At the current rate, open sourced models are expected to surpass cloud models within a couple years based on a study I read a couple days ago.
Looking back at chatGPT and claude a couple years ago, very small Qwen models are basically equal in coding to what those cloud based models could do then. Also factoring in scaling laws, a 9b going to 18b is roughly a 40% increase, whereas 18b to 35b is 20%, I expect there will be a change of at least price in cloud based models.
Adobe used to be $600 per month, then it became $20 when distribution scaled.
These posts are going to be a constant for the next year, because there's no objective way to compare models (past low-level numbers like token generation speed, average reasoning token amount, # of parameters, active experts, etc). They're all quite different in a lot of ways, they're used for many different things by different people, and they're not deterministic. So you're constantly gonna see benchmarks and tests and proclamations of "THIS model beat THAT model!", with people racing around trying to find the best one.
But there is no best one. There's just the best one for you, based on whatever your criteria is. It's likely we'll end up in a "Windows vs MacOS vs Linux" style world, where people stick to their camps that do a particular thing a particular way.
I'm glad we're seeing a shift towards objectively scored tests.
We've been doing this at scale at https://gertlabs.com/rankings, and although the author looks to be running unique one-off samples, it's not surprising to see how well Kimi K2.6 performed. Based on our testing, for coding especially, Kimi is within statistical uncertainty of MiMo V2.5 Pro for top open weights model, and performs much better with tools than DeepSeek V4 Pro.
GPT 5.5 has a comfortable lead, but Kimi is on par with or better than Opus 4.6. The problem with Kimi 2.6 is that it's one of the slower models we've tested.
In my experience benchmarks are pretty meaningless.
Not only is performance dependent on the language and tasks gives but also the prompts used and the expected results.
In my own internal tests it was really hard to judge whether GPT 5.5 or Opus 4.7 is the better model.
They have different styles and it's basically up to preference. There where even times where I gave the win to one model only to think about it more and change my mind.
At the end of the day I think I slightly prefer Opus 4.7.
I think benchmarks are improving and will always have value, but it's the equivalent to someone's college and GPA for an entry level job application.
It's a strong signal for a job, but the soft skills are sometimes going to get Claude Opus 4.6 a job over smarter applicants. That's what we'd really like to measure objectively, and are actively working on.
I'm not sure how many of our requests got routed to Fireworks -- for our testing, we set preferences for routing to providers with the highest advertised quantizations / highest reasoning mode support / or preferably the model developer itself.
While it may be possible to get better numbers from certain providers, we try to establish a common baseline. I.e. if we measure that Kimi K2.6 averages 450s on a task and GLM 5.1 averages 400s, you might be able to improve that number on a provider like Fireworks but GLM 5.1 would also likely be 10% faster on the premium provider. This is a caveat worth considering when comparing to proprietary model speeds on the site, though.
Currently it's not a huge difference given the subsidies of closed model subscriptions. Once that stops then yea it will be really nice to have open models as price competitors.
This is the future though. Open weights models that run on H200s provide far more opportunity to build products and real infrastructure around.
You can always distill this for your little RTX at home. But models shaped for consumer hardware will never win wide adoption or remain competitive with frontier labs.
This is something that _can_ compete. And it will both necessitate and inspire a new generation of open cloud infra to run inference. "Push button, deploy" or "Push button, fine tune" shaped products at the start, then far more advanced products that only open weights not locked behind an API can accomplish.
Now we just need open weights Nano Banana Pro / GPT Image 2, and Seedance 2.0 equivalents.
The battle and focus should be on open weights for the data center.
These large MoE models can work quite well on consumer or prosumer platforms, they'll just be slow, and you have to offset that by running them unattended around the clock. (Something that you can't really do with large SOTA models without spending way too much on tokens.) This actually works quite well for DeepSeek V4 series which has comparatively tiny KV-cache sizes so even a consumer platform can run big batches in parallel.
Multiple providers of the same model. That means competition for price, reliability, latency, etc. It also means you can use the same model as long as you want, instead of having it silently change behaviour.
> Open weights is great if you want to do additional training, or if you need on-prem for security.
The power of giving universities, companies, and hackers "full" models should not be understated.
Here are a just a few ideas for image, video, and creative media models:
- Suddenly you're not "blocked" for entire innocuous prompts. This is a huge issue.
- You can fine tune the model to learn/do new things. A lighting adjustment model, a pose adjustment model. You can hook up the model to mocap, train it to generate plates, etc.
- You can fine tune it on your brand aesthetic and not have it washed out.
The enshittification will go unnoticed at first but I'm already finding my favourite frontier models severely nerfed, doing incredibly dumb stuff they weren't in the past.
We need open weight models to have a stable "platform" when we rely on them, which we do more and more.
Most people won't roll out their own K2 deployment across rented GPUs, so in that sense it doesn't matter that much, they'll be using a paid service which is just as much of a black box as Claude or ChatGPT. For example, on OpenRouter you can select a provider which state they use a given open model, but you have no idea what actually goes on behind the curtains, which quantization levels they use and so on.
That said, I do fully agree that it is valuable to have open near-frontier models, as a balance to the closed ones.
It's not really a black box. Useful models becoming fungible is crucial for disincentivizing bad behaviour with model providers. I can't really overstate how different it is from relying on closed models. If you don't like or trust any of the providers on OpenRouter you can rent the GPUs yourself and host it, although this is probably unnecessary.
I have been using Sonnet and others (DeepSeek, ChatGPT, MiniMax, Qwen) for my compiler/vm project and the Claude Pro plan is mostly unusable for any serious coding effort. So I use it in chat mode in the browser where it cannot needlessly read your entire project, and use Kimi on the OpenCode Go plan with pi.
Kimi consistently exceeded Sonnet on the C+Python project. Never had to worry about it doing anything other than what I asked it to do. GLM crapped the bed once or twice. Kimi never did.
I absolutely love Kimi's personality - some of the things it says are so out there! And it's been great for very focused, iterative work.
Its weakness is that it seems to yak on-and-on when it needs to plan out something big or read through and make sense of how to use a niche piece of a complex library. To the point where it can fill up its 256k window - and rack up a build. (No cache.) I have had better experience with GLM 5.1 in those cases.
This seems to be testing the models on leetcode style prompts that also require the model to implement TCP calls to send the results. Interesting but probably not a apples to apples comparison. The fact only Grok qualified for the first one seems suspect
I’ve been maining Kimi k2.6 through opencode go and openrouter for a week and I can say it’s the same experience as when I was maining Sonnet 3.5/4 late last year.
Not as good or as fast as Claude Code on Opus now but definitely enough for casual/hobby use. The best part is multiple choices for providers, if opencode gimps their service, I’ll switch
Great to know, but what was the cost both in terms of $$ and tokens used?
Not to invalidate these benchmark results because they are useful, but the real usefulness it what they are capable to do when real people interact with them at scale.
Regardless, these are good news, because now that Microsoft is basically giving up their all-in strategy with Github's Copilot and Anthropic is playing the "I'm too good for you" game, it's about time for them to get pressed into not making this AI world into a divide between the haves and the have-nots.
You’d be surprised with some long running complex tasks. I’ve seen Kimi spend 8 minutes (total) thinking on a task that Claude got done in 30 seconds. They both ultimately got it right, but Kimi spent ~$2.25 to Claude’s ~$0.20
I have to try Kimi. I was looking for an alternative. If you have any experience, advice, please share. I saw Kimi is at the top of the Open Router ranking.
I use Kimi at home via a kimi.com subscription and Kimi CLI (sometimes running inside Zed, sometimes not). My favorite model by far. And it's just $20.
I have to use a supposedly frontier model at work and I hate it.
Thanks, I am trying it right now. I had an opencode plan 5$/month, so I will play with that. I use ZED and I added Pi ACP, so I can try the both pi and Kimi. I will also try it in opencode and via Kimi code.
Use kimi 2.6 for planning and a cheap model (preferably local) for execution, and then kimi once again for reviewing it. Then finally I review the code. Saves a lot on tokens.
Awesome to have a open model that can compete, but damn it would be so much better if you could run it locally. Otherwise, it's almost so difficult to run (e.g. self host) that it's just way more convenient to pay OpenAI, Claude, etc
I’m a little confused as to the setup. It was asking each model to one-shot a script and then the scripts faced off? Were the models given a computer environment? Or a test server to iterate against?
One shot just means the user doesn’t have to iterate on it via the agent. The agent does what ever it needs to deliver the best outcome, including its own running and iteration until it’s happy with it. This could be a short or long process potentially depending on the task.
If you can afford the hardware to run Kimi K2.6 at any decent speed for more than 1 simultaneous user, you probably have a whole team of people on staff who are already very familiar with how to benchmark it vs Claude, GPT-5.5, etc.
Amazing. To me it feels like GLM 5.1, Kimi 2.6, DeepSeek 4 are all competitive both with each other and with the American models. Truly a great time to be alive.
I would like to see more effort making the flash variants work for coding. They are super economical to use to brute force boilerplate and drudgery, and I wonder just how good they can be with the right harness, if it provides the right UX for the steering they require.
As much as vibe coding has captured the zeitgeist, I think long term using them as tools to generate code at the hands of skilled developers makes more sense. Companies can only go so long spending obscene amounts of money for subpar unmaintainable code.
You can sign up for a plan on the kimi code platform and use it via the pi.dev coding agent, or opencode. In planning, I’d say it’s almost on par with Claude Opus.
So we are now at the point where open weight models are rapidly catching up to the frontier models.
They are at best 30 days behind, and at worst case 2 months behind. The last issue is being able to run the best one on conventional hardware without a rack of GPUs.
The Macbooks, and Mac minis are behind on hardware but eventually in the next 2 years at worst will make it possible thanks to the advancements of the M-series machines.
All of this is why companies like Anthropic feel like they have to use "safety" to stop you from running local models on your machine and get you hooked on their casino wasting tokens with a slot machine named Claude.
At the current rate, open sourced models are expected to surpass cloud models within a couple years based on a study I read a couple days ago.
Looking back at chatGPT and claude a couple years ago, very small Qwen models are basically equal in coding to what those cloud based models could do then. Also factoring in scaling laws, a 9b going to 18b is roughly a 40% increase, whereas 18b to 35b is 20%, I expect there will be a change of at least price in cloud based models.
Adobe used to be $600 per month, then it became $20 when distribution scaled.
While this might be true I’m worried about the hardware side of things.
What if you have a good enough model but the cloud model providers are better in procuring hardware for interference?
These posts are going to be a constant for the next year, because there's no objective way to compare models (past low-level numbers like token generation speed, average reasoning token amount, # of parameters, active experts, etc). They're all quite different in a lot of ways, they're used for many different things by different people, and they're not deterministic. So you're constantly gonna see benchmarks and tests and proclamations of "THIS model beat THAT model!", with people racing around trying to find the best one.
But there is no best one. There's just the best one for you, based on whatever your criteria is. It's likely we'll end up in a "Windows vs MacOS vs Linux" style world, where people stick to their camps that do a particular thing a particular way.
I'm glad we're seeing a shift towards objectively scored tests.
We've been doing this at scale at https://gertlabs.com/rankings, and although the author looks to be running unique one-off samples, it's not surprising to see how well Kimi K2.6 performed. Based on our testing, for coding especially, Kimi is within statistical uncertainty of MiMo V2.5 Pro for top open weights model, and performs much better with tools than DeepSeek V4 Pro.
GPT 5.5 has a comfortable lead, but Kimi is on par with or better than Opus 4.6. The problem with Kimi 2.6 is that it's one of the slower models we've tested.
In my experience benchmarks are pretty meaningless.
Not only is performance dependent on the language and tasks gives but also the prompts used and the expected results.
In my own internal tests it was really hard to judge whether GPT 5.5 or Opus 4.7 is the better model.
They have different styles and it's basically up to preference. There where even times where I gave the win to one model only to think about it more and change my mind.
At the end of the day I think I slightly prefer Opus 4.7.
I think benchmarks are improving and will always have value, but it's the equivalent to someone's college and GPA for an entry level job application.
It's a strong signal for a job, but the soft skills are sometimes going to get Claude Opus 4.6 a job over smarter applicants. That's what we'd really like to measure objectively, and are actively working on.
Are you tests and results open source?
Test result summaries are openly available, test environments are not.
Any thoughts on using it on Fireworks? It's extremely fast there.
I'm not sure how many of our requests got routed to Fireworks -- for our testing, we set preferences for routing to providers with the highest advertised quantizations / highest reasoning mode support / or preferably the model developer itself.
While it may be possible to get better numbers from certain providers, we try to establish a common baseline. I.e. if we measure that Kimi K2.6 averages 450s on a task and GLM 5.1 averages 400s, you might be able to improve that number on a provider like Fireworks but GLM 5.1 would also likely be 10% faster on the premium provider. This is a caveat worth considering when comparing to proprietary model speeds on the site, though.
In a single challenge, measured by how performant the solution was.
Kimi K2.6 is definitely a frontier-sized model, so on the one hand it's not that surprising it's up there with the closed frontier models.
Being open is nice though, even though it doesn't matter that much for folks like me with a single consumer GPU.
>Being open is nice though, even though it doesn't matter that much for folks like me with a single consumer GPU.
Of course it matters because that makes coding plans much cheaper than those from Anthropic and OpenAI.
For personal use I have coding plans with GLM 5.1, Kimi K2.6, MiniMax M2.7 and Xiaomi MiMo V2.5 Pro and I am getting a lot of bang for the buck.
Currently it's not a huge difference given the subsidies of closed model subscriptions. Once that stops then yea it will be really nice to have open models as price competitors.
This is the future though. Open weights models that run on H200s provide far more opportunity to build products and real infrastructure around.
You can always distill this for your little RTX at home. But models shaped for consumer hardware will never win wide adoption or remain competitive with frontier labs.
This is something that _can_ compete. And it will both necessitate and inspire a new generation of open cloud infra to run inference. "Push button, deploy" or "Push button, fine tune" shaped products at the start, then far more advanced products that only open weights not locked behind an API can accomplish.
Now we just need open weights Nano Banana Pro / GPT Image 2, and Seedance 2.0 equivalents.
The battle and focus should be on open weights for the data center.
These large MoE models can work quite well on consumer or prosumer platforms, they'll just be slow, and you have to offset that by running them unattended around the clock. (Something that you can't really do with large SOTA models without spending way too much on tokens.) This actually works quite well for DeepSeek V4 series which has comparatively tiny KV-cache sizes so even a consumer platform can run big batches in parallel.
I don’t fully understand what open weights unlocks that cannot be accomplished via API from a product standpoint.
Open weights is great if you want to do additional training, or if you need on-prem for security.
Multiple providers of the same model. That means competition for price, reliability, latency, etc. It also means you can use the same model as long as you want, instead of having it silently change behaviour.
Or try to beat Anthropic's uptime.
> Open weights is great if you want to do additional training, or if you need on-prem for security.
The power of giving universities, companies, and hackers "full" models should not be understated.
Here are a just a few ideas for image, video, and creative media models:
- Suddenly you're not "blocked" for entire innocuous prompts. This is a huge issue.
- You can fine tune the model to learn/do new things. A lighting adjustment model, a pose adjustment model. You can hook up the model to mocap, train it to generate plates, etc.
- You can fine tune it on your brand aesthetic and not have it washed out.
It absolutely does matter.
The enshittification will go unnoticed at first but I'm already finding my favourite frontier models severely nerfed, doing incredibly dumb stuff they weren't in the past.
We need open weight models to have a stable "platform" when we rely on them, which we do more and more.
Most people won't roll out their own K2 deployment across rented GPUs, so in that sense it doesn't matter that much, they'll be using a paid service which is just as much of a black box as Claude or ChatGPT. For example, on OpenRouter you can select a provider which state they use a given open model, but you have no idea what actually goes on behind the curtains, which quantization levels they use and so on.
That said, I do fully agree that it is valuable to have open near-frontier models, as a balance to the closed ones.
It's not really a black box. Useful models becoming fungible is crucial for disincentivizing bad behaviour with model providers. I can't really overstate how different it is from relying on closed models. If you don't like or trust any of the providers on OpenRouter you can rent the GPUs yourself and host it, although this is probably unnecessary.
Kimi is really good.
I have been using Sonnet and others (DeepSeek, ChatGPT, MiniMax, Qwen) for my compiler/vm project and the Claude Pro plan is mostly unusable for any serious coding effort. So I use it in chat mode in the browser where it cannot needlessly read your entire project, and use Kimi on the OpenCode Go plan with pi.
Kimi consistently exceeded Sonnet on the C+Python project. Never had to worry about it doing anything other than what I asked it to do. GLM crapped the bed once or twice. Kimi never did.
I was surprised by the ranking, until I read what the test was. Not horribly relevant for coding.
The current ranking of all tests makes more sense (well, except for how well Gemini does)
https://aicc.rayonnant.ai
This seems less like Kimi is better at coding than Claude and more like Kimi found the right strategy for this particular game.
Still interesting though. The fact that an open weight model is close enough for that to matter is probably the real story.
I absolutely love Kimi's personality - some of the things it says are so out there! And it's been great for very focused, iterative work.
Its weakness is that it seems to yak on-and-on when it needs to plan out something big or read through and make sense of how to use a niche piece of a complex library. To the point where it can fill up its 256k window - and rack up a build. (No cache.) I have had better experience with GLM 5.1 in those cases.
Anyone out there relate?
Absolutely. I use caveman to help with that: https://github.com/JuliusBrussee/caveman
You can just add "be brief" to the prompt to replace the entire plugin. Same results.
https://www.maxtaylor.me/articles/i-benchmarked-caveman-agai...
Not a bad idea - however
> Caveman only affects output tokens — thinking/reasoning tokens are untouched.
The problem is the thinking. But could help to tune my system prompt for Kimi.
In my opinion, this kind of comparison is not very meaningful.
This seems to be testing the models on leetcode style prompts that also require the model to implement TCP calls to send the results. Interesting but probably not a apples to apples comparison. The fact only Grok qualified for the first one seems suspect
I’ve been maining Kimi k2.6 through opencode go and openrouter for a week and I can say it’s the same experience as when I was maining Sonnet 3.5/4 late last year.
Not as good or as fast as Claude Code on Opus now but definitely enough for casual/hobby use. The best part is multiple choices for providers, if opencode gimps their service, I’ll switch
Great to know, but what was the cost both in terms of $$ and tokens used?
Not to invalidate these benchmark results because they are useful, but the real usefulness it what they are capable to do when real people interact with them at scale.
Regardless, these are good news, because now that Microsoft is basically giving up their all-in strategy with Github's Copilot and Anthropic is playing the "I'm too good for you" game, it's about time for them to get pressed into not making this AI world into a divide between the haves and the have-nots.
Re pricing. Never as high as frontier commercial models.
You’d be surprised with some long running complex tasks. I’ve seen Kimi spend 8 minutes (total) thinking on a task that Claude got done in 30 seconds. They both ultimately got it right, but Kimi spent ~$2.25 to Claude’s ~$0.20
I have to try Kimi. I was looking for an alternative. If you have any experience, advice, please share. I saw Kimi is at the top of the Open Router ranking.
I use Kimi at home via a kimi.com subscription and Kimi CLI (sometimes running inside Zed, sometimes not). My favorite model by far. And it's just $20.
I have to use a supposedly frontier model at work and I hate it.
Nice, thanks for sharing!
Kimi K2.6 is great but I advice you to get a coding plan from Kimi.com as that way is much cheaper than paying for API calls using OpenRouter.
Thanks, I am trying it right now. I had an opencode plan 5$/month, so I will play with that. I use ZED and I added Pi ACP, so I can try the both pi and Kimi. I will also try it in opencode and via Kimi code.
Use kimi 2.6 for planning and a cheap model (preferably local) for execution, and then kimi once again for reviewing it. Then finally I review the code. Saves a lot on tokens.
Very interesting, thanks for sharing. I am testing it with Pi in Zed and it seems pretty good.
I always though claude is the goat, but i guess its time to change the notion and try Kimi K2.6
Is the site just slashdotted rn? Can anyone get to it?
Slashdot... Now that's a name I haven't heard in a long time. A long time.
What's the GPU VRAM requirements for this thing?
Awesome to have a open model that can compete, but damn it would be so much better if you could run it locally. Otherwise, it's almost so difficult to run (e.g. self host) that it's just way more convenient to pay OpenAI, Claude, etc
>Otherwise, it's almost so difficult to run (e.g. self host) that it's just way more convenient to pay OpenAI, Claude, etc
Getting a coding plan from Kimi.com will make coding 20x cheaper than using Anthropic.
BTW, I am using it with Claude Code.
I’m a little confused as to the setup. It was asking each model to one-shot a script and then the scripts faced off? Were the models given a computer environment? Or a test server to iterate against?
Sounds incredibly simple to me. One-shot.
So nothing like real-world coding, where you’d be able to run and test the script before submitting?
One shot just means the user doesn’t have to iterate on it via the agent. The agent does what ever it needs to deliver the best outcome, including its own running and iteration until it’s happy with it. This could be a short or long process potentially depending on the task.
People thinking to self-host Kimi K2.6 had better be prepared for how big it is.
Q8 K XL quantization for instance is around 600GB on disk. I would bet about 700GB of VRAM needed.
Quantizations lower than Q8 are probably worthless for quality.
Or 2.05TB on disk for the full precision GGUF.
https://huggingface.co/unsloth/Kimi-K2.6-GGUF
If you can afford the hardware to run Kimi K2.6 at any decent speed for more than 1 simultaneous user, you probably have a whole team of people on staff who are already very familiar with how to benchmark it vs Claude, GPT-5.5, etc.
Kimi is a natively quantized model, the lossless full precision release is 595GB. Your own link mentions that.
Amazing. To me it feels like GLM 5.1, Kimi 2.6, DeepSeek 4 are all competitive both with each other and with the American models. Truly a great time to be alive.
I would like to see more effort making the flash variants work for coding. They are super economical to use to brute force boilerplate and drudgery, and I wonder just how good they can be with the right harness, if it provides the right UX for the steering they require.
As much as vibe coding has captured the zeitgeist, I think long term using them as tools to generate code at the hands of skilled developers makes more sense. Companies can only go so long spending obscene amounts of money for subpar unmaintainable code.
All my co-workers say Claude blows away Gemini. Is it really that good? How can I do Kimi?
You can sign up for a plan on the kimi code platform and use it via the pi.dev coding agent, or opencode. In planning, I’d say it’s almost on par with Claude Opus.
So we are now at the point where open weight models are rapidly catching up to the frontier models.
They are at best 30 days behind, and at worst case 2 months behind. The last issue is being able to run the best one on conventional hardware without a rack of GPUs.
The Macbooks, and Mac minis are behind on hardware but eventually in the next 2 years at worst will make it possible thanks to the advancements of the M-series machines.
All of this is why companies like Anthropic feel like they have to use "safety" to stop you from running local models on your machine and get you hooked on their casino wasting tokens with a slot machine named Claude.
Yes gimini is a programming application