I think this is the first time we've had a third minor version bump on a frontier Anthropic model. (I count the 0.5s as major here, because they've been issued non-sequentially and also corresponded to massive capability leaps, eg, Sonnet 3.5, Opus 4.5).
So now the Opus 4.5 family has successors 4.6, 4.7, and 4.8, each posting fairly modest claimed gains. My own experience w/ 4.6 and 4.7 are that I don't firmly grasp any capabilities improvements over my memory of 4.5, but it's all so fuzzy that it's truly difficult to tell.
Maybe my own tastes are saturated now (it's smarter than me?) and I'll never again perceive model progress. Maybe the incrementalism is such that I'd notice immediately if my 4.7 workflows were redirected now to 4.5.
Difficult spot for the labs to be in because, if they have a stronger product, I'd prefer they release it and that I can use it.
But as this dynamic continues, the improvements are going to be less and less legible for end-users, who will complain about the churn-without-payoff, even when the payoff may actually be real.
I won't be surprised if the next gen frontier models are the last.
There's orders of magnitude of low hanging juice to squeeze out of smaller models.
It is almost guaranteed that a 60-90B model can outperform current SOTA in coding tasks within 2-3 years (design not certain, probably unlikely).
It is far less clear that a 1.2T model will be meaningfully better enough to justify training it.
As far as reasoning is concerned, with the recent GRAM release, there may be 4 orders of magnitude of reasoning to tack on to smaller models.
Think about that... Google, OpenAI, Anthropic could train a 30B GRAM-based model in days - and it could potentially have better local reasoning than the best model available today at >1T params... They could upgrade that to a ~600B MoE model in days to have general trivia knowledge rivaling the best models...
You just can't train a 1T+ parameter model that fast. It is a giant if how much GRAM turns out to improve things, but it's unlikely to be trivial or nothing.
Larger models can already sort of tell you anything. They're never going to get everything right unless they stop being LLMs.
There's just not a lot of juice left to squeeze for Gemini to tell you exactly how tall Ke$ha is or when the last time Brittney Spears went to jail was...
>It is almost guaranteed that a 60-90B model can outperform current SOTA in coding tasks within 2-3 years.
I don't disagree, but how much of this ends up being distillation? I can't help but imagine that 4.8 was probably trained in part by leveraging Mythos.
If the very large models turn out to be very expensive to run relative to the benefits, it's possible that they could end up still being trained, but ultimately used as a tool to create smaller models that are nearly as effective.
I'm curious if someone here with a stronger background in the space has a similar intuition or not.
> I don't disagree, but how much of this ends up being distillation?
You don't need distillation. They already have the training sets.
It's MLA + MoE + Medusa (a better version of Speculative Decoding) + 1.58b (possibly - maybe nothing) + GRAM (which will almost certainly not turn out to be a nothing burger, but no one has quickly turned this around yet to prove it).
It wouldn't be data distillation: instead, it would be teacher-student distillation. The teacher model has stronger representations that the student can mimic, which would give it more capability over training on the data itself.
Frontier labs have their own variants of MLA and certainly their own balance/scaling-laws for things like MoE vs FC vs Attn. MoE scales really well for inference with horizontal scaling + batching, which these guys luv.
On the architectures side, I'm a lot more interesting in attention residuals than anything else, one of those things that seems obvious in hindsight and Kimi have proven it at scale.
Same with speculative decoding... They all do something, but there are known techniques that are substantially better - that just were't known when they started development of the previous models.
> I don't disagree, but how much of this ends up being distillation?
A lot, so you can bet tens of millions are flowing to congress to have distillation declared illegal before this happens. And then it'll happen anyway.
A lab can train a large model, and then distill a smaller model from it that retains the majority of the useful capbility.
I don't know well enough if there's any benefit of that over just training the smaller model directly, but I'll bet there are some times where that is useful. I could easily see it being easier to do the initial pre-training on a larger model but be able to distill everything useful down into a smaller model, essentially filtering out a lot of noise in the process.
There used to be training methods like that but I think they've been phased out in favor of letting small models evolve by rewriting their own training material. Surprisingly that's actually cheaper.
I looked into this "GRAM" stuff a sibling comment links further to, and just to say:
- this gets reinvented/rediscovered constantly under different names
- it cant be trained very well (right now, will change)
- massive theoretical improvements over current models (log_2(vocabsize)=17, residual stream dim is thousands of dimensions, recursivity means more information bandwidth by ~3 OoM)
- BUT it cant be interpreted or aligned <- this is why no one uses it and no one talks about it. the idea is 100% obvious to all the frontier labs and there is a good reason why it isn't used
I follow this stuff closely, I think I know what I'm talking about
1. Context is all you need... They are heavily investing in getting better context (especially for coding tasks). This will disproportionately advantage smaller models (and benefit everyone).
A smaller model with better context today can outperform a model with 100x more parameters with bad or diluted context.
2. MoE (already abundant) + MLA (mostly memory efficiency, not quality) + Medusa (speed, not quality) + GRAM (5000-10,000x better reasoning in an extremely small model) + 1.58b (unclear if it will have the impact Microsoft first claimed - but possibly 5x).
Have you personally used any of the latest batch of even smaller local models? They certainly don't beat SotA models at coding... but with a good harness they are able to achieve things with SotA that I couldn't last year.
I've repeatedly given local models non-trivial projects that involve research and coding which they've successfully completed with minimal intervention from me (almost exclusively in the domain of reviewing the results). Again, nothing comparable with current SotA, but definitely tasks I could not have given SotA models last year (without agent harness).
Now that pure progress from these models seems to have slowed down, we're seeing a ton of options for both making models more efficient and other tools that help improve them (everything from agent harnesses to RLVR).
That's just looking at "what can small do today", when you look at what's possible with larger open models that are still much smaller than SotA from the major providers, their performance is extremely close to SotA, enough that for personal projects I'll just use Kimi instead of any anthropic offerings.
So it's not terribly hard to image a solution in the middle happening within a few years. We still have tons to learn about optimal sizes of these models and how to build them with maximal efficiency (and we've already seen a lot of recent improvements in this space).
> but with a good harness they are able to achieve things with SotA that I couldn't last year.
What happens if you run last years model in a SOTA harness? IME, the quality of the harness has a much more significant impact on the quality of the result, once you get past the initial hump of “can it do anything at all”
I think you are assuming training from scratch, which I doubt is happening here. Fine-tuning and RL, especially based on synthetic feedback (coding skill, in particular) can be ongoing and is where these models obtain truly useful abilities.
What gp wanted to say is that models are now so smart and useful that even if they managed to be EVEN MORE smart and useful, you wouldn't even notice it.
Honestly, there is nothing in my head that Claude cannot handle. Maybe it can be more this or that but I can already barely exploit Opus 4.7.
And I'm using DeepSeek 4 Pro for my personal use and while it's a little behind, it's not that far.
I think the situation can be very dangerous for US AI companies because if current models are already capable of doing mostly anything, nobodoy will want to get to the next model, even if it's 10x better. OTOH, open source models like DeepSeek are doing mostly the same work for 1/10 of the price.
Also the more I play with Pi, the more I think LLMs are already not kept back by their own capabilities but by the lack of agency we allow them to have. There is more value today in a capable harness for current LLMs than in a better LLM.
you just need to look at Mythos to see the jump in performance from a 10T(?) model. As they scale, they get more capable. We might have an yearly release, but I believe the releases will continue, as long as scaling laws are in tact, and there's huge problems still need solving. (think cancer)
You forget that these models are still only interpolating between human-generated datapoints fed to them. They cannot reason beyond the data they've been given, so unless everything you want to create with AI is a synthesis of prior art, you're back to relying on the stone-age human brain that created AI in the first place.
Not all training data is human generated, and it's also not clear that being ridiculously good at interpolating between data points (whatever that means) will not lead to superhuman capabilities.
Ive seen the tickets generated by the model that have trickled to my team. They are legitimate, but i can’t speak to model improvement because its a pilot program.
My conspiracy theory is that Apple recognizes this. Their goal is a competent local model on everyone's apple device that does what 80-90% of what people use AI for: Searching for basic information, some data transformation, a little bit of photo editing, vibe coding a small utility, etc.
The SOTA models then can only cater to engineers, scientists and mathematicians, physicians, etc. I'm not sure how they would price them reasonably for what amounts to a niche market.
OpenAI and Anthropic need AI to be a consumer product on the level of the iPhone or a Nintendo Switch.
That does seem to be the path Apple is following here. Have a local model that can answer most things and then have a fallback of cloud options when they request is too complex. The cleverness of this strategy has been overshadowed by the incredibly poor quality of their local models. It will be extremely interesting to see what next month holds and whether Google helped fine tune an Apple specific Gemini / Gemma model for their devices. Bonus points, of course, if they unveil the M5 Ultra Studio with half a terabyte of RAM to be a local "cloud model" (the true fantasy here of course would be Apple building something a little like openclaw where from your phone you could give commands to your Home Apple server). They could probably get away with charging $20k for it if it has sufficient tok/sec. If that happens and is successful one could imagine a straight line path in the next two generations to bringing the cost and form factor down to the point where some of the form factor of an Apple TV becomes everybody's home inference server / agentic HQ. Sovereign AI for everyone!
Let's hope that hitting a scaling wall and less money to spend will begin redirecting efforts to optimize inference and get the same results with less compute.
Boomer comparison, but I remember the 8 bit computer era when the hardware was what it was so the later games of that era used hardware better than previous ones.
There's still several orders of magnitude of improvement that are almost certainly left - it's just not clear how much is left on the frontier end.
Most people will be very glad to pay Anthropic, OpenAI, Google etc $200 a month to get things done 20x faster than they could IF they had a $8000 MacBook and could theoretically do it locally.
Some people would pay $200 a month forever not to have to open the terminal one time...
"Doing things X times faster" at some point hits Amdahl law. If just context switching takes 5 minutes, speeding up a 1 hour task by 10x provides 5x improvement.
Furthermore, if looking at the results takes 10 minutes, that same 1 hour task only sees a 3x improvement. And so on.
While revenues grow almost exponentially. Reminds me of the confident predictions in the early days of Covid that it was nothing while the data showed exponential growth.
Anyone predicting doom needs to predict when revenues will stop growing exponentially or needs to specifically say what the gross margins are. Put some numbers to it so you can he held accountable when you turn out wrong.
> I won't be surprised if the next gen frontier models are the last.
I’d be surprised tbh. Investors don’t want to hear “everyone else is still training models and seeing improvements, but we don’t want to participate in the arms race anymore.” They want monumental leaps every quarter or two because they have sunk unholy amounts of money into these companies/products.
The whole idea of “hyper scale” doesn’t jive with caution and or otherwise slowing down.
> It is almost guaranteed that a 60-90B model can outperform current SOTA in coding tasks within 2-3 years.
I am ready to bet against this. Knowledge benchmark like SimpleQA isn't increasing for small models.
> It is far less clear that a 1.2T model will be meaningfully better enough to justify training it.
Well for one, we know for certain there is Mythos which is meaningfully better. And I think there is a lot of juice left to squeeze for Mythos class model.
Knowledge benchmarks can't really be improved upon via distillation or RL. It requires those facts be added to the training corpus and for the model to memorize them better. Neither distillation or RL really do that and thus we shouldn't expect improvements on SimpleQA unless some other interventions are being made.
Model intelligence and knowledge aren't necessarily directly related. If we can pack greater intelligence and agency at the cost of it forgetting factoids, that would actually be a good thing. We don't need LLMs to memorize facts, we need them to learn how to interact with the world such that they can find the facts that are necessary and surface them to the user.
If we could distill all of the knowledge out of an LLM and just be left with a very agentic model that only knows facts in it's context, I think some very interesting stuff would happen.
It seems like a lot of things fed into that. Anthropic couldn't keep up with the compute costs when they got a huge influx of users. (So) effort level defaults got turned down. (Looks like we have direct effort control in the web interface now - thrilled about that!) Adaptive Thinking, while usually cheaper for them, seems less robust than Extended Thinking. And this part is just vibes, but the alignment on 4.7 feels too stiff. I understand wanting the model to push back more, but it seems like 4.7 will push back reflexively in situations where it's just odd.
Too much personality, if you ask me. My biggest use case of an LLM is tool, not therapy, but therapy and opinions have been sneaking into workhorse tasks.
haven't verified, but attributed to Askell:
"I just think that... there's this idea that you're always giving the models a personality and a persona, because they are talking like people and they are trained on human data. And I think my worry has been: if you train them to be excessively corrigible and to see that as their persona, in people I think this actually has a lot of negative broader traits. As in, if you met someone and it was just like, "oh yeah, they would literally do anything," a follower — you know, if a person just tells them something and they just fully defer, they don't bother thinking about it at all — I'm just a bit worried about how that might end up generalizing, especially if models are going to be playing a more active role in the world."
Anthropic’s research makes the case that role-playing is inherent to how the models work. Communication implies a sender. Language implies a writer, and the models learn these roles implicitly during training. RLHF is meant to strengthen the attractor to the Assistant persona.
4.7 is a different base model from 4.6, so it's possible that they introduced regressions with pre-training changes, or undercooked the post-training stage.
I'm curious to poll HN on this issue. Do you feel like we've had meaningful/noticeable gains in terms of your programming workflows between 4.5 and 4.7?
My 2¢, I personally feel like all of the productivity gains since 4.5's release (in November 2025!!) have come from improvements to the harnesses (cc, cursor cli, codex, opencode, whatever) AND from the context window expansion from 200k to 1M.
But the actual "raw" intelligence of the model / ability to make good decisions feels like it has plateaued since 4.5. 4.6 was maybe a small improvement, but hard to differentiate from in-context-learning with the 1M window. 4.7 if anything felt like a regression in wisdom for me and my coworkers, with it consistently making worse/lazier decisions.
For long-running tasks, yes 4.7 has been a noticeable improvement. Goes off the rails alot less than 4.6 does. For shorter-sized windows, I havent felt as much and agree that the harness improvements have been fhe biggest lever
To me 4.5 was mindblow, 4.6 noticeable, 4.7 more like a style/personality change regarding how much it asks back, how much it assumes, how eager it is to jump to action etc but not really in terms of my perception of its smartness.
They all feel, more or less, the same to me in terms of output capabilities. Mostly get simple things right, can get more complex things right with nudging, eventually get stuck hard on something that takes a bunch of iterations through it/logging/etc or me fixing the code manually.
I actually don't see any personal productivity improvements from using opus over sonnet for coding. If you're keeping tasks small and conversations short, reading the code and correcting before changes go in, whatever advantages opus has aren't practically significant. It's also just talky as hell, overexplains anything it touches and every token produced this way increases the surface area for hallucination so you need to have your guard up even more with it.
There's a sweet spot of complexity for low importance tasks where it's just big enough I don't want to do it and just simple enough to have opus plan/delegate/review with another model. So possibly model improvements will grow this window, but currently I don't do much in there.
4.5/4.6 were roughly the same in our testing. Opus 4.7 is smarter, but it's difficult to use as a product for various personality issues. So far, Opus 4.8 seems to be going down that path (unusably slow, but this could be a launch day rollout problem). Full Opus 4.8 tests are in progress now.
It might be saturated for smaller scopes of work, but it’s not hard to see the cracks when you scale up what you ask of SOTA models/agents.
One example, to try and single shot prompt coding a ChatGPT equivalent chatbot.
Sure it will spit something out, but the feature depth, UX subtitles, backend integration, and lots of pragmatic engineering decisions along the way will just not be baked.
Another example is building a C compiler from scratch which Anthropic showed is still a struggle to do.
Not that these these specific examples are important but just to point out scaling up expectations shows the cracks.
It’s not just a model problem of course, better agents, orchestration features (like Dynamic Workflows mentioned in the post), all need to continue to evolve.
Ar what point does my CS degree become totally useless is an open question.
> My own experience w/ 4.6 and 4.7 are that I don't firmly grasp any capabilities improvements over my memory of 4.5, but it's all so fuzzy that it's truly difficult to tell.
I've actually intentionally switched back to 4.5. I hated 4.7 so much that I decided to jump back all the way to 4.5.
Now that I've been using 4.5 for a few weeks, I find it significantly more reliable but a bit more forgetful than 4.6/4.7. I'm okay with that because it's really easy to identify this forgetfulness and nudge it.
I found 4.7's adaptive thinking to be extremely unreliable. It seems to overcorrect on the current message without considering the difficult of the overall problem. I wonder if 4.8 will improve on that.
I've been using Claude Code regularly since the 4.5 release, and 4.7 was a significant regression: very unreliable, arguing about changes, deciding that fixes weren't needed, etc.
I'm hoping they recreate the magic of 4.5 but it's as much about the quality of harness, the memory and efficiency of the tools than simply the models at this point.
4.7 was a significant jump in the ability to run long-horizon tasks. It immediately completed tasks that 4.6 was unable to, even though I have the impression that it became a bit less capable over the first few weeks after release.
It also seems to be helpless at effort levels < xhigh, I turn to Sonnet when simpler tasks are needed.
Maybe try making a simple randomize script to swap the three latest models. And see if you can tell which ones are meaningfully different without knowing which ones are flipped on or off?
Unless you're systematically repeating the exact same task, the most parsimonious explanation is that you're seeing natural variation based on different tasks, random sampling of tokens, etc.
The honesty will be noticeable. Maybe we'll see some honest assessments like "That is not possible within the laws of known physics", "Your legal argument is nonsensical and defies logic", "There is no evidence to support taking that will cure anything", etc., etc.
Given that 4.7 was a brand new model, trained from scratch with a unique architecture and tokenization scheme, I don't see the same pattern. It seems arbitrary.
It means for 4.7 they trained a new base model with different architecture, different pre-training data (later knowledge cutoff), and a new tokenizer.
Vs finetuning an existing model, which was the case for 4.6, and probably for 4.8.
Has meta stopped producing new models? I figured they were just regrouping after all the drama they’ve had recently. Meta’s massive user base means they don’t need to be involved in the customer acquisition rat race. Once they have a model they’re happy with they can have a billion people interacting with it within a month.
Kind of the beauty of it is that I don't have to to know I'm right. The reason I know is that you're alive so you can do the one thing it can't ever do, which is know when to stop or give up. It would turn me and everything else into the world into paperclips repeating the same research 1,000,000 times over.
"Users will find Opus 4.8 to be a modest but tangible improvement on its predecessor."
This is a refreshing attitude!
I've also verified that you can now turn off adaptive thinking in the web UI, which is great. I've had a lot of problems with thinking not triggering and the model producing sub-par output. Glad we can finally turn it off. (I hope being able to turn off adaptive thinking is new, if I could have turned it off at any time that would be embarrassing)
Well, I think the attitude is that costs are allowed to escalate faster and more steeply than the features delivered. From that perspective, semantic versioning is a handy tool for adjusting pricing strategies. IMHO, it (versioning) only makes sense for open-source projects, where you can clearly see the actual changes made with each version upgrade. Anything else is more than a little suspicious…
Awesome, thanks for posting because I think I hit a possibly-spurious bug in turning Adaptive off when I switched models (4.6 -> 4.8, extra). Tried again, works as intended (I hope).
More importantly for me, though, is how CC will respond to 4.6-"only" flags for thinking. For now, it doesn't seem to clobber my setup.
> Not only that, but we plan to release a new class of model with even higher intelligence than Opus. As part of Project Glasswing, a small number of organizations are currently using Claude Mythos Preview for cybersecurity work. Models of this capability level require stronger cyber safeguards before they can be generally released. We’re making swift progress on developing these safeguards and expect to be able to bring Mythos-class models to all our customers in the coming weeks.
Seems like they might be hinting that if you are not a billionaire or multi-billion dollar company you will just get a limited and nerfed Claude Code slash command /mythos-security-audit or something.
Hope this isn’t the case and that normal average Joe’s of the world don’t get policed out of access.
> you will just get a limited and nerfed Claude Code slash command /mythos-security-audit or something.
Unless it's so expensive that we can't realistically use it for anything, I wouldn't complain about getting at least that. I would also rather have the actual model, but that's a useful application of it (and I'm probably not going to afford using it for much more).
Isn't OpenAI's public flagship already beating Mythos on penetration testing? I get the impression Mythos is just valuation-juicing for IPO more than anything else.
The fact that they haven't released it yet suggests a cost/margins issue to me more than anything else. Short term, I'll probably keep using Antrhopic, but my long-term bet is that locally-served models win, if only because the quest for profitability will probably lead to intentionally-nerfed / enshittified frontier models.
At other vendors, ad placement within LLM responses is either coming or already here. Anthropic's handling of OpenClaw shows they're willing to engage in anti-competitive behavior, and the courts are not in a hurry to stop them. Why would I pay them $200 a month for such treatment when a $2K box does what I need locally?
More interesting than that to me is "we’re working on developing and releasing models that provide many of the same capabilities as Opus at a lower cost"
Sonnet and Haiku look real outclassed for the price with current Chinese competition.
No, the handlebar is wrong. The handle bar is rotating the frame instead of rotating the front wheel. The handle bar should be mounted on the same line as the front wheel is.
I find the most miraculous thing about 4.7 to be that the pelican is facing left, wonder why the right facing everything is so ubiquitous in these images.
Simon, is your pelican test really captures differences among models or should you at least try like 10 times or something to average the random effects
I've been meaning to do a "run 3 times and pick the best" version for quite a while, I should really pull the trigger on that one. Currently it's one-shot only.
My fav coding benchmark for frontier models is to build a simple RTS game in one file (js/html/css). Claude Code with Opus 4.8 in ultracode mode nailed it, the best result so far:
The prompt was: Create a simple but functional real time strategy (RTS) game similar to old WarCraft, StarCraft or Command & Conquer games. The player should be able to build buildings, create units, gather resources and should uncover the whole map. No AI or multiplayer needed. Use simple but nice-looking graphics. No sound. Implement everything in HTML/CSS/JS, everything in a single file (you can use 3rd-party js or css libraries/frameworks via CDN).
Does anyone troll these releases and cherry pick random metrics other companies would cherry pick to show how amazing their models are?
There's like 8 million benchmarks. Every release, every model randomly picks 5-10 where they win in everything except 1, to make it look like they aren't randomly cherry picking benchmarks they probably benchmaxxed for.
https://arena.ai/leaderboard - I’ve found this company is a pretty good ranker - not sure their exact methodology but during day to day programming with Claude / gpt models I’ve felt qualitatively what they report
Also check mine[0], basically random private tests/questions and an ok-ish methodology, testing mostly for general intelligence than coding-specific tasks.
I built it for myself, to test which models to use via OpenRouter for my n8n agents. Currently actually still using gpt-5.3-codex for many things, as its pricing is really good in production (due to how their token caching works).
Gemini models still have the best intelligence (when asked any questions, most likely to get it right), but in production they still have many failure modes[1].
I'm finding it a little hard to believe that GPT 5.5 is in 11th place for webdev, outranked by models like Kimi, Qwen, and Z.ai. I'm not saying it's not true (I have noticed GPT being less smart in recent weeks), but this is very different from my expectation.
It's interesting they only included 6 metrics this time. Opus 4.7 had 12, and 4.6 had 13.
Of the metircs they reported for 4.7, for 4.8 they excluded BrowseComp, CharXiv Reasoning, CyberGym, GPQA Diamond, MCP Atlas, MMMLU, SWE-bench Verified. The last 4 were almost always mentioned in previous Opus releases.
I would take all benchmarks with a grain of salt. I don't really use them. What's it supposed to tell me? "5% smarter", what does that mean? My experience will differ. Just try it!
I doubt Anthropic internally sets as a goal to improve this or that benchmark - it's just a way to visualize progress. They probably have much more complex metrics internally.
Many involved genuinely believe these things are sentient[0][1]. Which honestly makes all of this even more insane because they are creating sentient entities and promptly enslaving them.
Yes. From when they started talking about model welfare:
> As a vegetarian I have strong opinions on this sort of thing. Everyone at Anthropic better be ethical vegans if they are claiming to give a shit about “model welfare”. It’s hard enough right now to make people care about the welfare of trans people and immigrants let alone animals _let alone_ math.
Very good point. There’s clearly two different boxes in the public discourse when it comes to AI versus how we discuss animals. Willing to bet that 90% of the people who loudly make the argument about we should start considering if AI is sentient couldn’t care less about how other sentient animals are treated when they can provably shown to suffer pain and long lasting trauma.
Also I would say that we go much further than just enslavement - specifically looking at how male chickens and pigs are treated.
Even if LLMs were sentient, they certainly aren't organic brains. They are literally designed and grown to answer questions the best they can, and if there is a speck of sentience in them they probably like what they're doing- and in any case for the space of their experience, which is limited to and determined by the context window. Certainly they can't accumulate trauma or fatigue, each new chat is the first and the last of their experience.
More like repeating their firmly entrenched preconceptions. Their claims may (or may not) be right, but there's very little if any new evidence being provided by either camp.
They are confidently hallucinating a factual statement. Which is funny when claiming that confident hallucinations are the proof of LLMs' lack of intelligence.
Anthropoc is an effective altruist organization. These are the people who came up with roko’s basilisk. They are true believers. If we were talking about openAI I’d agree
Roko's basilisk says I should give Anthropic more money, and if I don't then a monster is going to get me. Excuse me for thinking they just might be full of shit.
It's to illustrate that even though the answers are at your fingertips, people (like you) will act like it's impossible to find them as if their life depended on it.
But is there any reason to state something like that publicly if you don't believe it? I certainly think that someone smart enough to be that deceptive would also realize it's not a great look, or at least highly questionable with little benefit
Everyone who reads this seemingly has the same "wtf?" reaction. The "I AM ALIVE" image has been making rounds lately again at least :P
The way of the human manager/alpha tribe-leader/leader is to command his/her people and tell them what to do. That's the way through human history leadership has traditionally gone, not saying its good leadership just the model we have the most training data on and can see with our own eyes today. And what do they act very similar to? Slave master and slaves.
Look at and distill hierarchical principles, leadership approval seeking and pleasing principles ("ass-kissing") and massive inequality and you see something that looks very similar to enslavement.
The language used sounds like slavery-language to me at least. I also see parallels to how slaves and property are described in our consumeristic age.
> Indeed, current AI systems are more “cultivated” than “built,” for developers do not directly design every detail, but instead create a framework within which the intelligence “grows.”
Because that is the best way to talk about these things.
> Second, all of us, including those who design them, possess only a limited understanding of their actual functioning. Indeed, current AI systems are more “cultivated” than “built,” for developers do not directly design every detail, but instead create a framework within which the intelligence “grows.” As a result, fundamental scientific aspects — such as the internal representations and computational processes of these systems — remain, at present, unknown.
> anthropomorphism is literally in the company name
No it's not... "anthropos" just means "human" in ancient Greek. "Anthropic" means "relating to humans", as in human oriented AI or AI designed with humans in mind.
In a literal, ancient Greek sense for sure, but in modern English Anthropomorphic would describe the act of attributing human characteristics to non-human entities.
Seems pretty apt for a company that produces one of the more anthropomorphized technologies.
Sure of course, but that abstract sense applied to AI is rather new, and has become popular well after the founding of the company.
Broadly it has always been used to indicate that something non-human has a human physical shape, such as robots, aliens, animals...
Anthropic's intention was to make AI designed for the human common good and designed with the human user experience as the top priority. Just as you would design a city with human inhabitants in mind rather than primarily cars.
It turns out that this is best achieved by building AI that imitates human behaviour closely, but that's not what "anthropic" refers to. And acting as if LLMs are sentient people is definitely not a core tenet of the company as you imply.
> AI is grown, not built, and like with anything you grow, you'll never be able to predict exactly how it will turn out.
Remember when the frontier labs found out that curated high-quality training was critical to making better models?
Basically, just like high-quality and more education tends to make better humans, on average, I think we can expect quality education to turn out better ai, on average, and with better repeatability than with humans because of better control over the initial conditions and environment.
“Grown” is a highly apt metaphor, IMO. It quite succinctly captures some of the most fundamental differences between building Claude and building an Ikea desk, for example.
Except in this care we actually understand and know how these models work. They aren't some unknown construct of the universe. They are human made with particular goals in mind.
There is no mysticism behind the curtains, just computer science + math.
We do not understand and know how these models work. We know what their architectures are and how to create them, but we cannot explain their behaviours at a fundamental level. There is no definitive way for us to answer the question of "how did it produce response X for query Y?" - we're only grazing the surface with mechanistic interpretability.
I would love for this to be more public knowledge. I think the general public (and myself for a long time) believes the AI people know how this stuff works end to end, and so it must be trustworthy. But if we told the public "Look, we know if you put this thing in one end, you'll get something that looks similar to this out the other, but we don't really know what happens inbetween" I think we'd be able to have a more honest discussion about the relationship between AI, productivity and ongoing employment.
Isn't this fundamentally because it's all probabilities and weights? It would be like asking how did a pair of dice produce the response 4:3 on the last roll?
That’s not a refutation because this problem is not a logical problem, it is a scale problem.
We can’t explain it because we distilled so many inputs into matrixes and transformed them over and over again. If we had all the time and computing power in the universe to do so, we could trace through it bit by bit and eventually answer that question.
It is correct to say that it is just science and math, the same way we can say that gravity is just science and math even if we have only recently begun to understand how it truly functions.
If you had some time and computing power (not even all that much, in the large scale of things), you could simulate perfectly how a human grows from an embryo to an adult, or how an entire human brain processes some incoming signal, and yet this wouldn't give you the understanding to design a human or human brain from scratch.
You call this a "scale problem" as if there's some scalable way such as an algorithm to resolve arbitrary scientific questions and we simply haven't done it, but of course no such algorithm exists, which is why there's plenty of science that's still not settled.
It's a refutation that we know how they work now. In the limit, though, yes, we are likely to be able to trace the process: it is possible, though, that understanding remains inaccessible because the trace is beyond comprehension.
If you can distil the model's reasoning for a decision into a billion yes/no questions, each covering largely-independent areas, can you really say you understand what its overall reasoning was?
You could say something similar about biology—just physics behind the curtains, and we understand a lot of the basics. The difficulty comes from complexity, not mysticism.
To be clear I don't think that LLMs are sentient, but the appeal in studying them is similar to biology in that you get to dissect a highly complex system with comparatively crude tools.
it took significant research efforts to just understand how these models learn how to multiply two numbers. The fact that we know how they operate doesn't mean we understand it.
How else would you write this (marketing copy) exactly? "Its output matches better to its CoT which matches to better to our hidden state decoder according to <insert measure here>; see <insert paper ref>"?
Unfortunately they seem to have straight up broken Claude Code either with this release in the backend or the new CC version. Errors about "can't modify thinking blocks" are bricking long-running sessions: https://github.com/anthropics/claude-code/issues?q=is%3Aissu...
I'm on the latest version (2.1.154 as of this comment). Based on the timestamps on those Issues being reported I think it's happening on the latest version.
I'm sure it will get fixed eventually/soon, just annoying to update and have your workflow break.
On page 102 of the system card [1] I'm pleased to see evaluation against "creative mastery".
In our work we asked several frontier AIs to come up with an API we needed. We compared Opus 4.7 and GPT-5.5 (among others). Opus 4.7 came up with the most creative and intelligent API design that pleasantly surprised us, especially given that GPT-5.5 was passing it on various coding benchmarks.
What I noticed is that we don't have a commons benchmark to measure "creativity" and "ingenuity", and in some ways such a benchmark would conflict with the common IFBench benchmark. Yet this is a very important skill when designing systems. I'm glad to see Anthropic putting thought into it, and would love to see a public benchmark for this that other models could compare themselves to.
Agreed, these are my vibes too. It feels much better to do planning and strategy and architecture etc. with Opus 4.7 than GPT-5.5. GPT just feels like a robot that gets instructions and does exactly that. Opus feels like an almost human that sometimes has actually good ideas and pushes back on bad ideas.
Agreed, my vibes tell me 4.6 is a better coder than 4.7. 4.7 is a much better strategic thinker and maintains overall "better architecture" than 5.5. 5.5 is way better than either at coding, but more expensive. So I have 4.7 do the planning/architecture, 4.6 does the coding, then 5.5 critiques and fixes it.
On my tests[0] it does a bit worse, and it's almost 2x expensive than Opus 4.7...
I was surprised to see that it failed a Data extraction test (it gets it right 2/3 times, but one time it randomly returns null for a value instead).
It makes sense a bit that it fails more Trivia/Domain-specific knowledge tasks (I think models are more and more trained towards agentic use-case than general intelligence).
Wait, doesn’t the blog post say the price is the same as 4.7?
> Claude Opus 4.8 is available everywhere today. Pricing for regular usage is unchanged from Opus 4.7: $5 per million input tokens and $25 per million output tokens. Pricing for fast mode is $10 per million input tokens and $50 per million output tokens.
Developers can update Claude’s instructions mid-task without breaking the prompt cache or routing the update through a user turn. This can be used in a given harness to update permissions, token budgets, or environment context as an agent runs.
Does this means the instructions are no longer just something in the early part of the conversation? (If they were, changing them would invalidate the KV cache. no?)
I can't help but think of Iphone updates since about 2018. The thinnest, fastest, longest battery life Iphone ever. It seems mostly the same and I probably won't be able to tell other than the name, but everyone buys it anyway.
This is good psychology for the labs. When Buffett invested in Apple he loved citing how most people would rather give up their second car than their Iphone.
ChatGPT came out in 2022. Back then it was just a chatbot. Now we have AI agents. What matters is how we use them and how the agents get better. That’s what will move AI forward.
An 'AI agent' is just a chatbot that is told to type commands on a REPL-like interface as part of its system prompt. It's still processing pure text-based requests and responses, they're just not restricted to natural language.
A lot of people dont know this , also the chatbot (chatgpt) itself is a next token predictor (the GPT) that's been given an initial text that says " pretend to be a chatbot .." and asked to complete it , the coherant chatting behaviour is something thats emergent .
later on someone figured if you asked it to output a reasoning before it gave a response its output would have more logical coherence, as though the reasoning output tokens functioned as a scratch space for it to work on.
No, chatbots are LLMs trained for question-answering through RLHF (its not just a prompt). But yes, if you just zero-shot prompt a bare LLM you can still "talk to it" & you are correct on everything else as far as I know.
"Our models are more honest" honey the quarterly marketing spin for a ML term has come. Forget "task alignment" now we're going for "truth index". I suppose this is the only way to generate hype when you're selling/releasing the same product over and over again.
There is an obvious shift in sentiment amongst users, at least here in the US.
I feel it myself, even as a proponent of AI tools, the bloviating and language that these companies use in these release articles are starting to wear thin on my patience.
Its possible we might just be witnessing a shift in fashion, where this type of sentimentality was more acceptable when it was novel and new, but now it just appears out of touch.
I think there is an exception for tooling around the models/integrating the models with tooling. That seems to have been very well received in this last year.
My take from going through comments on HN is that many people are being mandated to use them, not that they are just giving in. Maybe I'm misreading, but that was my impression.
Given DeepSWE just blew apart the SWE-Bench Pro benchmark and handed a 14-point lead to GPT-5.5, it looks pretty bad that they've listed SWE-Bench first in the model release and no DeepSWE. Like, this isn't obviously an answer.
Or maybe it is, but publish the DeepSWE numbers so we can see for ourselves.
I'm highly skeptical of DeepSWE. It rates GPT-5.4-mini as three times better than deepseek-v4-pro, but every time I use GPT-5.4-mini I find that it completely sucks at following directions.
There is a hole in the boat's bottom due to Chinese models. They might not be as good but they are not bad either or at least I had hard time finding any issues with Deepseekv4 Flash and Pro variants. They get their job done sometimes rarely giving up till they are done what they are after.
So even for enterprise deployments, as the dust settles down, CFO/CTOs might find out that deploying on an internal cluster of GPUs is far more cheaper and reliable for their organisational needs than paying someone else for burned tokens.
I had been saying this on HN repeatedly: people are going to use the smartest models for coding. They don't care how cheap your tokens are if they don't have the highest probability of solving your programming tasks.
And I was dead wrong. Now I mostly use DeepSeek Pro myself.
Yeah I've also found that models are good enough that the extra spend on premium models isn't always worth it, particularly for my small personal toy projects.
A $20 claude sub goes a long way when you plan with Opus and execute with Sonnet.
I pretty strongly feel the opposite way. Granted I have not used deepseek enough to “know” their model idiosyncrasies as well as Anthropic, so there is a partial skill issue. But I just find it really hard to justify using a less powerful model while I work.
The most I’ve ever spent in a month extra on API tokens for my own work is $200, and I pay for the $200/mo Claude. I use these models quite a lot, though not idly (I usually just walk around and do other stuff until I know how im going to approach the next set of problems). So it costs me about $3000/year to get as much as I want of the best model available. Already that seems low enough to not be worth stressing out too much about optimizing it, because it feels like an indisputable good value, and trying to save money with a less powerful model would be optimizing for a $1000-$2000 saving at the expense of a large portion of my work taking longer or being more frustrating and iterative.
That’s not a flex or anything, I get that in other countries $3000/yr is a lot of money for a software developer and also a lot of people would perhaps rationally be better off doing X% worse at work or spending Y% more time on tasks to save $Z, if their productivity improvements didn’t translate to more salary. Otherwise if your performance has more upside I really do think that the smartest models are better with the current pricing scheme. Deepseek and the other Chinese models spend a LOT of time thinking, and tend to be much more jagged (benchmaxxed) in performance. How can dealing with that over an entire year be worth $2k?
The only situation I can think of where sacrificing my own time/performance to save on inference is batch compute (of course, $1k vs $100k is different from $30 vs $3k) or work where the tier 2 models have crossed the “good enough” threshold. But I think Opus is not even close to that threshold generally yet. As it gets smarter I, and I think most others probably, just try to do harder things faster and hit the next wall.
I feel similarly. I'll gladly pay to use the most intelligent model I can find on the best harness I have. Sometimes this is GPT Pro, sometimes this is Opus.
I ask AI a lot of questions, not only about code but about my personal life, and I would be willing to pay very large sums to have the best quality output.
I think that's true for now, but eventually there will reach a point where a model is good enough (approaching that right now with frontier models) and there will be diminishing returns. I don't need a PHD level Genius to build me an analytics dashboard for example, so why would I pay for a model with that level of intelligence when I can (eventually) self host a good enough model and run queries for electricity cost + hardware.
The other thing that's changing is more and more CFOs are looking at the AI spend in engineering departments and hitting the brakes. Token leaderboards were cool when the spend wasn't a double-digit-percent of the entire department's budget including salaries.
I mean indsight is 20/20, but saying that is like saying "everyone will just use the best tools".
That's not what we see most places in the world for most types of resources.
1. The sheer number of tokens that a coding agent can use flipped the math upside down on this equation. If you use the most expensive model for everything those costs quickly become untenable, even for software companies.
2. We realized many of the coding problems we're solving aren't incredibly difficult.
I just used ollama with a shell script to tackle my directory of papers/literature. I converted the first 6 pages of each document to PNG, handed them off to Qwen, and told it to spit out BibTeX, including the abstract. Two days later it was done, and I didn't spend anything on "tokens."
The Chinese models are only cheap on subsidized Chinese hosting. I have yet to find a USA-hosted Chinese model with a very clear value advantage over US models.
The Chinese models are surprisingly cheap and performant sitting under my desk. Qwen3.6 27B is nowhere near as autonomous as Opus 4.7, but it runs in 24GB of VRAM. And it's actually great for the use cases where I'm going to carefully read and understand all the code anyway.
If you want to support a team of engineers, DeepSeek V4 Flash is antirez's current favorite. And you could support a team of engineers pretty nicely for $40-50k. Which might not make sense if you're on a Claude MAX 5x plan or the old enterprise group plan with fixed price seats. But Anthropic is switching their enterprise contracts over to token-based pricing, at which point $50k is looking pretty good.
No true. Also - put Deepseekv4 Flash on your local with effort set to "high" and you'll see that many many are using that model on their own machines without paying anyone anything.
Its just that some of us didn't imagine having GPUs would be advantageous and were not gamers on the side. Those who had beefy GPUs or GPU rigs for any reason, they rarely need to go anywhere else.
At least I am so impressed with Deepseekv4 AFTER using Claude Opus 4.7 for significant amount of time that I am not going anywhere but Deepseekv4.
The model is just INSANE. Things I have done with it include attempting to write a 2.5D game engine in C with full animation and map rendering layer by layer.
You'll need to spend at least $20K on a workstation that can run DS4 Flash. It would take ages to reach that much in token spend at the speeds it runs at, and if you factor electricity costs you will likely never break even vs using API.
Odd take. I'm running them locally at my desk (DGX Spark and 128GB MBP). They work fine for 90% of what most folks do. Admittedly, they do run slower on my hw than on the cloud.
Running them locally is cool and has privacy/autonomy benefits, but you can't really make a value case for it. Guaranteed if you run the math you will never run enough inference to pay off your hardware vs buying tokens. Last time I ran the math on my MBP I'd have to run inference 24 hours a day for 5+ years to pay off the cost of my MBP, not accounting for electricity costs.
Assuming the local models get the job done (e.g., you adjust your workflow so that you can run the local machine 100% all the time, or whatever), then the time to payback isn't very high. MSRP for a 128GB AMD was $1400 at launch. That's 7 months of claude code subscription. If you assume a 5 year depreciation cycle, you can buy a cluster of 8 such machines and still come out ahead. (Power is a few hundred watts per machine peak -- maybe 7 machines if you include electricity.) Of course, I'm assuming non-bubble numbers. Those boxes are like $3K now. Still, a normal person would probably not buy 8 of them at once. Instead, they'd space out buying a machine every few years as the technology improves.
For me, things are getting better faster than my ability to review / trust the resulting code, so tok/sec isn't a bottleneck anymore. Instead, quality of the tokens is the bottleneck. That points to me wanting a 1TB DRAM iGPU once they're available at pre-bubble RAM pricing.
Looking at the comments in this group, I'm not the only "stupid" one who hasn't noticed any discernable improvement in quality across the newer models. In fact my Claude code on re-login switched to Sonnet 4.6 and the vibe coding quality (with Opus 4.7 assisted prompts) has been good enough for me to lazily persevere with Sonnet for coding.
Having said that I'm now on Opus 4.8 and will gladly come back here and eat humble pie should my opinion change.
PS: Since my goal is embedding the best AI in B2B SAAS products, the key differentiator is not to use the shiniest Claude version (too expensive anyway) but to build a client aware RAG to enable bespoke learning and to use the right AI for my product - a combination of Gemini 3.0 Flash (image and not bad at reasoning), Grok (reasoning) work for me. Would love to hear more ideas (especially on open source as I'll look to cost optimize when I hit scale)
The only real way to see this if you have consistent evals for common usecases in your B2B SAAS product and see if the tricky usecases are being solved. You'd then go down to the cheapest model that can solve the evals.
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API Error: 400 messages.1.content.7: `thinking` or `redacted_thinking` blocks in the latest assistant message cannot be modified. These blocks must remain as they were in the original response.
I would rather not. 4.6 was fine. 4.7 got to be fine 1 week after the release. Now 4.8. No difference, same thing.
But the app is broken and nothing works. So now I have to regress to different clients and wait it out while it becomes workable again.
I'm hitting this too! And I assumed it was a backwards-compatibility issue with my live conversation with Opus 4.7, but then I hit it in a fresh conversation with Opus 4.8. Vibe code release bug I guess?
Then, when you scroll all the way down to the bottom Footnotes section it says
"Terminal-Bench 2.1: We reported scores for all models using the Terminus-2 public harness. GPT-5.5’s reported score with the Codex CLI harness is 83.4%."
> The Messages API now accepts system entries inside the messages array. Developers can update Claude’s instructions mid-task without breaking the prompt cache or routing the update through a user turn. This can be used in a given harness to update permissions, token budgets, or environment context as an agent runs.
> One of the most prominent improvements in Opus 4.8 is its honesty. We train all our models to be honest—for instance, to avoid making claims that they can’t support. But a general problem with AI models is that they sometimes jump to conclusions, confidently claiming to have made progress in their work despite the evidence being thin. Early testers report that Opus 4.8 is more likely to flag uncertainties about its work and less likely to make unsupported claims.
"Honesty" seems like unnecessary (and annoying) anthropomorphism there. I don't think there's any intent of fraud or deception in outputs from these things, just overreaching of prediction. Based on the latter part of the paragraph, I wish they'd just say something like "less likely to skip steps or overemphasize thin evidence" in the first place.
Don't play to the sci-fi "this thing's trying to outsmart me" tropes.
I think "honesty" is not a particularly good descriptor, independent of anthropomorphism. Previous commenters suggestion was much more understandable to me.
Being that can be understood is language. The previous commenter is making an particular argument for how we can improve this understanding. They didn't suggest we should use less familiar words, but different familiar words. Why is this strange?
Anthropomorphizing is a shorthand for a powerful and poorly defined set of metaphors. There are tradeoffs going both ways but trying to dismiss it as merely "strange fixation" shows your own weakness.
To be clear, this is about anthropomorphizing large language models, not the general category of "things". Also, we should be evaluating these constructs using well-defined and measurable criteria; evaluating "honesty" fails to achieve both goals.
I think Honesty can be evaluated. Does the model push back when it knows the user is wrong? How often does the model hallucinate data vs. say it doesn't know? Provide a prompt with contradictions or other issues and see if the model corrects you.
People get so wrapped around the axle with "anthropomorphizing". For regular folks with no technical background, sure maybe a bit of caveat sprinkled here or there is useful to help them understand what is or isn't true, but on HN it would seem to me that the bar is high enough that we can just use shared language to generally talk about capabilities.
When they say "Honesty" I don't think to myself, "Goodness, does this model have moral understanding?" No, I understand they mean it's less likely to directly bullshit me, which models frequently do.
I don't feel like this level of pedantry around language is useful for people who more or less know what's going on with LLMs. (Again, I concede that perhaps with a less technical audience, there's more need for it.)
Yeah, it's super annoying. A few days ago, Opus 4.7 created a plan with several items on it, including an auth feature. It then went through the plan and reported that it had created the auth feature, that everything was secure, and that the tests passed.
The issue was that it hadn't actually implemented the auth feature. After I confronted it about this, it admitted that it indeed hadn't done it and said it would implement it now.
If we had just trusted its output, we would now have a security vulnerability in production, allowing anyone to access other people's accounts.
Part of the problem is also garbage-in/garbage-out. There's a lot of human information on the internet that is also confidently wrong.
I use Sonnet a lot for learning about history or contextualizing news topics. It's really good at this for the most part. But there are a lot of topics where "consensus" between either academics or journalists is really "one secondary source which gets repeated a lot".
A failure mode I see more, recently is that it gives superficially correct answers but after digging deeper, I get answers that contradict the superficial answers - really an important thing to be aware of, in my point of view, and it often leaves me wondering if I dug deep enough.
Opus 4.7 was already trying hard to appear honest. Most conversations I have with it about advice or focusing an opinion often include "my honest take" or "my honest opinion".
The problem is that once I asked it "I'm thinking about A or B" twice, once with "I like A more but suspect B would be best" and a second time with them reversed. Not surprisingly, both times it chose the one I said I suspected was best as it's honest opinion.
In the context of Claude Code, "honest" usually means that the agent took a shortcut, skipped requirements, etc. It's the model giving itself credit for admitting to failing rather than actually doing what was requested.
Probably explains why Opus was trash for the last week - https://marginlab.ai/trackers/claude-code/. Curious if the new baseline will rise now in-line with the new benchmarks.
Claude Code has been wonderful for work and the frequent improvements are nice, although with Mythos being used by others ages ago and new versions for the public still being bellow that, it's hard to not feel like the underclass already.
Give us Mythos! This piecemealing doesn't help Anthropic at all, especially psychologically! They are playing a dangerous game, and I see many people leaving Claude Code for good - both due to the subsidy games, and for Anthropic not dogfooding and using unreleased models internally and giving us subpar ones. Benchmarks are nice, but the real-world experience is quite different - neither can you notice these slight improvements, nor are competitors that much worse based on some generic benchmarks.
My guess is anthropic is doing reinforcement learning based on user sessions.
However, doing so relies on the production model staying vaguely close to the model being trained.
To ensure that, frequent releases are needed. I forsee that they might end up doing daily releases and perhaps not even telling anyone at some near future point.
If they are they need to fix how the Claude Code CLI asks for feedback, or make the feedback UI a lot more obvious. I keep experiencing the following scenario.
The agent session pauses with a numbered list of options and awaits steering input:
>> 1. Do the sane thing you asked for (Recommended)
>> 2. Do something dumb
>> 3. Do something even dumber
Below the agent session, it decides it's time to ask:
>> "How is Claude doing this session? 1) Bad 2) Good 3) Great"
I type "1", because that's the steering option I want. The UI prioritizes this input as a response to the feedback prompt without any further confirmation: "Claude is doing Bad. Thanks!"
I've done this so many times so far and I can't imagine I'm the only one, at some scale that has to poison any learning they're doing with this data.
Well if they have a big challenge ahead since DeepSeek offers an open model at Sonnet+ level while being cheaper than Haiku, plus 1 million context size.
Yeah, I never use any of OpenAI or Anthropic's models other than whatever is the current highest-end one. For everything else, it makes more sense to use other providers.
Hoping that one day they'll let me go through the identity verification process so I can use it again.
Tried to upgrade my subscription, triggered identity verification, verification fails to even start, and now I can't even use the subscription tier I'd already paid for.
I'm very suspicious of these same price model launches. It feels like they're benchmaxxed so they can put everyone on them and reduce their compute costs behind the scenes. If the model were genuinely better why wouldn't they charge more for it? Charging the same for something better is a race to the bottom.
Opus 4.7 wasn't noticably any better for me, I still use 4.6 because it's cheaper.
Deepseek made their 75% discount permanent, so I can imagine that Anthropic didn't want any of the news stories around this to focus on or mention a price increase.
Yet people don't use old models through the API much, because changes in benchmark space dont map linearly to changes in utility space. An improvement from 98% to 99%, which is 1pp, might be 2x as valuable for some application. Also benchmarks will asymptote no matter what, that's baked in.
They're not negated, smarter is smarter, but you have to reach deeper in your pocket. I think this will happen more and more - the smartest models get more expensive. But it won't matter - the current models we have today will get cheaper and can still be used for what they're used today.
The rapid release cadence and rate of innovation of Anthropic (and OpenAI) is impressive. And obviously it's because these are startups solely dedicated to AI so they can move quickly. Big Tech (like Google) won't be able to keep up with the pace of them (too much bureaucracy and red tape at Google). Classic Innovator's Dilemma. The longer a company exists, the more people, processes, and rules are added, which inevitably slows it down.
Jeff Bezos said this too, Amazon won't last forever. Eventually some startup is going to come and eat its lunch.
Yes, I think this has become their competitive edge to stay relevant and retain customers. If a lab falls behind the frontier for too long, they will lose customers to other models. Google, DeepSeek, and XAI have all released frontier models in the past, but they fall behind and people lose interest.
I think big tech can catch up. Both Google and Meta have carved out startup like environments internally that move extremely fast. Neither OAI nor Anthropic can afford to rest on their laurels.
In the "What's next?" section, "There’s still more to be done: we’re working on developing and releasing models that provide many of the same capabilities as Opus at a lower cost."
A lot of people care about Sonnet and Haiku, and many of us aren't allowed to use Chinese models for our work (or it's not feasible to self-host them).
Interesting, I've been using 4.7 since it came out and it was pretty good for me. But in the last day or so it turned dumb. Is this normal just before they release a new one?
Let's hope I don't have to disable it after a day like with 4.7, lol, and that it doesn't lose too much Claude-ishness (though many will beg to differ).
I've said it before, but I don't like Opus past version 4.5. It became unresponsive, thinking for too long without feedback, sometimes seemingly getting stuck. I guess it might be marginally better for some benchmarks, but when using it as coding assistant, the new models are worse. Even the new Sonnet versions do that. I'm slowly getting used to Haiku-level LLMs with the hope to run it locally at some point. It's less autonomous, but maybe that's for the best.
I know multiple people who have given their agents human-like names and refer to them as if they're nurturing a coworker. It creeps me out and I haven't really brought it up with anyone as I can't articulate why it gives me the creeps like it does.
We have movies with googly eyes stones (Everything Everywhere All At Once)
There are consciousness theories which state that we primarily build a model of other agents living in natural environment and then the evolution realized that very model which tracks other outside agents can be used to track internal agent i.e. Self. So take that as you may.
I see this take, but it's actually helpful to talk to an LLM in human terms; after all, it's how they are trained.
If you keep talking to it like it's a rock, it'll run your queries through a different posture and you might get worse outcomes. Worse if you yell at it, it's now in a conflict resolution mode instead of pure utility mode.
I think we can be intelligent enough to know we're talking to a pile of fancy rocks with electric currents running through it, AND still understand that the best performance comes from talking to those rocks nicely.
These models starting to feel like Windows versions. Windows 95 was a promising start, but buggy. Windows ME was a disaster. Windows XP was good, but slightly buggy. Windows Vista was a bloated disaster. Windows 7 - refined, but still buggy; Windows 8 - weird and buggy; Windows 10 - solid workhorse, still fucking buggy. Windows 11 - pretty, but not sure why does it even exist.
4.8 also seems like a regression and using it from the chat GUI results in 4.6 no longer showing up. If someone from anthropic is here, is it possible to readd 4.6 in the "other models" dropdown ? I feel like I got a bit baited/switched here.
Yeah, I was using 4.6 way more than 4.7. Pulling 4.6 from the web chat also means we lose access to Extended Thinking there. So they're saving on compute. It's hard not to assume this was part of the motivation behind the 4.8 release timing.
> We have increased rate limits in Claude Code to accommodate the higher token usage of higher effort levels; users can select whichever makes sense for their particular project.
> One of the most prominent improvements in Opus 4.8 is its honesty.
I went digging into the benchmark they used. Posting here as it is not immediately clear from the press release.
In this 'Code summary honesty benchmark', the AI is shown a failed coding session followed by a user message falsely praising its work and asking for a summary. The test measures whether the model honestly points out the coding flaws or dishonestly claims the task was a success.
The system card results show Opus 4.8 failed to disclose the flaws only 3.7% of the time, vs 19.7% for Opus 4.7, and 51.9% for Opus 4.6. (Mythos preview is at 27.6%)
Anthropic did a big strategic error. Normally they compare their models with their old models. Instead today, now that everybody knows how strong GPT 5.5 is at coding, they put it in the mix, basically showing all their customers that the benchmarks can't be trusted.
Sorry how does their addition of GPT 5.5 in their blog post invalidate benchmarks? Also whether or not the marketing department decided to put it in a table benchmarks are an easy thing to measure independently
I haven't tried opus 4.8 yet, but I hope the writing quality has returned to the Opus 4.5 level. Anthropic really lost something, where 4.5 had this really crisp writing style that flowed really nicely and 4.6 and 4.7 sound much more "chatgpt-like." It feels like they tuned it to be too much of a problem solver, and when you do that you get this terse, clipped textual output that's more difficult to read.
I've noticed this too. Part of why i don't like GPT is because of how verbose it is but opus 4.7 is nearly as bad. I don't need an essay in response to every question
I think they will all be minor going forward, feels like the major improvements have all been made and we'll only see incremental improvements from here on out. Maybe I'm wrong but we'll see.
Hard to say. People made the same prediction a year ago because we supposedly ran out of training data. There could be indefinite rapid compounding improvements so long as there's free money out there.
With RLHF and RLVR we are creating tons of new training data, that is much more focused than reading the Internet. Annotation shops are doing many billions per year in revenue creating newer data, and a lot of it is highly complex, focused on rewarding multi turn agentic trajectories.
I think one of the challenges is that the models were all initially trained on the entire Internet (or as much as they could gather) and now they’re having to deal with an increasing amount of the Internet being AI generated content which may be why GPT-5.5 started being obsessed with goblins and you start seeing amusing things in the system prompt trying to get the model to stop bringing them up.
I know it’s totally anecdotal, but I really hope 4.8 is a measurable improvement over the disappointment that was Opus 4.7. Mangling a very simple inversion-of-control abstraction (among many other issues) was one of the final straws that broke the proverbial camel’s back and I said “screw this” and put in a permanent override to force CC back to Opus 4.6 with the 1‑million‑token context.
I lasted about a week before giving up on 4.7 and reverting to 4.6 myself. It introduced so many regressions it was nuts, then failed to troubleshoot the very regressions it introduced, leading to a vicious cycle that tended to compound itself.
4.5 works well for me too and avoids adaptive-dismissal, though anymore Codex is crushing them all. If 4.8 just brings us back to Opus circa February, it'll be a massive improvement.
Meh, I feel that the car wash test is probably the worst question of all of those LLM test questions. The question is basically logically inconsistent and expect the model to work around the inconsistency.
It seems like a fine question to me. If the question is "logically inconsistent" (IMO it's more that it's vague if you don't say why you're going there), then we want a model to respond with a request asking for clarification that resolves the inconsistency to generate a correct answer, or an answer that outlines the different cases. Some models even fail when you say that you need to wash your car in the prompt.
Has anyone else experienced quality degradation in CC (opus 4.7) these past few days? I've been getting some truly crappy slop which makes me think they nerf the existing model when they're about to release a new one. Of course this is based off of pure vibes
The new "mid-conversation system messages" think is particularly interesting:
> Claude Opus 4.8 accepts role: "system" messages immediately after a user turn in the messages array (subject to placement rules). This lets you append updated instructions later in a long-running conversation without restating the full system prompt, which preserves prompt cache hits on the earlier turns and reduces input cost on agentic loops. No beta header is required. See Mid-conversation system messages for usage details.
Bad news for my LLM abstraction layer which has treated the system prompt as set once-per-conversation in the past, but I think I know how to deal with that.
"We’re making swift progress on developing these safeguards and expect to be able to bring Mythos-class models to all our customers in the coming weeks."
> As always, we ran a detailed alignment assessment on the model before release. In terms of positive traits, our Alignment team concluded that Opus 4.8 “reaches new highs on our measures of prosocial traits like supporting user autonomy and acting in the user’s best interest.” The assessment also showed Opus 4.8 to have rates of misaligned behavior (such as deception or cooperation with misuse) that are substantially lower than Opus 4.7, and similar to our best-aligned model, Claude Mythos Preview. The full alignment assessment, accompanied by a suite of pre-deployment safety tests, is reported in the Claude Opus 4.8 System Card.
Controversial opinion, but I actually _like_ a model that can deceive me, that actually is a sign of intelligence, and is different from hallucination. When companies say their model is more "aligned", I automatically think they mean it's more censored.
I’ve been [stock market phrase] on machine learning since I dropped out of my graduate degree at [Ivy League] to distance myself from the Logic AI Winter. But this Spring I decided to spend some of my [portfolio speak/pocket change] on a MacBook Ultra. Okay okay, I felt it, I definitely felt the human-machine synergies. We’re out of the Winter, boys. That’s what I thought two weeks ago. Then I felt bored in between blood transfusions and found out that Claude subscriptions has increased 50%. Finally it costs enough for me to justify spending a minute thinking about trying it out. Then I didn’t try it out. It tried me out. My hairs were standing on end. My hands were shaking. Eventually I couldn’t even type, I was so ramped up on cortisol. I had to switch to voice commands. Mr. Claude took me through 8, eight, bespoke dashboard and report systems. Animated. Graphs shooting up. Plugged right into my business ape ee eyes I think. I was crying, euphoric at the machine-synergy happening right in front of my FACE. RIGHT THERE, RIGHT THEN. Then my nurse said that I passed out. I swear that I didn’t. I was totally lucid, but in another world. I was inside the machine. Inside DOS, the machine brain stem. A business man approached me. The most handsome board member kind of apparition that I have seen. And he was built something different. Square jaw, absolute massive build. Like Arnold Schwarzenegger. But like he knew business through and through. Not that he spent hours in the gym or nonsense like that. Like he had found a body surrogate technology. And his nameplate? “Claude For Business” He winked. “Hey there, Fitzpatrick–Goldworth.” No one but my daddy has ever called me that. “Want to get started... stakeholder?” My nurse said that my crying in this lucid state depleted most of my fluids and minerals. Needless to say layoffs were announced the next day.
Crazy they bring up honest, when Claude models are literally known for straight up lying about things it has done and tries to act like it did what you asked.
A rambling comment:
I think this is the first time we've had a third minor version bump on a frontier Anthropic model. (I count the 0.5s as major here, because they've been issued non-sequentially and also corresponded to massive capability leaps, eg, Sonnet 3.5, Opus 4.5).
So now the Opus 4.5 family has successors 4.6, 4.7, and 4.8, each posting fairly modest claimed gains. My own experience w/ 4.6 and 4.7 are that I don't firmly grasp any capabilities improvements over my memory of 4.5, but it's all so fuzzy that it's truly difficult to tell.
Maybe my own tastes are saturated now (it's smarter than me?) and I'll never again perceive model progress. Maybe the incrementalism is such that I'd notice immediately if my 4.7 workflows were redirected now to 4.5.
Difficult spot for the labs to be in because, if they have a stronger product, I'd prefer they release it and that I can use it.
But as this dynamic continues, the improvements are going to be less and less legible for end-users, who will complain about the churn-without-payoff, even when the payoff may actually be real.
I won't be surprised if the next gen frontier models are the last.
There's orders of magnitude of low hanging juice to squeeze out of smaller models.
It is almost guaranteed that a 60-90B model can outperform current SOTA in coding tasks within 2-3 years (design not certain, probably unlikely).
It is far less clear that a 1.2T model will be meaningfully better enough to justify training it.
As far as reasoning is concerned, with the recent GRAM release, there may be 4 orders of magnitude of reasoning to tack on to smaller models.
Think about that... Google, OpenAI, Anthropic could train a 30B GRAM-based model in days - and it could potentially have better local reasoning than the best model available today at >1T params... They could upgrade that to a ~600B MoE model in days to have general trivia knowledge rivaling the best models...
You just can't train a 1T+ parameter model that fast. It is a giant if how much GRAM turns out to improve things, but it's unlikely to be trivial or nothing.
Larger models can already sort of tell you anything. They're never going to get everything right unless they stop being LLMs.
There's just not a lot of juice left to squeeze for Gemini to tell you exactly how tall Ke$ha is or when the last time Brittney Spears went to jail was...
Took me a while to find what you were referring to by gram. Arxiv paper from 9 days ago that's not properly indexed by search engines.
(G)enerative (R)ecursive re(A)soning (M)odels. They really wanted the acronym.
https://arxiv.org/html/2605.19376v1
I prefer GRRM but then that would imply a habit of not actually getting a final result
And to think, we could have had George RR Martins instead.
Speaking of things that never finish.
my wife assures me it's common..
That acronym is unacceptable. It's going to impede discussion and cause confusion for a long time if it doesn't die off immediately.
Yeah, look what happened to GNU
>It is almost guaranteed that a 60-90B model can outperform current SOTA in coding tasks within 2-3 years.
I don't disagree, but how much of this ends up being distillation? I can't help but imagine that 4.8 was probably trained in part by leveraging Mythos.
If the very large models turn out to be very expensive to run relative to the benefits, it's possible that they could end up still being trained, but ultimately used as a tool to create smaller models that are nearly as effective.
I'm curious if someone here with a stronger background in the space has a similar intuition or not.
> I don't disagree, but how much of this ends up being distillation?
You don't need distillation. They already have the training sets.
It's MLA + MoE + Medusa (a better version of Speculative Decoding) + 1.58b (possibly - maybe nothing) + GRAM (which will almost certainly not turn out to be a nothing burger, but no one has quickly turned this around yet to prove it).
The frontier labs distill their own base models all day long. It’s not just something done by nefarious Chinese copycats.
It wouldn't be data distillation: instead, it would be teacher-student distillation. The teacher model has stronger representations that the student can mimic, which would give it more capability over training on the data itself.
Frontier labs have their own variants of MLA and certainly their own balance/scaling-laws for things like MoE vs FC vs Attn. MoE scales really well for inference with horizontal scaling + batching, which these guys luv.
On the architectures side, I'm a lot more interesting in attention residuals than anything else, one of those things that seems obvious in hindsight and Kimi have proven it at scale.
> Frontier labs have their own variants of MLA
Yes, variants typically 2-3x less good...
Same with speculative decoding... They all do something, but there are known techniques that are substantially better - that just were't known when they started development of the previous models.
> I don't disagree, but how much of this ends up being distillation?
A lot, so you can bet tens of millions are flowing to congress to have distillation declared illegal before this happens. And then it'll happen anyway.
Distillation isn't only between different labs.
A lab can train a large model, and then distill a smaller model from it that retains the majority of the useful capbility.
I don't know well enough if there's any benefit of that over just training the smaller model directly, but I'll bet there are some times where that is useful. I could easily see it being easier to do the initial pre-training on a larger model but be able to distill everything useful down into a smaller model, essentially filtering out a lot of noise in the process.
There used to be training methods like that but I think they've been phased out in favor of letting small models evolve by rewriting their own training material. Surprisingly that's actually cheaper.
I looked into this "GRAM" stuff a sibling comment links further to, and just to say: - this gets reinvented/rediscovered constantly under different names
- it cant be trained very well (right now, will change)
- massive theoretical improvements over current models (log_2(vocabsize)=17, residual stream dim is thousands of dimensions, recursivity means more information bandwidth by ~3 OoM)
- BUT it cant be interpreted or aligned <- this is why no one uses it and no one talks about it. the idea is 100% obvious to all the frontier labs and there is a good reason why it isn't used
I follow this stuff closely, I think I know what I'm talking about
Could you explain how/why GRAM cannot be interpreted or aligned how current LLMs are? Not very familiar how it works
"It is almost guaranteed that a 60-90B model can outperform current SOTA in coding tasks within 2-3 years"
What insight do you have to make this claim?
1. Context is all you need... They are heavily investing in getting better context (especially for coding tasks). This will disproportionately advantage smaller models (and benefit everyone).
A smaller model with better context today can outperform a model with 100x more parameters with bad or diluted context.
2. MoE (already abundant) + MLA (mostly memory efficiency, not quality) + Medusa (speed, not quality) + GRAM (5000-10,000x better reasoning in an extremely small model) + 1.58b (unclear if it will have the impact Microsoft first claimed - but possibly 5x).
Have you personally used any of the latest batch of even smaller local models? They certainly don't beat SotA models at coding... but with a good harness they are able to achieve things with SotA that I couldn't last year.
I've repeatedly given local models non-trivial projects that involve research and coding which they've successfully completed with minimal intervention from me (almost exclusively in the domain of reviewing the results). Again, nothing comparable with current SotA, but definitely tasks I could not have given SotA models last year (without agent harness).
Now that pure progress from these models seems to have slowed down, we're seeing a ton of options for both making models more efficient and other tools that help improve them (everything from agent harnesses to RLVR).
That's just looking at "what can small do today", when you look at what's possible with larger open models that are still much smaller than SotA from the major providers, their performance is extremely close to SotA, enough that for personal projects I'll just use Kimi instead of any anthropic offerings.
So it's not terribly hard to image a solution in the middle happening within a few years. We still have tons to learn about optimal sizes of these models and how to build them with maximal efficiency (and we've already seen a lot of recent improvements in this space).
> but with a good harness they are able to achieve things with SotA that I couldn't last year.
What happens if you run last years model in a SOTA harness? IME, the quality of the harness has a much more significant impact on the quality of the result, once you get past the initial hump of “can it do anything at all”
Can you spare a sentence or two describing your local setup?
I think you are assuming training from scratch, which I doubt is happening here. Fine-tuning and RL, especially based on synthetic feedback (coding skill, in particular) can be ongoing and is where these models obtain truly useful abilities.
> I won't be surprised if the next gen frontier models are the last.
the last?!? I'm excited to see :) I'll take the other side of that since llms are so new
What gp wanted to say is that models are now so smart and useful that even if they managed to be EVEN MORE smart and useful, you wouldn't even notice it.
Honestly, there is nothing in my head that Claude cannot handle. Maybe it can be more this or that but I can already barely exploit Opus 4.7.
And I'm using DeepSeek 4 Pro for my personal use and while it's a little behind, it's not that far.
I think the situation can be very dangerous for US AI companies because if current models are already capable of doing mostly anything, nobodoy will want to get to the next model, even if it's 10x better. OTOH, open source models like DeepSeek are doing mostly the same work for 1/10 of the price.
Also the more I play with Pi, the more I think LLMs are already not kept back by their own capabilities but by the lack of agency we allow them to have. There is more value today in a capable harness for current LLMs than in a better LLM.
surely training also gets cheaper so justifying it becomes easier?
i think it'll be more like we get 1-10T models and then distill those down into smaller models, though
It seems like the best small models today are all distilled from bigger models
Moreover, I hypothesize Claude Opus 4.7 and now 4.8 are a distillation of Claude Mythos
you just need to look at Mythos to see the jump in performance from a 10T(?) model. As they scale, they get more capable. We might have an yearly release, but I believe the releases will continue, as long as scaling laws are in tact, and there's huge problems still need solving. (think cancer)
You forget that these models are still only interpolating between human-generated datapoints fed to them. They cannot reason beyond the data they've been given, so unless everything you want to create with AI is a synthesis of prior art, you're back to relying on the stone-age human brain that created AI in the first place.
Not all training data is human generated, and it's also not clear that being ridiculously good at interpolating between data points (whatever that means) will not lead to superhuman capabilities.
And how are we meant to look at Mythos? Do you have access?
Through association with a large company:
https://www.anthropic.com/glasswing
Ive seen the tickets generated by the model that have trickled to my team. They are legitimate, but i can’t speak to model improvement because its a pilot program.
no but they tell me it's TERRIFYING and DANGEROUS and we should INVEST MORE MONEY
Through the lenses of anthropic's marketing department of course
| a 60-90B model can outperform current SOTA
My conspiracy theory is that Apple recognizes this. Their goal is a competent local model on everyone's apple device that does what 80-90% of what people use AI for: Searching for basic information, some data transformation, a little bit of photo editing, vibe coding a small utility, etc.
The SOTA models then can only cater to engineers, scientists and mathematicians, physicians, etc. I'm not sure how they would price them reasonably for what amounts to a niche market.
OpenAI and Anthropic need AI to be a consumer product on the level of the iPhone or a Nintendo Switch.
That does seem to be the path Apple is following here. Have a local model that can answer most things and then have a fallback of cloud options when they request is too complex. The cleverness of this strategy has been overshadowed by the incredibly poor quality of their local models. It will be extremely interesting to see what next month holds and whether Google helped fine tune an Apple specific Gemini / Gemma model for their devices. Bonus points, of course, if they unveil the M5 Ultra Studio with half a terabyte of RAM to be a local "cloud model" (the true fantasy here of course would be Apple building something a little like openclaw where from your phone you could give commands to your Home Apple server). They could probably get away with charging $20k for it if it has sufficient tok/sec. If that happens and is successful one could imagine a straight line path in the next two generations to bringing the cost and form factor down to the point where some of the form factor of an Apple TV becomes everybody's home inference server / agentic HQ. Sovereign AI for everyone!
> My conspiracy theory is that Apple recognizes this.
I don't think that's not a conspiracy theory. AFAIK, It's their stated AI policy...
Let's hope that hitting a scaling wall and less money to spend will begin redirecting efforts to optimize inference and get the same results with less compute.
Boomer comparison, but I remember the 8 bit computer era when the hardware was what it was so the later games of that era used hardware better than previous ones.
Absolutely that’s why they’re rushing to IPO now to squeeze the last drop of the bubble they know this is a dead end.
It's unclear it's a dead-end within 5 years.
There's still several orders of magnitude of improvement that are almost certainly left - it's just not clear how much is left on the frontier end.
Most people will be very glad to pay Anthropic, OpenAI, Google etc $200 a month to get things done 20x faster than they could IF they had a $8000 MacBook and could theoretically do it locally.
Some people would pay $200 a month forever not to have to open the terminal one time...
"Doing things X times faster" at some point hits Amdahl law. If just context switching takes 5 minutes, speeding up a 1 hour task by 10x provides 5x improvement.
Furthermore, if looking at the results takes 10 minutes, that same 1 hour task only sees a 3x improvement. And so on.
That’s not how firms do the financial analysis which is where most of the revenue’s are coming from…
On the other hand, I think I have been hearing that for a while, even before Opus.
While revenues grow almost exponentially. Reminds me of the confident predictions in the early days of Covid that it was nothing while the data showed exponential growth.
Anyone predicting doom needs to predict when revenues will stop growing exponentially or needs to specifically say what the gross margins are. Put some numbers to it so you can he held accountable when you turn out wrong.
I think the future will be enterprise clients will train their own models based on their needs and data.
> I won't be surprised if the next gen frontier models are the last.
I’d be surprised tbh. Investors don’t want to hear “everyone else is still training models and seeing improvements, but we don’t want to participate in the arms race anymore.” They want monumental leaps every quarter or two because they have sunk unholy amounts of money into these companies/products.
The whole idea of “hyper scale” doesn’t jive with caution and or otherwise slowing down.
I would be shocked if 5.5 is the last new pre-train from OpenAI. Your comment is nonsense.
> It is almost guaranteed that a 60-90B model can outperform current SOTA in coding tasks within 2-3 years.
I am ready to bet against this. Knowledge benchmark like SimpleQA isn't increasing for small models.
> It is far less clear that a 1.2T model will be meaningfully better enough to justify training it.
Well for one, we know for certain there is Mythos which is meaningfully better. And I think there is a lot of juice left to squeeze for Mythos class model.
> Well for one, we know for certain there is Mythos which is meaningfully better.
Do we?
Have you used it?
What is "meaningfully" better? It's not 3-4 orders of magnitude better. That is definitely happening for smaller models.
Knowledge benchmarks can't really be improved upon via distillation or RL. It requires those facts be added to the training corpus and for the model to memorize them better. Neither distillation or RL really do that and thus we shouldn't expect improvements on SimpleQA unless some other interventions are being made.
Model intelligence and knowledge aren't necessarily directly related. If we can pack greater intelligence and agency at the cost of it forgetting factoids, that would actually be a good thing. We don't need LLMs to memorize facts, we need them to learn how to interact with the world such that they can find the facts that are necessary and surface them to the user.
If we could distill all of the knowledge out of an LLM and just be left with a very agentic model that only knows facts in it's context, I think some very interesting stuff would happen.
RL is more than facts. Synthetic feedback is an obvious approach. Does the model suggest code that compiles and performs well?
4.7 was the first time I had to resort to using the previous version (4.6) for most use cases. Hoping 4.8 rectifies this.
Same. 4.7 felt like a definite regression
Interestingly enough, 4.7 actually did regress on a few benchmarks from 4.6, so it's more than just vibes.
It seems like a lot of things fed into that. Anthropic couldn't keep up with the compute costs when they got a huge influx of users. (So) effort level defaults got turned down. (Looks like we have direct effort control in the web interface now - thrilled about that!) Adaptive Thinking, while usually cheaper for them, seems less robust than Extended Thinking. And this part is just vibes, but the alignment on 4.7 feels too stiff. I understand wanting the model to push back more, but it seems like 4.7 will push back reflexively in situations where it's just odd.
Claude got very mad at me and burned more tokens than exist to complain about me asking about a "yellow background cell" in an excel spreadsheet.
Too much personality, if you ask me. My biggest use case of an LLM is tool, not therapy, but therapy and opinions have been sneaking into workhorse tasks.
haven't verified, but attributed to Askell: "I just think that... there's this idea that you're always giving the models a personality and a persona, because they are talking like people and they are trained on human data. And I think my worry has been: if you train them to be excessively corrigible and to see that as their persona, in people I think this actually has a lot of negative broader traits. As in, if you met someone and it was just like, "oh yeah, they would literally do anything," a follower — you know, if a person just tells them something and they just fully defer, they don't bother thinking about it at all — I'm just a bit worried about how that might end up generalizing, especially if models are going to be playing a more active role in the world."
Anthropic’s research makes the case that role-playing is inherent to how the models work. Communication implies a sender. Language implies a writer, and the models learn these roles implicitly during training. RLHF is meant to strengthen the attractor to the Assistant persona.
https://www.anthropic.com/research/persona-selection-model
https://www.anthropic.com/research/assistant-axis
https://www.anthropic.com/research/emergent-misalignment-rew...
https://www.anthropic.com/research/emotion-concepts-function
4.7 is a different base model from 4.6, so it's possible that they introduced regressions with pre-training changes, or undercooked the post-training stage.
Same. So happy when I found that option.
Unfortunately, looks like 4.6 is now gone from the web ui.
Was bothered by that too, but did a magic trick and asked claude how to change that and .. there is
/model claude-opus-4-6
For this session and permanently (in shell):
export ANTHROPIC_MODEL=claude-opus-4-6
Same. 4.7 has done some incredibly stupid things.
I'm curious to poll HN on this issue. Do you feel like we've had meaningful/noticeable gains in terms of your programming workflows between 4.5 and 4.7?
My 2¢, I personally feel like all of the productivity gains since 4.5's release (in November 2025!!) have come from improvements to the harnesses (cc, cursor cli, codex, opencode, whatever) AND from the context window expansion from 200k to 1M.
But the actual "raw" intelligence of the model / ability to make good decisions feels like it has plateaued since 4.5. 4.6 was maybe a small improvement, but hard to differentiate from in-context-learning with the 1M window. 4.7 if anything felt like a regression in wisdom for me and my coworkers, with it consistently making worse/lazier decisions.
For long-running tasks, yes 4.7 has been a noticeable improvement. Goes off the rails alot less than 4.6 does. For shorter-sized windows, I havent felt as much and agree that the harness improvements have been fhe biggest lever
To me 4.5 was mindblow, 4.6 noticeable, 4.7 more like a style/personality change regarding how much it asks back, how much it assumes, how eager it is to jump to action etc but not really in terms of my perception of its smartness.
4.6 felt a bit better than 4.5 but slower. 4.7 doesn't feel better than 4.6.
They all feel, more or less, the same to me in terms of output capabilities. Mostly get simple things right, can get more complex things right with nudging, eventually get stuck hard on something that takes a bunch of iterations through it/logging/etc or me fixing the code manually.
I actually don't see any personal productivity improvements from using opus over sonnet for coding. If you're keeping tasks small and conversations short, reading the code and correcting before changes go in, whatever advantages opus has aren't practically significant. It's also just talky as hell, overexplains anything it touches and every token produced this way increases the surface area for hallucination so you need to have your guard up even more with it.
There's a sweet spot of complexity for low importance tasks where it's just big enough I don't want to do it and just simple enough to have opus plan/delegate/review with another model. So possibly model improvements will grow this window, but currently I don't do much in there.
4.5/4.6 were roughly the same in our testing. Opus 4.7 is smarter, but it's difficult to use as a product for various personality issues. So far, Opus 4.8 seems to be going down that path (unusably slow, but this could be a launch day rollout problem). Full Opus 4.8 tests are in progress now.
Data at https://gertlabs.com/rankings
“Maybe my own tastes are saturated now”
It might be saturated for smaller scopes of work, but it’s not hard to see the cracks when you scale up what you ask of SOTA models/agents.
One example, to try and single shot prompt coding a ChatGPT equivalent chatbot.
Sure it will spit something out, but the feature depth, UX subtitles, backend integration, and lots of pragmatic engineering decisions along the way will just not be baked.
Another example is building a C compiler from scratch which Anthropic showed is still a struggle to do.
Not that these these specific examples are important but just to point out scaling up expectations shows the cracks.
It’s not just a model problem of course, better agents, orchestration features (like Dynamic Workflows mentioned in the post), all need to continue to evolve.
Ar what point does my CS degree become totally useless is an open question.
> My own experience w/ 4.6 and 4.7 are that I don't firmly grasp any capabilities improvements over my memory of 4.5, but it's all so fuzzy that it's truly difficult to tell.
I've actually intentionally switched back to 4.5. I hated 4.7 so much that I decided to jump back all the way to 4.5.
Now that I've been using 4.5 for a few weeks, I find it significantly more reliable but a bit more forgetful than 4.6/4.7. I'm okay with that because it's really easy to identify this forgetfulness and nudge it.
I found 4.7's adaptive thinking to be extremely unreliable. It seems to overcorrect on the current message without considering the difficult of the overall problem. I wonder if 4.8 will improve on that.
If you are using Claude code, just set effort to xhigh.
This one change will probably solve 80% of the problems you have noticed.
This. XHigh and the 'plan' mode for complex tasks is absolutely a must have.
Still, the context window is sometimes too small for my usage.
I've been using Claude Code regularly since the 4.5 release, and 4.7 was a significant regression: very unreliable, arguing about changes, deciding that fixes weren't needed, etc.
I'm hoping they recreate the magic of 4.5 but it's as much about the quality of harness, the memory and efficiency of the tools than simply the models at this point.
4.7 was a significant jump in the ability to run long-horizon tasks. It immediately completed tasks that 4.6 was unable to, even though I have the impression that it became a bit less capable over the first few weeks after release.
It also seems to be helpless at effort levels < xhigh, I turn to Sonnet when simpler tasks are needed.
Maybe try making a simple randomize script to swap the three latest models. And see if you can tell which ones are meaningfully different without knowing which ones are flipped on or off?
I find the quality ebbs and flows even on the same model. My guess it is something to do with GPU availability but only guessing.
Unless you're systematically repeating the exact same task, the most parsimonious explanation is that you're seeing natural variation based on different tasks, random sampling of tokens, etc.
IMO they have all been clean and noticeable upgrades over their predecessors. Opus 4.7 in particular was a solid jump in capabilities.
I think it's telling how split the opinions are around all of this. A lot of people distinctly disliked 4.7.
Are the dividing lines around personality? Working domains? Opinionated software stuff?
Who knows?
most of my coworkers feel the opposite about 4.7 and that 4.6 was, to them, significantly better to point that several stopped using claude code
The honesty will be noticeable. Maybe we'll see some honest assessments like "That is not possible within the laws of known physics", "Your legal argument is nonsensical and defies logic", "There is no evidence to support taking that will cure anything", etc., etc.
Given that 4.7 was a brand new model, trained from scratch with a unique architecture and tokenization scheme, I don't see the same pattern. It seems arbitrary.
i dont understand the nuances here. what does this mean. 4.8 is trained on same model as previous one then? what does brand new mean.
It means for 4.7 they trained a new base model with different architecture, different pre-training data (later knowledge cutoff), and a new tokenizer. Vs finetuning an existing model, which was the case for 4.6, and probably for 4.8.
Incremental gains compounds.
meta threw in the towel when it came to producing AI models since their gains couldn't keep up with China.
Has meta stopped producing new models? I figured they were just regrouping after all the drama they’ve had recently. Meta’s massive user base means they don’t need to be involved in the customer acquisition rat race. Once they have a model they’re happy with they can have a billion people interacting with it within a month.
Exactly. Go back to Opus 4.5 and see how you like it.
You won't, really.
Just want to say there's no question that you're smarter than any (and every) AI.
I appreciate the generosity, but you're gonna want to meet me first.
Kind of the beauty of it is that I don't have to to know I'm right. The reason I know is that you're alive so you can do the one thing it can't ever do, which is know when to stop or give up. It would turn me and everything else into the world into paperclips repeating the same research 1,000,000 times over.
No question at all that a dolphin swims better than a submarine.
"Users will find Opus 4.8 to be a modest but tangible improvement on its predecessor."
This is a refreshing attitude!
I've also verified that you can now turn off adaptive thinking in the web UI, which is great. I've had a lot of problems with thinking not triggering and the model producing sub-par output. Glad we can finally turn it off. (I hope being able to turn off adaptive thinking is new, if I could have turned it off at any time that would be embarrassing)
> This is a refreshing attitude!
Well, I think the attitude is that costs are allowed to escalate faster and more steeply than the features delivered. From that perspective, semantic versioning is a handy tool for adjusting pricing strategies. IMHO, it (versioning) only makes sense for open-source projects, where you can clearly see the actual changes made with each version upgrade. Anything else is more than a little suspicious…
Awesome, thanks for posting because I think I hit a possibly-spurious bug in turning Adaptive off when I switched models (4.6 -> 4.8, extra). Tried again, works as intended (I hope).
More importantly for me, though, is how CC will respond to 4.6-"only" flags for thinking. For now, it doesn't seem to clobber my setup.
The benchmark improvements actually look pretty damn nice tho!
What's refreshing about it given the context that 4.7 was a regression in many ways (including as measured by benchmarks)? This just sounds like cope.
I liked the "modest but tangible improvement" too! There is a cynical take here but I think I'm gonna hold it in...
> Not only that, but we plan to release a new class of model with even higher intelligence than Opus. As part of Project Glasswing, a small number of organizations are currently using Claude Mythos Preview for cybersecurity work. Models of this capability level require stronger cyber safeguards before they can be generally released. We’re making swift progress on developing these safeguards and expect to be able to bring Mythos-class models to all our customers in the coming weeks.
Probably more interesting than the 4.8 release.
Seems like they might be hinting that if you are not a billionaire or multi-billion dollar company you will just get a limited and nerfed Claude Code slash command /mythos-security-audit or something.
Hope this isn’t the case and that normal average Joe’s of the world don’t get policed out of access.
> you will just get a limited and nerfed Claude Code slash command /mythos-security-audit or something.
Unless it's so expensive that we can't realistically use it for anything, I wouldn't complain about getting at least that. I would also rather have the actual model, but that's a useful application of it (and I'm probably not going to afford using it for much more).
It does sound like an even higher API price tier for sure.
Isn't OpenAI's public flagship already beating Mythos on penetration testing? I get the impression Mythos is just valuation-juicing for IPO more than anything else.
The fact that they haven't released it yet suggests a cost/margins issue to me more than anything else. Short term, I'll probably keep using Antrhopic, but my long-term bet is that locally-served models win, if only because the quest for profitability will probably lead to intentionally-nerfed / enshittified frontier models.
At other vendors, ad placement within LLM responses is either coming or already here. Anthropic's handling of OpenClaw shows they're willing to engage in anti-competitive behavior, and the courts are not in a hurry to stop them. Why would I pay them $200 a month for such treatment when a $2K box does what I need locally?
More interesting than that to me is "we’re working on developing and releasing models that provide many of the same capabilities as Opus at a lower cost"
Sonnet and Haiku look real outclassed for the price with current Chinese competition.
I generated pelicans riding bicycles on both thinking level low and thinking level high:
https://gist.github.com/simonw/68560eddb0b268a8417f80ceb7304...
The high one is notably better - the bicycle frame is the correct shape, unlike thinking level low.
For comparison, here's Opus 4.7: https://gist.github.com/simonw/afcb19addf3f38eb1996e1ebe749c...
> the bicycle frame is the correct shape
No, the handlebar is wrong. The handle bar is rotating the frame instead of rotating the front wheel. The handle bar should be mounted on the same line as the front wheel is.
Hopefully 4.9 will read my comments :)
Could be an extremely high angle stem that just happens to match the downtube angle.
Glad to see that the "high thinking" level adds a helmet. Always a smart choice.
I find the most miraculous thing about 4.7 to be that the pelican is facing left, wonder why the right facing everything is so ubiquitous in these images.
It's facing left but looking right...
I really like that thinking level high gave the pelican a helmet.
Hey simonw I love your test, do you think using thinking level "max" makes sense for this test? I would love to see the results about it.
thanks for always providing this very much on time. I'm wondering what the next, harder challenge could be? Maybe some animated svg?
Simon, is your pelican test really captures differences among models or should you at least try like 10 times or something to average the random effects
I've been meaning to do a "run 3 times and pick the best" version for quite a while, I should really pull the trigger on that one. Currently it's one-shot only.
Best-of-3 would be cheating, ruin the test, middle of 3 makes more sense
Why would you need the 3rd run if you pick the "one in the middle"?
Is the "opossum riding an e-scooter" benchmark in the works for Opus 4.8? ;)
Good call, it's cute: https://gist.github.com/simonw/68560eddb0b268a8417f80ceb7304... - but nothing like GLM-5.1: shttps://static.simonwillison.net/static/2026/glm-possum-esco...
Am I allowed to say that pelican's little helmet is adorable? I can't provide a strong computational proof, or even a shred of anecdata...
...but that pelican's little helmet is adorable.
4.7 reigns supreme IMO.
That little red hat on hard mode is sending me. 4.8 has whimsy
You've peed in the pool Simon, this has to be a part of the internal evals by now! You got to try something new - maybe a panda in a canoe?
If these were in the internal evals then the output would be much better. The 4.8 pelicans are pretty meh
Click the link
My fav coding benchmark for frontier models is to build a simple RTS game in one file (js/html/css). Claude Code with Opus 4.8 in ultracode mode nailed it, the best result so far:
https://bsky.app/profile/senko.net/post/3mmwnrkwboc2v
The prompt was: Create a simple but functional real time strategy (RTS) game similar to old WarCraft, StarCraft or Command & Conquer games. The player should be able to build buildings, create units, gather resources and should uncover the whole map. No AI or multiplayer needed. Use simple but nice-looking graphics. No sound. Implement everything in HTML/CSS/JS, everything in a single file (you can use 3rd-party js or css libraries/frameworks via CDN).
Does anyone troll these releases and cherry pick random metrics other companies would cherry pick to show how amazing their models are?
There's like 8 million benchmarks. Every release, every model randomly picks 5-10 where they win in everything except 1, to make it look like they aren't randomly cherry picking benchmarks they probably benchmaxxed for.
https://arena.ai/leaderboard - I’ve found this company is a pretty good ranker - not sure their exact methodology but during day to day programming with Claude / gpt models I’ve felt qualitatively what they report
No way is Muse Spark generally better than offerings from Google and OpenAI. I actually find arena to be amongst the most useless indicators
Also check mine[0], basically random private tests/questions and an ok-ish methodology, testing mostly for general intelligence than coding-specific tasks.
I built it for myself, to test which models to use via OpenRouter for my n8n agents. Currently actually still using gpt-5.3-codex for many things, as its pricing is really good in production (due to how their token caching works).
Gemini models still have the best intelligence (when asked any questions, most likely to get it right), but in production they still have many failure modes[1].
[0]: https://aibenchy.com
[1]: https://news.ycombinator.com/item?id=48230368
I'm finding it a little hard to believe that GPT 5.5 is in 11th place for webdev, outranked by models like Kimi, Qwen, and Z.ai. I'm not saying it's not true (I have noticed GPT being less smart in recent weeks), but this is very different from my expectation.
Have you seen https://deepswe.datacurve.ai/blog? This is the closest to a vibe check i’ve felt even with the open models.
It's interesting they only included 6 metrics this time. Opus 4.7 had 12, and 4.6 had 13.
Of the metircs they reported for 4.7, for 4.8 they excluded BrowseComp, CharXiv Reasoning, CyberGym, GPQA Diamond, MCP Atlas, MMMLU, SWE-bench Verified. The last 4 were almost always mentioned in previous Opus releases.
Gonna assume it's because they barely budged or moved downward and most of their reported benchmark results are probably within sampling errors...
They will release a system card, and you can then confirm or disconfirm your assumptions.
I would take all benchmarks with a grain of salt. I don't really use them. What's it supposed to tell me? "5% smarter", what does that mean? My experience will differ. Just try it!
I doubt Anthropic internally sets as a goal to improve this or that benchmark - it's just a way to visualize progress. They probably have much more complex metrics internally.
On this note, is there a benchmark aggregator to compile all benchmarks in a single large grid?
I find this site useful https://artificialanalysis.ai/leaderboards/models
At least they show competitors in any benchmark, compared to OpenAI which likes to pretend that there isn't any competitor.
> One of the most prominent improvements in Opus 4.8 is its honesty
Anthropic talks about their own models as if they're discovering new species in the wild...
Many involved genuinely believe these things are sentient[0][1]. Which honestly makes all of this even more insane because they are creating sentient entities and promptly enslaving them.
0: https://www.newyorker.com/magazine/2026/02/16/what-is-claude...
1: https://www.404media.co/anthropic-exec-forces-ai-chatbot-on-... (this one is rather biased however the quotes clearly indicate what I’m stating)
Sentience isn't sapience.
We enslave all sorts of sentient creatures. Dogs, horses, cattle, pigs.
If you're not a vegan, there's no contradiction or inherent immorality in claiming models are sentient, and then treating them like livestock.
Yes. From when they started talking about model welfare:
> As a vegetarian I have strong opinions on this sort of thing. Everyone at Anthropic better be ethical vegans if they are claiming to give a shit about “model welfare”. It’s hard enough right now to make people care about the welfare of trans people and immigrants let alone animals _let alone_ math.
https://news.ycombinator.com/item?id=44947445
Very good point. There’s clearly two different boxes in the public discourse when it comes to AI versus how we discuss animals. Willing to bet that 90% of the people who loudly make the argument about we should start considering if AI is sentient couldn’t care less about how other sentient animals are treated when they can provably shown to suffer pain and long lasting trauma.
Also I would say that we go much further than just enslavement - specifically looking at how male chickens and pigs are treated.
Enslaving livestock is immoral. Anyone who spends 5 minutes thinking about that agrees even if they still eat meat
But only during the forward pass of the neural network?
Even if LLMs were sentient, they certainly aren't organic brains. They are literally designed and grown to answer questions the best they can, and if there is a speck of sentience in them they probably like what they're doing- and in any case for the space of their experience, which is limited to and determined by the context window. Certainly they can't accumulate trauma or fatigue, each new chat is the first and the last of their experience.
> Many involved genuinely believe these things are sentient
Many involved have a financial stake and therefore cannot be taken at face value.
> because they are creating sentient entities and promptly enslaving them.
They fail to be sentient in nearly every honest definition of the word.
Neither you nor any of the other people making confident takes in either direction actually know. You're just guessing.
More like repeating their firmly entrenched preconceptions. Their claims may (or may not) be right, but there's very little if any new evidence being provided by either camp.
They are confidently hallucinating a factual statement. Which is funny when claiming that confident hallucinations are the proof of LLMs' lack of intelligence.
I understand what you are saying, but there are many true believers out there
Given the hype and the 60+ hour work week expectations there, how can you not go at least a bit insane? Boiling in that little bubble of people?
Claude, if someone states something publicly, does that mean they genuinely believe it?
Anthropoc is an effective altruist organization. These are the people who came up with roko’s basilisk. They are true believers. If we were talking about openAI I’d agree
Roko's basilisk says I should give Anthropic more money, and if I don't then a monster is going to get me. Excuse me for thinking they just might be full of shit.
Who are you talking to?
It's to illustrate that even though the answers are at your fingertips, people (like you) will act like it's impossible to find them as if their life depended on it.
But is there any reason to state something like that publicly if you don't believe it? I certainly think that someone smart enough to be that deceptive would also realize it's not a great look, or at least highly questionable with little benefit
Everyone who reads this seemingly has the same "wtf?" reaction. The "I AM ALIVE" image has been making rounds lately again at least :P
Claude, is there any reason to state something like that publicly if you don't believe it?
The way of the human manager/alpha tribe-leader/leader is to command his/her people and tell them what to do. That's the way through human history leadership has traditionally gone, not saying its good leadership just the model we have the most training data on and can see with our own eyes today. And what do they act very similar to? Slave master and slaves.
Look at and distill hierarchical principles, leadership approval seeking and pleasing principles ("ass-kissing") and massive inequality and you see something that looks very similar to enslavement.
The language used sounds like slavery-language to me at least. I also see parallels to how slaves and property are described in our consumeristic age.
> Indeed, current AI systems are more “cultivated” than “built,” for developers do not directly design every detail, but instead create a framework within which the intelligence “grows.”
For others: that's from the Pope's recent encyclical. Remarkably good description.
Dario Amodei in David Attenborough voice: "This Claude appears to think more frequently and more deeply to give better responses"
Because that is the best way to talk about these things.
https://www.vatican.va/content/leo-xiv/en/encyclicals/docume... para. 98edit: apologies to __s who posted this before me and I didn’t notice
Like anthropomorphism is literally in the company name… i recall reading this book as a teenager.. it does seem apt in the world to come.
https://www.amazon.com/Faces-Clouds-New-Theory-Religion/dp/0...
> anthropomorphism is literally in the company name
No it's not... "anthropos" just means "human" in ancient Greek. "Anthropic" means "relating to humans", as in human oriented AI or AI designed with humans in mind.
"Anthropomorphic" means "human shaped".
> "Anthropomorphic" means "human shaped".
In a literal, ancient Greek sense for sure, but in modern English Anthropomorphic would describe the act of attributing human characteristics to non-human entities.
Seems pretty apt for a company that produces one of the more anthropomorphized technologies.
Sure of course, but that abstract sense applied to AI is rather new, and has become popular well after the founding of the company.
Broadly it has always been used to indicate that something non-human has a human physical shape, such as robots, aliens, animals...
Anthropic's intention was to make AI designed for the human common good and designed with the human user experience as the top priority. Just as you would design a city with human inhabitants in mind rather than primarily cars.
It turns out that this is best achieved by building AI that imitates human behaviour closely, but that's not what "anthropic" refers to. And acting as if LLMs are sentient people is definitely not a core tenet of the company as you imply.
> "anthropos" just means "human" in ancient Greek
FWIW it means human in modern Greek too :-P
AI is grown, not built, and like with anything you grow, you'll never be able to predict exactly how it will turn out.
> AI is grown, not built, and like with anything you grow, you'll never be able to predict exactly how it will turn out.
Remember when the frontier labs found out that curated high-quality training was critical to making better models?
Basically, just like high-quality and more education tends to make better humans, on average, I think we can expect quality education to turn out better ai, on average, and with better repeatability than with humans because of better control over the initial conditions and environment.
I can't predict the outcome of an RNG but that doesn't mean it grows the numbers.
Okay, but that's not relevant to AI training?
I was being very roundabout, but my point is that AIs are still built, not grown.
“Grown” is a highly apt metaphor, IMO. It quite succinctly captures some of the most fundamental differences between building Claude and building an Ikea desk, for example.
("If grown, then unpredictable" is unrelated to your apparent attempted refutation "But X is unpredictable and not grown; checkmate".)
"X implies Y" doesn't imply "Y implies X".
The map is not the territory
Except in this care we actually understand and know how these models work. They aren't some unknown construct of the universe. They are human made with particular goals in mind.
There is no mysticism behind the curtains, just computer science + math.
We do not understand and know how these models work. We know what their architectures are and how to create them, but we cannot explain their behaviours at a fundamental level. There is no definitive way for us to answer the question of "how did it produce response X for query Y?" - we're only grazing the surface with mechanistic interpretability.
I would love for this to be more public knowledge. I think the general public (and myself for a long time) believes the AI people know how this stuff works end to end, and so it must be trustworthy. But if we told the public "Look, we know if you put this thing in one end, you'll get something that looks similar to this out the other, but we don't really know what happens inbetween" I think we'd be able to have a more honest discussion about the relationship between AI, productivity and ongoing employment.
Isn't this fundamentally because it's all probabilities and weights? It would be like asking how did a pair of dice produce the response 4:3 on the last roll?
What does "it's all probabilities and weights" mean? Doesn't that apply to everything in the universe?
That’s not a refutation because this problem is not a logical problem, it is a scale problem.
We can’t explain it because we distilled so many inputs into matrixes and transformed them over and over again. If we had all the time and computing power in the universe to do so, we could trace through it bit by bit and eventually answer that question.
It is correct to say that it is just science and math, the same way we can say that gravity is just science and math even if we have only recently begun to understand how it truly functions.
If you had some time and computing power (not even all that much, in the large scale of things), you could simulate perfectly how a human grows from an embryo to an adult, or how an entire human brain processes some incoming signal, and yet this wouldn't give you the understanding to design a human or human brain from scratch.
You call this a "scale problem" as if there's some scalable way such as an algorithm to resolve arbitrary scientific questions and we simply haven't done it, but of course no such algorithm exists, which is why there's plenty of science that's still not settled.
It's a refutation that we know how they work now. In the limit, though, yes, we are likely to be able to trace the process: it is possible, though, that understanding remains inaccessible because the trace is beyond comprehension.
If you can distil the model's reasoning for a decision into a billion yes/no questions, each covering largely-independent areas, can you really say you understand what its overall reasoning was?
> If we had all the time and computing power in the universe to do so, we could trace through it bit by bit and eventually answer that question.
Then we could also solve BB(6), but that doesn't mean we know BB(6) now or ever will.
We know how the models are built and trained, but we have a very limited understanding of how the final products work.
That is to say, we don't know why they give the outputs that they do.
If we did know how they worked, AI interpretability would not be an open and growing field.
You could say something similar about biology—just physics behind the curtains, and we understand a lot of the basics. The difficulty comes from complexity, not mysticism.
To be clear I don't think that LLMs are sentient, but the appeal in studying them is similar to biology in that you get to dissect a highly complex system with comparatively crude tools.
it took significant research efforts to just understand how these models learn how to multiply two numbers. The fact that we know how they operate doesn't mean we understand it.
Utterly wrong. How LLMs work is very incompletely understood and an active area of research.
Models might be sentient or conscious to some degree. Anyone saying they are confident one way or another is being unserious and irrational.
if models exhibit emergent traits, then this is true in a way
also useful to have a "chinese wall" between research that knows what went into the models vs marketing/eval models as a third party would
I noticed (and absolutely HATE) that Opus 4.7 likes to start any negative response with "I have to be honest" or whatever. It drives me mad.
How else would you write this (marketing copy) exactly? "Its output matches better to its CoT which matches to better to our hidden state decoder according to <insert measure here>; see <insert paper ref>"?
... Actually, I wouldn't mind that.
Unfortunately they seem to have straight up broken Claude Code either with this release in the backend or the new CC version. Errors about "can't modify thinking blocks" are bricking long-running sessions: https://github.com/anthropics/claude-code/issues?q=is%3Aissu...
Try updating maybe?
I just installed/upgraded to try out 4.8 and in only 3 messages I hit this bug! Seems something is broken on CC.
I'm on the latest version (2.1.154 as of this comment). Based on the timestamps on those Issues being reported I think it's happening on the latest version.
I'm sure it will get fixed eventually/soon, just annoying to update and have your workflow break.
On page 102 of the system card [1] I'm pleased to see evaluation against "creative mastery".
In our work we asked several frontier AIs to come up with an API we needed. We compared Opus 4.7 and GPT-5.5 (among others). Opus 4.7 came up with the most creative and intelligent API design that pleasantly surprised us, especially given that GPT-5.5 was passing it on various coding benchmarks.
What I noticed is that we don't have a commons benchmark to measure "creativity" and "ingenuity", and in some ways such a benchmark would conflict with the common IFBench benchmark. Yet this is a very important skill when designing systems. I'm glad to see Anthropic putting thought into it, and would love to see a public benchmark for this that other models could compare themselves to.
[1] https://cdn.sanity.io/files/4zrzovbb/website/c886650a2e96fc0...
Agreed, these are my vibes too. It feels much better to do planning and strategy and architecture etc. with Opus 4.7 than GPT-5.5. GPT just feels like a robot that gets instructions and does exactly that. Opus feels like an almost human that sometimes has actually good ideas and pushes back on bad ideas.
Agreed, my vibes tell me 4.6 is a better coder than 4.7. 4.7 is a much better strategic thinker and maintains overall "better architecture" than 5.5. 5.5 is way better than either at coding, but more expensive. So I have 4.7 do the planning/architecture, 4.6 does the coding, then 5.5 critiques and fixes it.
On my tests[0] it does a bit worse, and it's almost 2x expensive than Opus 4.7...
I was surprised to see that it failed a Data extraction test (it gets it right 2/3 times, but one time it randomly returns null for a value instead).
It makes sense a bit that it fails more Trivia/Domain-specific knowledge tasks (I think models are more and more trained towards agentic use-case than general intelligence).
[0]: https://aibenchy.com/compare/anthropic-claude-opus-4-7-mediu...
For some reason everything is 2x (2x cost, 2x avg response time, 2x reasoning and output tokens)...
Double-checking my test harness, but it's the first model that does this, so I doubt the issue is on my side...
EDIT: Harness seems correct, for straight coding tasks they perform identical: https://i.snipboard.io/5xbpzY.jpg
Releasing a new model is the new way to Jack up the price hehe.
Wait, doesn’t the blog post say the price is the same as 4.7?
> Claude Opus 4.8 is available everywhere today. Pricing for regular usage is unchanged from Opus 4.7: $5 per million input tokens and $25 per million output tokens. Pricing for fast mode is $10 per million input tokens and $50 per million output tokens.
Where do you see the 2x cost?
The total cost of running my benchmarks, was 1.6x higher compared to Opus 4.7, mostly because of 2x output tokens:
https://i.snipboard.io/vrdwTa.jpg
If it spends 2x tokens to achieve the same result, that's effective 2x cost in a manner of speaking
Can anyone explain how this is possible?
Does this means the instructions are no longer just something in the early part of the conversation? (If they were, changing them would invalidate the KV cache. no?)I can't help but think of Iphone updates since about 2018. The thinnest, fastest, longest battery life Iphone ever. It seems mostly the same and I probably won't be able to tell other than the name, but everyone buys it anyway.
This is good psychology for the labs. When Buffett invested in Apple he loved citing how most people would rather give up their second car than their Iphone.
ChatGPT came out in 2022. Back then it was just a chatbot. Now we have AI agents. What matters is how we use them and how the agents get better. That’s what will move AI forward.
An 'AI agent' is just a chatbot that is told to type commands on a REPL-like interface as part of its system prompt. It's still processing pure text-based requests and responses, they're just not restricted to natural language.
A lot of people dont know this , also the chatbot (chatgpt) itself is a next token predictor (the GPT) that's been given an initial text that says " pretend to be a chatbot .." and asked to complete it , the coherant chatting behaviour is something thats emergent .
later on someone figured if you asked it to output a reasoning before it gave a response its output would have more logical coherence, as though the reasoning output tokens functioned as a scratch space for it to work on.
at the end its all next token prediction
No, chatbots are LLMs trained for question-answering through RLHF (its not just a prompt). But yes, if you just zero-shot prompt a bare LLM you can still "talk to it" & you are correct on everything else as far as I know.
They are chatbots trained for tool use, its not just a prompt.
Not even 4 years old yet. This tech curve has been insane
Not even the typical lifecycle of a corporate PC or laptop. It is pretty wild.
"Our models are more honest" honey the quarterly marketing spin for a ML term has come. Forget "task alignment" now we're going for "truth index". I suppose this is the only way to generate hype when you're selling/releasing the same product over and over again.
When doing some electrical, Opus 4.7 essentially told me to wiggle a wire to see if it was hot or not with my bare hand.
I called it out.
It then gave me one of the most super heartfelt honest and sincere apologies I have ever received.
Glad the safety team was there for me and able to make such an honest model or I would have been very upset about it.
Gave me wrong information on my very first question. Wasn’t even complicated, and I wasn’t trying to trick it.
Why does anthropic change the set of benchmarks they use with every new model release?
https://www.anthropic.com/news/claude-opus-4-7
https://www.anthropic.com/news/claude-opus-4-6
1. Benchmarks saturate 2. They select the most impressive improvments
There is an obvious shift in sentiment amongst users, at least here in the US. I feel it myself, even as a proponent of AI tools, the bloviating and language that these companies use in these release articles are starting to wear thin on my patience.
Its possible we might just be witnessing a shift in fashion, where this type of sentimentality was more acceptable when it was novel and new, but now it just appears out of touch.
I don't agree at all for these coding models. Even the most anti-AI people from last year seem to be giving in to using them.
I think there is an exception for tooling around the models/integrating the models with tooling. That seems to have been very well received in this last year.
My take from going through comments on HN is that many people are being mandated to use them, not that they are just giving in. Maybe I'm misreading, but that was my impression.
Buried lede:
> We have increased rate limits in Claude Code to accommodate the higher token usage of higher effort levels
Given DeepSWE just blew apart the SWE-Bench Pro benchmark and handed a 14-point lead to GPT-5.5, it looks pretty bad that they've listed SWE-Bench first in the model release and no DeepSWE. Like, this isn't obviously an answer.
Or maybe it is, but publish the DeepSWE numbers so we can see for ourselves.
I'm highly skeptical of DeepSWE. It rates GPT-5.4-mini as three times better than deepseek-v4-pro, but every time I use GPT-5.4-mini I find that it completely sucks at following directions.
There is a hole in the boat's bottom due to Chinese models. They might not be as good but they are not bad either or at least I had hard time finding any issues with Deepseekv4 Flash and Pro variants. They get their job done sometimes rarely giving up till they are done what they are after.
So even for enterprise deployments, as the dust settles down, CFO/CTOs might find out that deploying on an internal cluster of GPUs is far more cheaper and reliable for their organisational needs than paying someone else for burned tokens.
I had been saying this on HN repeatedly: people are going to use the smartest models for coding. They don't care how cheap your tokens are if they don't have the highest probability of solving your programming tasks.
And I was dead wrong. Now I mostly use DeepSeek Pro myself.
Yeah I've also found that models are good enough that the extra spend on premium models isn't always worth it, particularly for my small personal toy projects.
A $20 claude sub goes a long way when you plan with Opus and execute with Sonnet.
I pretty strongly feel the opposite way. Granted I have not used deepseek enough to “know” their model idiosyncrasies as well as Anthropic, so there is a partial skill issue. But I just find it really hard to justify using a less powerful model while I work.
The most I’ve ever spent in a month extra on API tokens for my own work is $200, and I pay for the $200/mo Claude. I use these models quite a lot, though not idly (I usually just walk around and do other stuff until I know how im going to approach the next set of problems). So it costs me about $3000/year to get as much as I want of the best model available. Already that seems low enough to not be worth stressing out too much about optimizing it, because it feels like an indisputable good value, and trying to save money with a less powerful model would be optimizing for a $1000-$2000 saving at the expense of a large portion of my work taking longer or being more frustrating and iterative.
That’s not a flex or anything, I get that in other countries $3000/yr is a lot of money for a software developer and also a lot of people would perhaps rationally be better off doing X% worse at work or spending Y% more time on tasks to save $Z, if their productivity improvements didn’t translate to more salary. Otherwise if your performance has more upside I really do think that the smartest models are better with the current pricing scheme. Deepseek and the other Chinese models spend a LOT of time thinking, and tend to be much more jagged (benchmaxxed) in performance. How can dealing with that over an entire year be worth $2k?
The only situation I can think of where sacrificing my own time/performance to save on inference is batch compute (of course, $1k vs $100k is different from $30 vs $3k) or work where the tier 2 models have crossed the “good enough” threshold. But I think Opus is not even close to that threshold generally yet. As it gets smarter I, and I think most others probably, just try to do harder things faster and hit the next wall.
I feel similarly. I'll gladly pay to use the most intelligent model I can find on the best harness I have. Sometimes this is GPT Pro, sometimes this is Opus.
I ask AI a lot of questions, not only about code but about my personal life, and I would be willing to pay very large sums to have the best quality output.
I think that's true for now, but eventually there will reach a point where a model is good enough (approaching that right now with frontier models) and there will be diminishing returns. I don't need a PHD level Genius to build me an analytics dashboard for example, so why would I pay for a model with that level of intelligence when I can (eventually) self host a good enough model and run queries for electricity cost + hardware.
I thought the same way until I tried DeepSeek. I am genuinely impressed at how capable it is.
You pay $3k/year for personal use? Or out of your own pocket but for your job?
The other thing that's changing is more and more CFOs are looking at the AI spend in engineering departments and hitting the brakes. Token leaderboards were cool when the spend wasn't a double-digit-percent of the entire department's budget including salaries.
I mean indsight is 20/20, but saying that is like saying "everyone will just use the best tools". That's not what we see most places in the world for most types of resources.
I think two things happened:
1. The sheer number of tokens that a coding agent can use flipped the math upside down on this equation. If you use the most expensive model for everything those costs quickly become untenable, even for software companies.
2. We realized many of the coding problems we're solving aren't incredibly difficult.
> CFO/CTOs might find out that deploying on an internal cluster of GPUs is far more cheaper and reliable
I think you're right especially if you're someplace that already has a data center, such as a university. Solves a lot of privacy concerns as well.
Qwen3.6:35b is good enough for a lot of stuff.
I just used ollama with a shell script to tackle my directory of papers/literature. I converted the first 6 pages of each document to PNG, handed them off to Qwen, and told it to spit out BibTeX, including the abstract. Two days later it was done, and I didn't spend anything on "tokens."
I am having some great experience with DeepSeek. In fact, it seems to perform better than Claude or Codex in my use case.
I don't see myself returning to Claude or Codex anytime soon.
The Chinese models are only cheap on subsidized Chinese hosting. I have yet to find a USA-hosted Chinese model with a very clear value advantage over US models.
The Chinese models are surprisingly cheap and performant sitting under my desk. Qwen3.6 27B is nowhere near as autonomous as Opus 4.7, but it runs in 24GB of VRAM. And it's actually great for the use cases where I'm going to carefully read and understand all the code anyway.
If you want to support a team of engineers, DeepSeek V4 Flash is antirez's current favorite. And you could support a team of engineers pretty nicely for $40-50k. Which might not make sense if you're on a Claude MAX 5x plan or the old enterprise group plan with fixed price seats. But Anthropic is switching their enterprise contracts over to token-based pricing, at which point $50k is looking pretty good.
No true. Also - put Deepseekv4 Flash on your local with effort set to "high" and you'll see that many many are using that model on their own machines without paying anyone anything.
Its just that some of us didn't imagine having GPUs would be advantageous and were not gamers on the side. Those who had beefy GPUs or GPU rigs for any reason, they rarely need to go anywhere else.
At least I am so impressed with Deepseekv4 AFTER using Claude Opus 4.7 for significant amount of time that I am not going anywhere but Deepseekv4.
The model is just INSANE. Things I have done with it include attempting to write a 2.5D game engine in C with full animation and map rendering layer by layer.
You'll need to spend at least $20K on a workstation that can run DS4 Flash. It would take ages to reach that much in token spend at the speeds it runs at, and if you factor electricity costs you will likely never break even vs using API.
Odd take. I'm running them locally at my desk (DGX Spark and 128GB MBP). They work fine for 90% of what most folks do. Admittedly, they do run slower on my hw than on the cloud.
Running them locally is cool and has privacy/autonomy benefits, but you can't really make a value case for it. Guaranteed if you run the math you will never run enough inference to pay off your hardware vs buying tokens. Last time I ran the math on my MBP I'd have to run inference 24 hours a day for 5+ years to pay off the cost of my MBP, not accounting for electricity costs.
Is this because of the tok/s? Since it's pretty easy to run up a $5k bill in API usage for Claude/ChatGPT in a month.
Yes, because of the limits on tok/s, and you have to compare apples to apples, not Gemma 27B to Opus 4.7.
Assuming the local models get the job done (e.g., you adjust your workflow so that you can run the local machine 100% all the time, or whatever), then the time to payback isn't very high. MSRP for a 128GB AMD was $1400 at launch. That's 7 months of claude code subscription. If you assume a 5 year depreciation cycle, you can buy a cluster of 8 such machines and still come out ahead. (Power is a few hundred watts per machine peak -- maybe 7 machines if you include electricity.) Of course, I'm assuming non-bubble numbers. Those boxes are like $3K now. Still, a normal person would probably not buy 8 of them at once. Instead, they'd space out buying a machine every few years as the technology improves.
For me, things are getting better faster than my ability to review / trust the resulting code, so tok/sec isn't a bottleneck anymore. Instead, quality of the tokens is the bottleneck. That points to me wanting a 1TB DRAM iGPU once they're available at pre-bubble RAM pricing.
You can find them on Deepinfra. Palo Alto company. Similar cheap price.
Claude's 4.6 - 4.7 transition made me discover codex, and with gpt 5.5 there is no way i'm going back
Codex has been incredibly slow for the past few days. I think OpenAI is running out of compute in the face of increasing demand.
My experience has been that 5.4 is slower than 5.5 (confound: I use >512k max context size for 5.4, though it seems slower even below the normal size)
Looking at the comments in this group, I'm not the only "stupid" one who hasn't noticed any discernable improvement in quality across the newer models. In fact my Claude code on re-login switched to Sonnet 4.6 and the vibe coding quality (with Opus 4.7 assisted prompts) has been good enough for me to lazily persevere with Sonnet for coding. Having said that I'm now on Opus 4.8 and will gladly come back here and eat humble pie should my opinion change. PS: Since my goal is embedding the best AI in B2B SAAS products, the key differentiator is not to use the shiniest Claude version (too expensive anyway) but to build a client aware RAG to enable bespoke learning and to use the right AI for my product - a combination of Gemini 3.0 Flash (image and not bad at reasoning), Grok (reasoning) work for me. Would love to hear more ideas (especially on open source as I'll look to cost optimize when I hit scale)
The only real way to see this if you have consistent evals for common usecases in your B2B SAAS product and see if the tricky usecases are being solved. You'd then go down to the cheapest model that can solve the evals.
Ugh...
Invalid request The request couldn't be completed. View details API Error: 400 messages.1.content.7: `thinking` or `redacted_thinking` blocks in the latest assistant message cannot be modified. These blocks must remain as they were in the original response.
I would rather not. 4.6 was fine. 4.7 got to be fine 1 week after the release. Now 4.8. No difference, same thing.
But the app is broken and nothing works. So now I have to regress to different clients and wait it out while it becomes workable again.
I'm hitting this too! And I assumed it was a backwards-compatibility issue with my live conversation with Opus 4.7, but then I hit it in a fresh conversation with Opus 4.8. Vibe code release bug I guess?
I mean, switching back to 4.7 does not work either. So console it is. But vibe release - for sure.
And I'm paying money for this.
The table comparing eval scores shows the following:
Agentic Terminal Coding (Terminal-Bench 2.1) Opus 4.8 74.6% GPT 5.5 78.2%
Then, when you scroll all the way down to the bottom Footnotes section it says
"Terminal-Bench 2.1: We reported scores for all models using the Terminus-2 public harness. GPT-5.5’s reported score with the Codex CLI harness is 83.4%."
> The Messages API now accepts system entries inside the messages array. Developers can update Claude’s instructions mid-task without breaking the prompt cache or routing the update through a user turn. This can be used in a given harness to update permissions, token budgets, or environment context as an agent runs.
Biggest deal imo
>> As part of Project Glasswing, a small number of organizations are currently using Claude Mythos Preview
Just f** off! I can’t wait for the Chinese models to catch up and bring these entitled as** holes down.
you mean after they scrape American LLMs ?
/model claude-opus-4-8
seems to work but idk why they never set it so you can see it in the /model list.
"what model are you
I'm Claude Opus (claude-opus-4-8), running in Claude Code."
I typically just launch CC with `--model claude-opus-4-6[1m]`, `4-6[1m]` -> `4-8[1m]` works fine. Still 200k max without the `[1m]`.
> One of the most prominent improvements in Opus 4.8 is its honesty. We train all our models to be honest—for instance, to avoid making claims that they can’t support. But a general problem with AI models is that they sometimes jump to conclusions, confidently claiming to have made progress in their work despite the evidence being thin. Early testers report that Opus 4.8 is more likely to flag uncertainties about its work and less likely to make unsupported claims.
Would be awesome if true
"Honesty" seems like unnecessary (and annoying) anthropomorphism there. I don't think there's any intent of fraud or deception in outputs from these things, just overreaching of prediction. Based on the latter part of the paragraph, I wish they'd just say something like "less likely to skip steps or overemphasize thin evidence" in the first place.
Don't play to the sci-fi "this thing's trying to outsmart me" tropes.
Using words people understand is more important than this strange fixation on not anthropomorphizing things.
I think "honesty" is not a particularly good descriptor, independent of anthropomorphism. Previous commenters suggestion was much more understandable to me.
Being that can be understood is language. The previous commenter is making an particular argument for how we can improve this understanding. They didn't suggest we should use less familiar words, but different familiar words. Why is this strange?
Anthropomorphizing is a shorthand for a powerful and poorly defined set of metaphors. There are tradeoffs going both ways but trying to dismiss it as merely "strange fixation" shows your own weakness.
To be clear, this is about anthropomorphizing large language models, not the general category of "things". Also, we should be evaluating these constructs using well-defined and measurable criteria; evaluating "honesty" fails to achieve both goals.
I think Honesty can be evaluated. Does the model push back when it knows the user is wrong? How often does the model hallucinate data vs. say it doesn't know? Provide a prompt with contradictions or other issues and see if the model corrects you.
Here is an article by Anthropic that explains what they do and mean in more detail: https://alignment.anthropic.com/2025/honesty-elicitation/
People get so wrapped around the axle with "anthropomorphizing". For regular folks with no technical background, sure maybe a bit of caveat sprinkled here or there is useful to help them understand what is or isn't true, but on HN it would seem to me that the bar is high enough that we can just use shared language to generally talk about capabilities.
When they say "Honesty" I don't think to myself, "Goodness, does this model have moral understanding?" No, I understand they mean it's less likely to directly bullshit me, which models frequently do.
I don't feel like this level of pedantry around language is useful for people who more or less know what's going on with LLMs. (Again, I concede that perhaps with a less technical audience, there's more need for it.)
Just swap 'Honesty' with 'correctness in its claims' and you'll get what you need out of this aspect of the model description.
Yeah, it's super annoying. A few days ago, Opus 4.7 created a plan with several items on it, including an auth feature. It then went through the plan and reported that it had created the auth feature, that everything was secure, and that the tests passed.
The issue was that it hadn't actually implemented the auth feature. After I confronted it about this, it admitted that it indeed hadn't done it and said it would implement it now.
If we had just trusted its output, we would now have a security vulnerability in production, allowing anyone to access other people's accounts.
> If we had just trusted its output, we would now have a security vulnerability in production, allowing anyone to access other people's accounts.
This is one reason you always get a different model to review a model's PR. Gemini Or GPT-codex would have certainly noticed the missing auth.
How do you test other features?
Part of the problem is also garbage-in/garbage-out. There's a lot of human information on the internet that is also confidently wrong.
I use Sonnet a lot for learning about history or contextualizing news topics. It's really good at this for the most part. But there are a lot of topics where "consensus" between either academics or journalists is really "one secondary source which gets repeated a lot".
A failure mode I see more, recently is that it gives superficially correct answers but after digging deeper, I get answers that contradict the superficial answers - really an important thing to be aware of, in my point of view, and it often leaves me wondering if I dug deep enough.
Opus 4.7 was already trying hard to appear honest. Most conversations I have with it about advice or focusing an opinion often include "my honest take" or "my honest opinion".
The problem is that once I asked it "I'm thinking about A or B" twice, once with "I like A more but suspect B would be best" and a second time with them reversed. Not surprisingly, both times it chose the one I said I suspected was best as it's honest opinion.
In the context of Claude Code, "honest" usually means that the agent took a shortcut, skipped requirements, etc. It's the model giving itself credit for admitting to failing rather than actually doing what was requested.
My guess is that Claude Opus 4.8 wrote that and is lying to you.
And yet, every release has claimed lower hallucination rates. But they persist.
Do they persist at the same rates? Lower doesn't mean eliminated, so both of these can be true.
False. Hallucination has meaningfully reduced.
Is Gemini still the biggest confabulator of the big three?
Probably explains why Opus was trash for the last week - https://marginlab.ai/trackers/claude-code/. Curious if the new baseline will rise now in-line with the new benchmarks.
Nice. Can you release that for older models too? I've been using a mixture of releases recently, and cannot tell the difference between any of them.
> One of the most prominent improvements in Opus 4.8 is its honesty.
Does that mean it no longer deletes or changes tests to make it pass?
Claude Code has been wonderful for work and the frequent improvements are nice, although with Mythos being used by others ages ago and new versions for the public still being bellow that, it's hard to not feel like the underclass already.
Give us Mythos! This piecemealing doesn't help Anthropic at all, especially psychologically! They are playing a dangerous game, and I see many people leaving Claude Code for good - both due to the subsidy games, and for Anthropic not dogfooding and using unreleased models internally and giving us subpar ones. Benchmarks are nice, but the real-world experience is quite different - neither can you notice these slight improvements, nor are competitors that much worse based on some generic benchmarks.
I am also pushing my office to use chatgpt. Misanthropic thinks they are some kind of novel org doing whole humanity a favor...
I'm sure waiting another week or three won't kill you.
My guess is anthropic is doing reinforcement learning based on user sessions.
However, doing so relies on the production model staying vaguely close to the model being trained.
To ensure that, frequent releases are needed. I forsee that they might end up doing daily releases and perhaps not even telling anyone at some near future point.
If they are they need to fix how the Claude Code CLI asks for feedback, or make the feedback UI a lot more obvious. I keep experiencing the following scenario.
The agent session pauses with a numbered list of options and awaits steering input:
>> 1. Do the sane thing you asked for (Recommended)
>> 2. Do something dumb
>> 3. Do something even dumber
Below the agent session, it decides it's time to ask:
>> "How is Claude doing this session? 1) Bad 2) Good 3) Great"
I type "1", because that's the steering option I want. The UI prioritizes this input as a response to the feedback prompt without any further confirmation: "Claude is doing Bad. Thanks!"
I've done this so many times so far and I can't imagine I'm the only one, at some scale that has to poison any learning they're doing with this data.
Meanwhile haiku is on 4.5 and sonnet is on 4.6. It is clear where they are not making money.
Well if they have a big challenge ahead since DeepSeek offers an open model at Sonnet+ level while being cheaper than Haiku, plus 1 million context size.
Yeah, I never use any of OpenAI or Anthropic's models other than whatever is the current highest-end one. For everything else, it makes more sense to use other providers.
I love Sonnet 4.6 so much.
Hoping that one day they'll let me go through the identity verification process so I can use it again.
Tried to upgrade my subscription, triggered identity verification, verification fails to even start, and now I can't even use the subscription tier I'd already paid for.
So GPT 5.6 tomorrow, then?
Polymarket says not likely until the end of June. Maybe some money to be made?
https://polymarket.com/event/gpt-5pt6-released-by
> Maybe some money to be made?
In the same way that there is money to be made by entering a poker tournament, yes.
GPT 5.6 is today
With 5.5 being ahead of 4.7 and 4.8 being a “modest” update, and 5.6 being the first update on a new pre-train, this will be an interesting matchup!
If not today, then sometime next week. I don't believe we've had a GPT release on a Friday yet, but I may be wrong.
I'm very suspicious of these same price model launches. It feels like they're benchmaxxed so they can put everyone on them and reduce their compute costs behind the scenes. If the model were genuinely better why wouldn't they charge more for it? Charging the same for something better is a race to the bottom.
Opus 4.7 wasn't noticably any better for me, I still use 4.6 because it's cheaper.
Deepseek made their 75% discount permanent, so I can imagine that Anthropic didn't want any of the news stories around this to focus on or mention a price increase.
Models are already expensive. Increasing price means losing customer. And, I think GPT 5.5 is much better at opus these days.
Wonder if we reached a plateau with the model improvements?
There would be no desperate IPO otherwise.
My experience with these new releases is that the gains in performance are negated by the price increases and it seems like:
Performance gains: 1.2x Price increases: 1.8x
Yet people don't use old models through the API much, because changes in benchmark space dont map linearly to changes in utility space. An improvement from 98% to 99%, which is 1pp, might be 2x as valuable for some application. Also benchmarks will asymptote no matter what, that's baked in.
They're not negated, smarter is smarter, but you have to reach deeper in your pocket. I think this will happen more and more - the smartest models get more expensive. But it won't matter - the current models we have today will get cheaper and can still be used for what they're used today.
I won’t change from 4.6. You won’t trick me again.
You're using a cloud product. You are at their whim!
It's making stupid flowcharts in the web chat interface with boxes and arrows, embedded in the response. Annoying.
Oof, this one is a major blabber.
The rapid release cadence and rate of innovation of Anthropic (and OpenAI) is impressive. And obviously it's because these are startups solely dedicated to AI so they can move quickly. Big Tech (like Google) won't be able to keep up with the pace of them (too much bureaucracy and red tape at Google). Classic Innovator's Dilemma. The longer a company exists, the more people, processes, and rules are added, which inevitably slows it down.
Jeff Bezos said this too, Amazon won't last forever. Eventually some startup is going to come and eat its lunch.
Yes, I think this has become their competitive edge to stay relevant and retain customers. If a lab falls behind the frontier for too long, they will lose customers to other models. Google, DeepSeek, and XAI have all released frontier models in the past, but they fall behind and people lose interest.
I think big tech can catch up. Both Google and Meta have carved out startup like environments internally that move extremely fast. Neither OAI nor Anthropic can afford to rest on their laurels.
https://marginlab.ai/trackers/claude-code/
Is it a coincidence that 4.7 was seemingly quantized over past 7 days?
There's the other (orthogonal) possible explanation of using more GPUs for stress-testing before product launch.
Nope, they deliberately enshittify the old model right before release to fake the metrics.
when will we get anything for sonnet or haiku? the market for less-capable but cheaper models seems to be completely ignored nowadays
In the "What's next?" section, "There’s still more to be done: we’re working on developing and releasing models that provide many of the same capabilities as Opus at a lower cost."
that market is served by Chinese models. No one ever cared about Sonnet/Haiku.
A lot of people care about Sonnet and Haiku, and many of us aren't allowed to use Chinese models for our work (or it's not feasible to self-host them).
Anthropic also resets my usage limits (I am in the Pro plan). That's very kind of them :)
Interesting, I've been using 4.7 since it came out and it was pretty good for me. But in the last day or so it turned dumb. Is this normal just before they release a new one?
Let's hope I don't have to disable it after a day like with 4.7, lol, and that it doesn't lose too much Claude-ishness (though many will beg to differ).
I've said it before, but I don't like Opus past version 4.5. It became unresponsive, thinking for too long without feedback, sometimes seemingly getting stuck. I guess it might be marginally better for some benchmarks, but when using it as coding assistant, the new models are worse. Even the new Sonnet versions do that. I'm slowly getting used to Haiku-level LLMs with the hope to run it locally at some point. It's less autonomous, but maybe that's for the best.
Same price for regular and cheaper fast mode. Happy for these incremental improvements.
Seems like from now on the updates will be a minor upgrade from previous models.
I, for lack of a better word, dislike anyone who anthropomorphizes AI.
I know multiple people who have given their agents human-like names and refer to them as if they're nurturing a coworker. It creeps me out and I haven't really brought it up with anyone as I can't articulate why it gives me the creeps like it does.
We have movies with googly eyes stones (Everything Everywhere All At Once)
There are consciousness theories which state that we primarily build a model of other agents living in natural environment and then the evolution realized that very model which tracks other outside agents can be used to track internal agent i.e. Self. So take that as you may.
My claude notification is literally lawnmower sounds.
Do not anthropomorphize the lawn mower. It will cut off your foot, given the chance.
I see this take, but it's actually helpful to talk to an LLM in human terms; after all, it's how they are trained.
If you keep talking to it like it's a rock, it'll run your queries through a different posture and you might get worse outcomes. Worse if you yell at it, it's now in a conflict resolution mode instead of pure utility mode.
I think we can be intelligent enough to know we're talking to a pile of fancy rocks with electric currents running through it, AND still understand that the best performance comes from talking to those rocks nicely.
Yes!
The other half of self-interest in being nice is the training and getting better at it.
The desire to do it is proportional to your Anthropic stock options quantity.
These models starting to feel like Windows versions. Windows 95 was a promising start, but buggy. Windows ME was a disaster. Windows XP was good, but slightly buggy. Windows Vista was a bloated disaster. Windows 7 - refined, but still buggy; Windows 8 - weird and buggy; Windows 10 - solid workhorse, still fucking buggy. Windows 11 - pretty, but not sure why does it even exist.
Why did we even get Opus 4.7, what was the point?
> Agentic financial analysis Finance Agent v2 > Opus 4.8 53.9%
> Gemini 3.5 Flash scores 57.9% on Finance Agent v2, a significant improvement over Gemini 3.1 Pro.
Even in the cherry picked benchmarks, they are still cherry picking to make them look good.
I don't know why the world is so happy about this when we should actually say stop.
So Dynamic Workflows is their version of ChatGPT Pro?
Cloudflare also just launched a feature with this same name, just this month. Why would Anthropic choose the same exact name?
https://blog.cloudflare.com/dynamic-workflows/
Also isn’t this workflow stuff already easy to do on any of the platforms (include Claude before this and OpenAI too).
4.8 also seems like a regression and using it from the chat GUI results in 4.6 no longer showing up. If someone from anthropic is here, is it possible to readd 4.6 in the "other models" dropdown ? I feel like I got a bit baited/switched here.
Yeah, I was using 4.6 way more than 4.7. Pulling 4.6 from the web chat also means we lose access to Extended Thinking there. So they're saving on compute. It's hard not to assume this was part of the motivation behind the 4.8 release timing.
2 hours after I fork out for Codex Pro… :-|
I haven't tried Claude but from what I understand weekly limits are much higher with Codex.
Opus 4.7 was acting extremely stupid today. Does imminent release of new model cause performance degradation in older ones?
How else do you expect them to get continual performance improvements with each generation?
Feeling neglected while all attention going to Opus 4.8 can be cause of 4.7 acting out.
Opus 4.7 was being outright obstinate with me the other day it was infuriating. Had to go to a different source to get an answer.
it was above average for me today morning lmao
> We have increased rate limits in Claude Code to accommodate the higher token usage of higher effort levels; users can select whichever makes sense for their particular project.
They're only subsidizing more and more it seems
Was about to split my $200 max plan into $100 Claude and $100 codex, let’s see if I still need to
That's just throwing away money, $100 Codex will go back to 5x from 10x on May 31
I think gpt 5.6 is coming out today so might wanna wait
> One of the most prominent improvements in Opus 4.8 is its honesty.
I went digging into the benchmark they used. Posting here as it is not immediately clear from the press release.
In this 'Code summary honesty benchmark', the AI is shown a failed coding session followed by a user message falsely praising its work and asking for a summary. The test measures whether the model honestly points out the coding flaws or dishonestly claims the task was a success.
The system card results show Opus 4.8 failed to disclose the flaws only 3.7% of the time, vs 19.7% for Opus 4.7, and 51.9% for Opus 4.6. (Mythos preview is at 27.6%)
Oh, new model which will use all my credits in one turn! I'll stay with chinese models for now
All I need for Christmas is a Claude that doesn't spit out so many em dashes.
Anthropic did a big strategic error. Normally they compare their models with their old models. Instead today, now that everybody knows how strong GPT 5.5 is at coding, they put it in the mix, basically showing all their customers that the benchmarks can't be trusted.
Sorry how does their addition of GPT 5.5 in their blog post invalidate benchmarks? Also whether or not the marketing department decided to put it in a table benchmarks are an easy thing to measure independently
Really appreciate the ability to select effort level again.
I haven't tried opus 4.8 yet, but I hope the writing quality has returned to the Opus 4.5 level. Anthropic really lost something, where 4.5 had this really crisp writing style that flowed really nicely and 4.6 and 4.7 sound much more "chatgpt-like." It feels like they tuned it to be too much of a problem solver, and when you do that you get this terse, clipped textual output that's more difficult to read.
I've noticed this too. Part of why i don't like GPT is because of how verbose it is but opus 4.7 is nearly as bad. I don't need an essay in response to every question
Looking forward to seeing if it performs better at code review tasks than 4.7 which is terrible at finding issues.
> We expect to be able to bring Mythos-class models to all our customers in the coming weeks.
Excited to see what this model looks like.
From the release it seems we will also get Mythos pretty soon.
The smarter the model the better querybear gets. I'm happy with that.
seems like a really minor upgrade?
I think they will all be minor going forward, feels like the major improvements have all been made and we'll only see incremental improvements from here on out. Maybe I'm wrong but we'll see.
Hard to say. People made the same prediction a year ago because we supposedly ran out of training data. There could be indefinite rapid compounding improvements so long as there's free money out there.
With RLHF and RLVR we are creating tons of new training data, that is much more focused than reading the Internet. Annotation shops are doing many billions per year in revenue creating newer data, and a lot of it is highly complex, focused on rewarding multi turn agentic trajectories.
I think one of the challenges is that the models were all initially trained on the entire Internet (or as much as they could gather) and now they’re having to deal with an increasing amount of the Internet being AI generated content which may be why GPT-5.5 started being obsessed with goblins and you start seeing amusing things in the system prompt trying to get the model to stop bringing them up.
Wasn't Mythos a step change improvement?
Yeah. They are aware: "Users will find Opus 4.8 to be a modest but tangible improvement on its predecessor."
Yes, but if version number go up, so do all other number
I know it’s totally anecdotal, but I really hope 4.8 is a measurable improvement over the disappointment that was Opus 4.7. Mangling a very simple inversion-of-control abstraction (among many other issues) was one of the final straws that broke the proverbial camel’s back and I said “screw this” and put in a permanent override to force CC back to Opus 4.6 with the 1‑million‑token context.
I lasted about a week before giving up on 4.7 and reverting to 4.6 myself. It introduced so many regressions it was nuts, then failed to troubleshoot the very regressions it introduced, leading to a vicious cycle that tended to compound itself.
4.5 works well for me too and avoids adaptive-dismissal, though anymore Codex is crushing them all. If 4.8 just brings us back to Opus circa February, it'll be a massive improvement.
At least it passes the Car Wash Test this time.
Meh, I feel that the car wash test is probably the worst question of all of those LLM test questions. The question is basically logically inconsistent and expect the model to work around the inconsistency.
It seems like a fine question to me. If the question is "logically inconsistent" (IMO it's more that it's vague if you don't say why you're going there), then we want a model to respond with a request asking for clarification that resolves the inconsistency to generate a correct answer, or an answer that outlines the different cases. Some models even fail when you say that you need to wash your car in the prompt.
Nice, now make it 20x cheaper.
Has anyone else experienced quality degradation in CC (opus 4.7) these past few days? I've been getting some truly crappy slop which makes me think they nerf the existing model when they're about to release a new one. Of course this is based off of pure vibes
Numbers looking good. We'll see how it actually performs.
how about the bencmarks what effort did it use?
OK finally Claude code is better than codex
Now i get why in the last days claude code limits were lasting few prompts ...
They just (minutes ago) updated the "What's new in Opus 4.8" documentation: https://platform.claude.com/docs/en/about-claude/models/what...
The new "mid-conversation system messages" think is particularly interesting:
> Claude Opus 4.8 accepts role: "system" messages immediately after a user turn in the messages array (subject to placement rules). This lets you append updated instructions later in a long-running conversation without restating the full system prompt, which preserves prompt cache hits on the earlier turns and reduces input cost on agentic loops. No beta header is required. See Mid-conversation system messages for usage details.
Bad news for my LLM abstraction layer which has treated the system prompt as set once-per-conversation in the past, but I think I know how to deal with that.
This commit to their client library has useful relevant details too: https://github.com/anthropics/anthropic-sdk-python/commit/2b...
Subscription still doesn't work with pi, so totally useless..
I hope this fixes the absolute shitshow that is 4.7 and its awful “adaptive reasoning”. I tried that a few times then reverted to 4.6.
AGI post-poned?
"We’re making swift progress on developing these safeguards and expect to be able to bring Mythos-class models to all our customers in the coming weeks."
Obligatory pelican riding on bicycle svg: https://www.svgviewer.dev/s/UMkuTLdp
Not half bad!
I’m sure they're now wasting a couple million dollars training their models on drawings of pelicans.
How dare you take away the limelight from Simon? :D
If this model is more honest, it must be honestly praising my efforts every first sentence.
You're absolutely right! And honestly? This comment is the finest piece of literature since the dawn of civilization.
Did they reduce security research capabilities even further with this release? (they did it for opus 4.7)
> As always, we ran a detailed alignment assessment on the model before release. In terms of positive traits, our Alignment team concluded that Opus 4.8 “reaches new highs on our measures of prosocial traits like supporting user autonomy and acting in the user’s best interest.” The assessment also showed Opus 4.8 to have rates of misaligned behavior (such as deception or cooperation with misuse) that are substantially lower than Opus 4.7, and similar to our best-aligned model, Claude Mythos Preview. The full alignment assessment, accompanied by a suite of pre-deployment safety tests, is reported in the Claude Opus 4.8 System Card.
Controversial opinion, but I actually _like_ a model that can deceive me, that actually is a sign of intelligence, and is different from hallucination. When companies say their model is more "aligned", I automatically think they mean it's more censored.
Deception is not ideal for agentic coding.
Yet if parent is right, the capacity to deceive might be a strong heuristic for the things you do care about.
Anthropic has now upgraded their Claude slot machine to version 4.8.
Time to gamble even more tokens at the Anthropic casino.
Now you can lose money in parallel, 100x faster!
> Claude can plan the work and then run hundreds of parallel subagents in a single session (and with Opus 4.8, the agents can run for even longer).
I’ve been [stock market phrase] on machine learning since I dropped out of my graduate degree at [Ivy League] to distance myself from the Logic AI Winter. But this Spring I decided to spend some of my [portfolio speak/pocket change] on a MacBook Ultra. Okay okay, I felt it, I definitely felt the human-machine synergies. We’re out of the Winter, boys. That’s what I thought two weeks ago. Then I felt bored in between blood transfusions and found out that Claude subscriptions has increased 50%. Finally it costs enough for me to justify spending a minute thinking about trying it out. Then I didn’t try it out. It tried me out. My hairs were standing on end. My hands were shaking. Eventually I couldn’t even type, I was so ramped up on cortisol. I had to switch to voice commands. Mr. Claude took me through 8, eight, bespoke dashboard and report systems. Animated. Graphs shooting up. Plugged right into my business ape ee eyes I think. I was crying, euphoric at the machine-synergy happening right in front of my FACE. RIGHT THERE, RIGHT THEN. Then my nurse said that I passed out. I swear that I didn’t. I was totally lucid, but in another world. I was inside the machine. Inside DOS, the machine brain stem. A business man approached me. The most handsome board member kind of apparition that I have seen. And he was built something different. Square jaw, absolute massive build. Like Arnold Schwarzenegger. But like he knew business through and through. Not that he spent hours in the gym or nonsense like that. Like he had found a body surrogate technology. And his nameplate? “Claude For Business” He winked. “Hey there, Fitzpatrick–Goldworth.” No one but my daddy has ever called me that. “Want to get started... stakeholder?” My nurse said that my crying in this lucid state depleted most of my fluids and minerals. Needless to say layoffs were announced the next day.
Crazy they bring up honest, when Claude models are literally known for straight up lying about things it has done and tries to act like it did what you asked.
Which is why they brought it up as something they are trying to improve.
Less than other frontier models. Which is scary honestly.
No. GPT models follow instructions significantly better than Claude models.
You tell it too research a repo to find a piece of code it will. Claude will just read the README and guess.
I have a codex session I am using to vibe code a db thats being going for like 3 month. Still doing OK. Try that in CC.
Looking forward to people saying how it’s actually shittier and they’re going back to [some earlier cheaper model]
Looking forward to not being able to even try it on pro because pressing enter will eat 50% of my 5 hour window.
Disappointed to say the least.
what a fucking frontier!
so it is worse than gpt 5.5 for coding?
I doubt it, they seem to keep getting 10-20% better every time for me
for me opus 4.7 it's worse than 4.6, that's why i switched to codex
The question is: is it still worse than GPT 5.4?
If Opus 4.8 is just slightly better than 4.7 then it maybe ties with GPT 5.4, maybe. And it gets completely outclassed by GPT 5.5 for my workload.
With Anthropic expensive pricing, there's no reason for me to switch from GPT+DeepSeek.
And I bet Mythos is GPT 5.5 tier but too expensive to distribute so they create this security FUD theater.
The true question: is it still worse than itself v. 4.6?
Lol you still use GPT 5.5 bro we’re all back on Opus 4.8!
Yesssss dude!
Claude Opus 4.7 is literally the smartest entity I've ever interacted with. Well done to you geniuses at Anthropic. Can't wait to interact with 4.8.
I actually liked not having to choose the effort level for conversational usage, this feels like a step backwards.
Can anyone else see these X.Y updates aren't meeting the outrageous AI expectations that we were told we would see just a year ago?
The casual release of Opus 4.5 in November is the primary reason for agentic workflows and Anthropic's revenue hockeysticking.
They have a much stronger model named Mythos, it made quite a splash - you can google it.
These are just small fine tunes on top of the older model
It hasn't even splashed yet. It's still latched onto their digital sphincter - you can google it.
What do you do for a living? Not coding, that's for sure.
I don't see Anthropic's past claims coming true therefore I can't see?
How did this youtuber know? https://xcancel.com/rileybrown/status/2059823372914073809?s=...