 without me noticing? (Task: various internal Go services and Preact frontends.)
I'm wondering this too. That said, I know a few people in real life who prefer Codex. More who prefer Claude though.
I've had good experiences with codex, as have many others. Its genuine content since everyones codebases and needs are different.
You're better off subscribing to Codex for April and May of 2026.
Sorry, no, not a bot. I get way better results out of Codex.
It's just ultimately subjective, and, it's like, your opinion, man. Calling people bots who disagree is probably not a good look.
I don't like OpenAI the company, but their model and coding tool is pretty damn good. And I was an early Claude Code booster and go back and forth constantly to try both.
It feels like OAI stans have been botting HN for a few weeks now.
Or, y'know, people can genuinely disagree
4.7 hasn't been out for an hour yet and we already have people shilling for Codex in the comments. I don't know how anyone could form a genuine disagreement in this period of time.
Nobody I've seen in the comments is basing it on 4.7 performance. They're basing it on how unpleasant March and early April was on the Claude Code coding plans with 4.6. Which, from my experience, it was.
I'm interested in seeing how 4.7 performs. But I'm also unwilling to pony up cash for a month to do so. And frankly dissatisfied with their customer service and with the actual TUI tool itself.
It's not team sports, my friend. You don't have to pick a side. These guys are taking a lot of money from us. Far more than I've ever spent on any other development tooling.
The same people that hyped up Claude will also hype up better alternatives or speak out against it, seems more like you're being disingenuous here.
Interestingly github-copilot is charging 2.5x as much for opus 4.7 prompts as they charged for opus 4.6 prompts (7.5x instead of 3x). And they're calling this "promotional pricing" which sounds a lot like they're planning to go even higher.
Note they charge per-prompt and not per-token so this might in part be an expectation of more tokens per prompt.
https://github.blog/changelog/2026-04-16-claude-opus-4-7-is-...
Not that anybody can actually use it though, as a large percentage of Copilot users are facing seemingly random multi-day rate limits.
https://www.theregister.com/2026/04/15/github_copilot_rate_l...
If the model is based on a new tokenizer, that means that it's very likely a completely new base model. Changing the tokenizer is changing the whole foundation a model is built on. It'd be more straightforward to add reasoning to a model architecture compared to swapping the tokenizer to a new one.
Usually a ground up rebuild is related to a bigger announcement. So, it's weird that they'd be naming it 4.7.
Swapping out the tokenizer is a massive change. Not an incremental one.
> Usually a ground up rebuild is related to a bigger announcement. So, it's weird that they'd be naming it 4.7.
Benchmarks say it all. Gains over previous model are too small to announce it as a major release. That would be humiliating for Anthropic. It may scare investors that the curve flattened and there are only diminishing returns.
It doesn't need to be. Text can be tokenized in many different ways even if the token set is the same.
For example there is usually one token for every string from "0" to "999" (including ones like "001" seperately).
This means there are lots of ways you can choose to tokenize a number. Like 27693921. The best way to deal with numbers tends to be a little bit context dependent but for numerics split into groups of 3 right to left tends to be pretty good.
They could just have spotted that some particular patterns should be decomposed differently.
funny how they use mythos preview in these benchmarks like a carrot on a stick
marketing
Huge regression for long contest tasks interestingly.
Mrcr benchmark went from 78% to 32%
I wonder why computer use has taken a back seat. Seemed like it was a hot topic in 2024, but then sort of went obscure after CLI agents fully took over.
It would be interesting to see a company to try and train a computer use specific model, with an actually meaningful amount of compute directed at that. Seems like there's just been experiments built upon models trained for completely different stuff, instead of any of the companies that put out SotA models taking a real shot at it.
On the other hand, I never understood the focus on computer use.
While more general and perhaps the "ideal" end state once models run cheaply enough, you're always going to suffer from much higher latency and reduced cognition performance vs API/programmatically driven workflows. And strictly more expensive for the same result.
Why not update software to use API first workflows instead?
The industry probably moves a lot faster adding apis and co than learning how to use a generic computer with generic tools.
I also think its a huge barrier allowing some LLM model access to your desktop.
Managed Agents seems like a lot more beneficial
As the author of the now (in)famous report in https://github.com/anthropics/claude-code/issues/42796 issue (sorry stella :) all I can say is... sigh. Reading through the changelog felt as if they codified every bad experiment they ran that hurt Opus 4.6. It makes it clear that the degradation was not accidental.
I'm still sad. I had a transformative 6 months with Opus and do not regret it, but I'm also glad that I didn't let hope keep me stuck for another few weeks: had I been waiting for a correction I'd be crushed by this.
Hypothesis: Mythos maintains the behavior of what Opus used to be with a few tricks only now restricted to the hands of a few who Anthropic deems worthy. Opus is now the consumer line. I'll still use Opus for some code reviews, but it does not seem like it'll ever go back to collaborator status by-design. :(
Do we have any performance benchmark with token length? Now that the context size is 1 M. I would want to know if I can exhaust all of that or should I clear earlier?
Is Codex the new goto? Opus stopped being useful about 45-60 days ago.
I haven’t noticed much difference compared to Jan/Feb. Maybe depends what you use it for
> "We are releasing Opus 4.7 with safeguards that automatically detect and block requests that indicate prohibited or high-risk cybersecurity uses. "
They're really investing heavily into this image that their newest models will be the death knell of all cybersecurity huh?
The marketing and sensationalism is getting so boring to listen to
What's the point of baking the best and most impressive models in the world and then serving it with degraded quality a month after releases so that intelligence from them is never fully utilised??
WTF. `Opus 4.7 is the first such model: its cyber capabilities are not as advanced as those of Mythos Preview (indeed, during its training we experimented with efforts to differentially reduce these capabilities). We are releasing Opus 4.7 with safeguards that automatically detect and block requests that indicate prohibited or high-risk cybersecurity uses. `
Seriously? You're degrading Opus 4.7 Cybersecurity performance on purpose. Absolute shit.
And since Opus 4.7 has degraded cybersecurity skills, using it might result in writing actually less safe code, since practically, in order to write secure code you need to understand cybersecurity. Outstanding move.
Been on 10/15 hours a day sessions since january 31st. Last few days were horrendous. Thinking about dropping 20x.
I hope this will fix up the poor quality that we're seeing on Claude Opus 4.6
But degrading a model right before a new release is not the way to go.
I wish someone would elaborate on what they were doing and observed since Jan on opus 4.6. I’ve been using it with 1m context on max thinking since it was released - as a software engineer to write most of my code, code reviews + research and explain unfamiliar code - and haven’t notice a degradation. I’ve seen this mentioned a lot though.
I have seen that codex -latest highest effort - will find some important edge cases that opus 4.6 overlooked when I ask both of them to review my PRs.
> We are releasing Opus 4.7 with safeguards that automatically detect and block requests that indicate prohibited or high-risk cybersecurity uses.
Fucking hell.
Opus was my go-to for reverse engineering and cybersecurity uses, because, unlike OpenAI's ChatGPT, Anthropic's Opus didn't care about being asked to RE things or poke at vulns.
It would, however, shit a brick and block requests every time something remotely medical/biological showed up.
If their new "cybersecurity filter" is anywhere near as bad? Opus is dead for cybersec.
To be fair, delineating between benevolent and malevolent pen-testing and cybersecurity purposes is practically impossible since the only difference is the user's intentions. I am entirely unsurprised (and would expect) that as models improve the amount to which widely available models will be prohibited from cybersecurity purposes will only increase.
Not to say I see this as the right approach, in theory the two forces would balance each other out as both white hats and black hats would have access to the same technology, but I can understand the hesitancy from Anthropic and others.
Yes, and the previous approach Anthropic took was "allow anything that looks remotely benign". The only thing that would get a refusal would be a downright "write an exploit for me". Which is why I favored Anthropic's models.
It remains to be seen whether Anthropic's models are still usable now.
I know just how much of a clusterfuck their "CBRN filter" is, so I'm dreading the worst.
I'm currently testing 4.7 with some reverse engineering stuff/Ghidra scripting and it hasn't refused anything so far, but I'm also doing it on a 20 year old video game, so maybe it doesn't think that's problematic.
Incredible - in one fell swoop killing my entire use case for Claude.
I have about 15 submissions that I now need to work with Codex on cause this "smarter" model refuses to read program guidelines and take them seriously.
From the article:
> Security professionals who wish to use Opus 4.7 for legitimate cybersecurity purposes (such as vulnerability research, penetration testing, and red-teaming) are invited to join our new Cyber Verification Program.
This seems reasonable to me. The legit security firms won't have a problem doing this, just like other vendors (like Apple, who can give you special iOS builds for security analysis).
If anyone has a better idea on how to _pragmatically_ do this, I'm all ears.
Yeah no. They can fuck right off with KYC humiliation rituals.
Claude code had safeguards like that hardcoded into the software. You could see it if you intercept the prompts with a proxy
It appears we're learning the hard way that we can't rely on capabilities of models that aren't open weights. These can be taken from us at any time, so expect it to get much worse..
Can't wait for a random chinese company to train a model on Mythos by breaking Anthropic's ToS just to release it for free and with open weights.
How should one compare benchmark results? For example, SWE-bench Pro improved ~11% compared with Opus 4.6. Should one interpret it as 4.7 is able to solve more difficult problems? or 11% less hallucinations?
There is no hallucination benchmark currently.
I was researching how to predict hallucinations using the literature (fastowski et al, 2025) (cecere et al, 2025) and the general-ish situation is that there are ways to introspect model certainty levels by probing it from the outside to get the same certainty metric that you _would_ have gotten if the model was trained as a bayesian model, ie, it knows what it knows and it knows what it doesn't know.
This significantly improves claim-level false-positive rates (which is measured with the AUARC metric, ie, abstention rates; ie have the model shut up when it is actually uncertain).
This would be great to include as a metric in benchmarks because right now the benchmark just says "it solves x% of benchmarks", whereas the real question real-world developers care about is "it solves x% of benchmarks *reliably*" AND "It creates false positives on y% of the time".
So the answer to your question, we don't know. It might be a cherry picked result, it might be fewer hallucinations (better metacognition) it might be capability to solve more difficult problems (better intelligence).
The benchmarks don't make this explicit.
Benchmarks are meaningless. Try it on your own problems and see if it has improved for what you want to use it for.
Benchmark results don’t directly translate to actual real world improvement. So we might guess it’s somewhat better but hard to say exactly in what way
11% further along the particular bell curve of SWE-bench. Not really easy to extrapolate to real world, especially given that eg the Chinese models tend to heavily train on the benchmarks. But a 10% bump with the same model should equate to “feels noticeably smarter”.
A more quantifiable eval would be METR’s task time - it’s the duration of tasks that the model can complete on average 50% of the time, we’ll have to wait to see where 4.7 lands on this one.
Install the latest claude code to use opus 4.7:
`claude install latest`
Honestly I've been doing a lot of image-related work recently and the biggest thing here for me is the 3x higher resolution images which can be submitted. This is huge for anyone working with graphs, scientific photographs, etc. The accuracy on a simple automated photograph processing pipeline I recently implemented with Opus 4.6 was about 40% which I was surprised at (simple OCR and recognition of basic features). It'll be interesting to see if 4.7 does much better.
I wonder if general purpose multimodal LLMs are beginning to eat the lunch of specific computer vision models - they are certainly easier to use.
Did you try the same with gemini 3 models? Those usually score higher on vision benchmarks
Interesting to see the benchmark numbers, though at this point I find these incremental seeming updates hard to interpret into capability increases for me beyond just "it might be somewhat better".
Maybe I've skimmed too quickly and missed it, but does calling it 4.7 instead of 5 imply that it's the same as 4.6, just trained with further refined data/fine tuned to adapt the 4.6 weights to the new tokenizer etc?
With the new tokenizer did they A/B test this one?
I'm curious if that might be responsible for some of the regressions in the last month. I've been getting feedback requests on almost every session lately, but wasn't sure if that was because of the large amount of negative feedback online.
The benchmarks of Opus 4.6 they compare to MUST be retaken the day of the new model release. If it was nerfed we need to know how much.
https://marginlab.ai/trackers/claude-code-historical-perform...
Looks completely broken on AWS Bedrock
"errorCode": "InternalServerException", "errorMessage": "The system encountered an unexpected error during processing. Try your request again.",
I get this error too and if I try again: { ... "error":{"type":"permission_error","message":"anthropic.claude-opus-4-7 is not available for this account. You can explore other available models on Amazon Bedrock. For additional access options, contact AWS Sales at https://aws.amazon.com/contact-us/sales-support/"}}
Is it just Opus 4.6 with throttling removed?
Interesting that the MCP-Atlas score for 4.6 jumped to 75.8% compared to 59.5% https://www.anthropic.com/news/claude-opus-4-6
There's other small single digit differences, but I doubt that the benchmark is that unreliable...?
page is updated to state:
MCP-Atlas: The Opus 4.6 score has been updated to reflect revised grading methodology from Scale AI.
Tried it, after about 10 messages, Opus 4.7 ceased to be able to recall conversation beyond the initial 10 messages. Super weird.
> Opus 4.7 is a direct upgrade to Opus 4.6, but two changes are worth planning for because they affect token usage. First, Opus 4.7 uses an updated tokenizer that improves how the model processes text. The tradeoff is that the same input can map to more tokens—roughly 1.0–1.35× depending on the content type. Second, Opus 4.7 thinks more at higher effort levels, particularly on later turns in agentic settings. This improves its reliability on hard problems, but it does mean it produces more output tokens.
This is concerning & tone-deaf especially given their recent change to move Enterprise customers from $xxx/user/month plans to the $20/mo + incremental usage.
IMO the pursuit of ultraintelligence is going to hurt Anthropic, and a Sonnet 5 release that could hit near-Opus 4.6 level intelligence at a lower cost would be received much more favorably. They were already getting extreme push-back on the CC token counting and billing changes made over the past quarter.
Qwen 3.6 OSS and now this, almost feels like Anthropic rushed a release to steal hype away from Qwen
How powerful will Opus become before they decide to not release it publicly like Mythos?
They are planning to release a Mythos-class model (from the initial announcement), but they won't until they can trust their safeguards + the software ecosystem has been sufficiently patched.
It seems they nerf it, then release a new version with previous power. So they can do this forever without actually making another step function model release.
Interesting that despite Anthropic billing it at the same rate as Opus 4.6, GitHub CoPilot bills it at 7.5x rather than 3x.
Seems they jumped the gun releasing this without a claude code update?
https://news.ycombinator.com/item?id=47794516
claude-opus-4-7
Claude Code hasn't updated yet it seems, but I was able to test it using `claude --model claude-opus-4-7`
Or `/model claude-opus-4-7` from an existing session
edit: `/model claude-opus-4-7[1m]` to select the 1m context window version
~~That just changes it to Opus 4, not Opus 4.7~~
My statusline showed _Opus 4_, but it did indeed accept this line.
I did change it to `/model claude-opus-4-7[1m]`, because it would pick the non-1M context model instead.
Oh good call
Does it run for you? I can select it this way but it says 'There's an issue with the selected model (claude-opus-4-7). It may not exist or you may not have access to it. Run /model to pick a different model.'
Weird, yeah it works for me
API Error: 400 {"type":"error","error":{"type":"invalid_request_error","message":"\"thinking.type.enabled\" is not supported for this model. Use \"thinking.type.adaptive\" and \"output_config.effort\" to control thinking behavior."},"request_id":"req_011Ca7enRv4CPAEqrigcRNvd"}
Eep. AFAIK the issues most people have been complaining about with Opus 4.6 recently is due to adaptive thinking. Looks like that is not only sticking around but mandatory for this newer model.
edit: I still can't get it to work. Opus 4.6 can't even figure out what is wrong with my config. Speaking of which, claude configuration is so confusing there are .claude/ (in project) setting.json + a settings.local.json file, then a global ~/.claude/ dir with the same configuration files. None of them have anything defined for adaptive thinking or thinking type enable. None of these strings exist on my machine. Running latest version, 2.1.110
"Agentic Coding/Terminal/Search/Analysis/Etc"...
False: Anthropic products cannot be used with agents.
four prompts with opus 4.6 today is equivalent to 30 or 40 two months ago. infernal downgrade in my case.
Excited to start using from within Cursor.
Those Mythos Preview numbers look pretty mouthwatering.
Seems like it's not in Claude Code natively yet, but you can do an explicit `/model claude-opus-4-7` and it works.
Claude Code doesn't seem to have updated yet, but I was able to try it out by running `claude --model claude-opus-4-7`
/model claude-opus-4-7[1m]
Backlash on HN for Anthropic adjusting usage limits is insane. There's almost no discussion about the model, just people complaining about their subscription.
Who cares about a new model you can’t even use?
Even using Mythos with their own benchmarks as a comparison that isn't available for most people to use, what a joke.
True but I guess their primary customers are businesses not individual devs. Maybe Mythos is more affordable for them
someone tell me if i should be happy
Did you try asking the model?
Well this explains the outages over the last few days
Excited to use 1 prompt and have my whole 5-hour window at 100%. They can keep releasing new ones but if they don't solve their whole token shrinkage and gaslighting it is not gonna be interesting to se.
It seems a lot of the problem isn't "token shrinkage" (reducing plan limits), but rather changes they made to prompt caching - things that used to be cached for 1 hour now only being cached for 5 min.
Coding agents rely on prompt caching to avoid burning through tokens - they go to lengths to try to keep context/prompt prefixes constant (arranging non-changing stuff like tool definitions and file content first, variable stuff like new instructions following that) so that prompt caching gets used.
This change to a new tokenizer that generates up to 35% more tokens for the same text input is wild - going to really increase token usage for large text inputs like code.
on Tuesday, with 4.6, I waited for my 5 hour window to reset, asked it to resume, and it burned up all my tokens for the next 5 hour window and ran for less than 10 seconds. I’ve never cancelled a subscription so fast.
I tried the Claude Extension for VSCode on WSL for a reverse engineering task, it consumed all of my tokens, broke and didn't even save the conversatioon
That’s truly awful. What a broken tool.
Solve? You solve a problem, not something you introduced on purpose.
Pretty bad. As nerfed 4.6
They're now hiding thinking traces. Wtf Anthropic.
This is the first new model from Anthropic in a while that I'm not super enthused about. Not because of the model, I literally haven't opened the page about it, I can already guess what it says ("Bigger, better, faster, stronger"), but because of the company.
I have enjoyed using Claude Code quite a bit in the past but that has been waning as of late and the constant reports of nerfed models coupled with Anthropic not being forthcoming about what usage is allowed on subscriptions [0] really leaves a bad taste in my mouth. I'll probably give them another month but I'm going to start looking into alternatives, even PayG alternatives.
[0] Please don't @ me, I've read every comment about how it _is clear_ as a response to other similar comments I've made. Every. Single. One. of those comments is wrong or completely misses the point. To head those off let me be clear:
Anthropic does not at all make clear what types of `claude -p` or AgentSDK usage is allowed to be used with your subscription. That's all I care about. What am I allowed to use on my subscription. The docs are confusing, their public-facing people give contradictory information, and people commenting state, with complete confidence, completely wrong things.
I greatly dislike the Chilling Effect I feel when using something I'm paying quite a bit (for me) of money for. I don't like the constant state of unease and being unsure if something might be crossing the line. There are ideas/side-projects I'm interested in pursuing but don't because I don't want my account banned for crossing a line I didn't know existed. Especially since there appears to be zero recourse if that happens.
I want to be crystal clear: I am not saying the subscription should be a free-for-all, "do whatever you want", I want clear lines drawn. I increasingly feeling like I'm not going to get this and so while historically I've prefered Claude over ChatGPT, I'm considering going to Codex (or more likely, OpenCode) due to fewer restrictions and clearer rules on what's is and is not allowed. I'd also be ok with kind of warning so that it's not all or nothing. I greatly appreciate what Anthropic did (finally) w.r.t. OpenClaw (which I don't use) and the balance they struck there. I just wish they'd take that further.
Regardless of the model quality improvement, the corporate damage was done by not only ignoring the Opus quality degradation but gaslighting users into thinking they aren’t using it right.
I switched to Codex 5.4 xhigh fast and found it to be as good as the old Claude. So I’ll keep using that as my daily driver and only assess 4.7 on my personal projects when I have time.
amazing speed...
Is that time to turning back from Codex to Claude Code?
Codex release coming today: https://x.com/thsottiaux/status/2044803491332526287
It’s funny, a few months ago I would have been pretty excited about this. But I honestly don’t really care because I can’t trust Anthropic to not play games with this over the next month post release.
I just flat out don’t trust them. They’ve shown more than enough that they change things without telling users.
> during its training we experimented with efforts to differentially reduce these capabilities
> We are releasing Opus 4.7 with safeguards that automatically detect and block requests that indicate prohibited or high-risk cybersecurity uses.
Ah f... you!
Might be sticking with 4.6 it's only been 20 minutes of using 4.7 and there are annoyances I didn't face with 4.6 what the heck. Huge downgrade on MRCR too....
256K:
- Opus 4.6: 91.9% - Opus 4.7: 59.2%
1M:
- Opus 4.6: 78.3% - Opus 4.7: 32.2%
its a pretty good coding model - using it in cursor now.
is this just mythos flex?
Here’s the problem. The distribution of query difficulty / task complexity is probably heavily right-skewed which drives up the average cost dramatically. The logical thing for anthropic to do, in order to keep costs under control, is to throttle high-cost queries. Claude can only approximate the true token cost of a given query prior to execution. That means anything near the top percentile will need to get throttled as well.
By definition this means that you’re going to get subpar results for difficult queries. Anything too complicated will get a lightweight model response to save on capacity. Or an outright refusal which is also becoming more common.
New models are meaningless in this context because by definition the most impressive examples from the marketing material will not be consistently reproducible by users. The more users who try to get these fantastically complex outputs the more those outputs get throttled.
Where is chatgpt answer to this?
Gemini and Codex already scored higher on benchmarks than Opus 4.6 and they recently added a $100 tier with limited 2x limits, that's their answer and it seems people have caught on.
just started using codex. claude is just marketing machine and benchmaxxing and only if you pay gazillion and show your ID you can use their dangerous model.
All fine, where is pelican on bicycle?
Getting a little suspicious that we might not actually get AGI.
Dude we dont even have GI
even sonnet right now has degraded for me to the point of like ChatGPT 3.5 back then. took ~5 hours on getting a playwright e2e test fixed that waited on a wrong css selector. literlly, dumb as fuck. and it had been better than opus for the last week or so still... did roughly comparable work for the last 2 weeks and it all went increasingly worse - taking more and more thinking tokens circling around nonsense and just not doing 1 line changes that a junior dev would see on the spot. Too used to vibing now to do it by hand (yeah i know) so I kept watching and meanwhile discovered that codex just fleshed out a nontrivial app with correct financial data flows in the same time without any fuzz. I really don't get why antrhopic is dropping their edge so hard now recently, in my head they might aim for increasing hype leading to the IPO, not disappointment crashes from their power user base.
You are operating purely on vibes, https://marginlab.ai/trackers/claude-code-historical-perform...
not rejecting reality, but increasing doubts about the effectiveness of these tests. and yes its subjective n=1, but I literally create and ship projects for many months now always from the same github template repository forked and essentially do the same steps with a few differnt brand touches and nearly muscle memory prompting to do the just right next steps mechanically over and over again, and the amount of things getting done per step gots worse and the quality degraded too, forgetting basic things along the way a few prompts in. as I said n=1 but the very repetitive nature of my current work days alwyas doing a new thing from the exact same start point that hasn't changed in half a year is kind of my personal benchmark. YMMV but on my end the effects are real, specifically when tracking hours over this stuff.
> First, Opus 4.7 uses an updated tokenizer that improves how the model processes text
wow can I see it and run it locally please? Making API calls to check token counts is retarded.
Excited to start using this!
Reminder that 4.7 may seem like a huge upgrade to 4.6 because they nerfed the F out of 4.6 ahead of this launch so 4.7 would seem like a remarkable improvement...
I wonder if this one will be able to stop putting my fucking python imports inline LIKE I'VE TOLD IT A THOUSAND TIMES.
Sigh here we go again, model release day is always the worst day of the quarter for me. I always get a lovely anxiety attack and have to avoid all parts of the internet for a few days :/
I feel this way too. Wish I could fully understand the 'why'. I know all of the usual arguments, but nothing seems to fully capture it for me - maybe it' all of them, maybe it's simply the pace of change and having to adapt quicker than we're comfortable with. Anyway best of luck from someone who understands this sentiment.
Really? I think it's pretty straightforward, at least for me - fear of AI replacing my profession and also fear that it will become harder to succeed with a side project.
Yeah I can understand that, and sure this is part of it, just not all of it. There is also broader societal issues (ie. inequality), personal questions around meaning and purpose, and a sprinkling of existential (but not much). I suspect anyone surveyed would have a different formula for what causes this unease - I struggle to define it (yet think about it constantly), hence my comment above.
Ultimately when I think deeper, none of this would worry me if these changes occurred over 20 years - societies and cultures change and are constantly in flux, and that includes jobs and what people value. It's the rate of change and inability to adapt quick enough which overwhelms me.
I have some of those too, to a limited extent.
Not worried about inequality, at least not in the sense that AI would increase it, I'm expecting the opposite. Being intelligent will become less valuable than today, which will make the world more equal, but it may be not be a net positive change for everybody.
Regarding meaning and purpose, I have some worries here too, but can easily imagine a ton of things to do and enjoy in a post-AGI world. Travelling, watching technological progress, playing amazing games.
Maybe the unidentified cause of unease is simply the expectation that the world is going to change and we don't know how and have no control over it. It will just happen and we can only hope that the changes will be positive.
> fear of AI replacing my profession
See i don't have any of this fear, I have 0 concerns that LLMs will replace software engineering because the bulk of the work we do (not code) is not at risk.
My worries are almost purely personal.
Thank you thank you, misery loves company lol! I haven't fully pinned down what the exact cause is as well, an ongoing journey.
Why? Good anxiety or bad?
> indeed, during its training we experimented with efforts to differentially reduce these capabilities
can't wait for the chinese models to make arrogant silicon valley irrelevant
We all know this is actually Mythos but called Opus 4.7 to avoid disappointments, right?
New model - that explains why for the past week/two weeks I had this feeling of 4.6 being much less "intelligent". I hope this is only some kind of paranoia and we (and investors) are not being played by the big corp. /s
I don't get it. Why would they make the previous model worse before releasing an update?
Why do stores increase prices before a sale?
Ok, so the answer is "they make the existing model worse to make it seem that the new model is good". I'm almost certain that this is not what's going on. It's hard to make the argument that the benefits outweigh the drawbacks of such approach. It doesn't give the more market share or revenue.
TL;DR; iPhone is getting better every year
The surprise: agentic search is significantly weaker somehow hmm...
TL;DR; iPhone is getting better every year
The surprise: agentic search is significantly weaker somehow hmm...
Introducing a new upgraded slot machine named "Claude Opus" in the Anthropic casino.
You are in for a treat this time: It is the same price as the last one [0] (if you are using the API.)
But it is slightly less capable than the other slot machine named 'Mythos' the one which everyone wants to play around with. [1]
[0] https://claude.com/pricing#api
[1] https://www.anthropic.com/news/claude-opus-4-7
If you're building a standard app Opus is already good enough to build anything you want. I don't even know what you'd really need Mythos for.
You'd be surprised. With React, Claude can get twisted in knots mostly because React lends itself to a pile of spaghetti code.
What's an alternative library that doesn't turn large/complex frontend code into spaghetti code?
Vue (my favorite) and Svelte do well.
I've got a gfx device crash that only happens on switch. Not Xbox, ps4, steam, epic, or anything. Only switch.
Opus hasn't been able to fix it. I haven't been able to fix it. Maybe mythos can idk, but I'll be surprised.
This is true if you know what you are doing and provide proper guidance. It’s not true if you just want to vibe the whole app.
You'd need Mythos to free your iPhone, SamsungTV, SmartWatches or such. Maybe even printer drivers.
i sincerely doubt mythos is capable of jailbreaking an iphone
Consumerism... if it ain't the best, some people don't want it.
Time/frustration
If it’s all slop, the smallest waste of time comes from the best thing on the market
Also 640 KB ram ought to be enough for everybody.
It seems like we're hitting a solid plateau of LLM performance with only slight changes each generation. The jumps between versions are getting smaller. When will the AI bubble pop?
SWE-bench pro is ~20% higher than the previous .1 generation which was released 2 months ago. For their SWE benchmark, the token consumption iso-performance is down 2x from the model they released 2 months ago.
If this is a plateau I struggle to imagine what you consider fast progress.
Your comment doesn't make any sense, opus 4.6 was release two months ago, what jump would you expect?
The generations are two months apart now though…
Every night praying for tomorrow
> In Claude Code, we’ve raised the default effort level to xhigh for all plans.
Does it also mean faster to getting our of credits?