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.

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.

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.

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.

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.

The current ranking of all tests makes more sense (well, except for how well Gemini does)

https://aicc.rayonnant.ai

In the real world, you don't hire a plumber and expect him to also do your landscaping, fix your car, and tailor your clothes. It would seem like a much better use of resources if I could download an app that specialized in shell, Python, and C coding for example, or maybe even that would be 3 apps that communicated. Maybe I could even run them on a regular machine with 16GB of RAM. I don't need one huge model that can do that and code in Fortran, COBOL, and Lisp.

As humans, we've done pretty well by specializing. I hope this gets explored more with smaller, focused AI models vs the current path of one model to rule them all that can only be run in a data center the size of a country.

Maybe it’s better in one particular case here and there and I think this blog post is example of that.

Still interesting though. The fact that an open weight model is close enough for that to matter is probably the real story.

This has already happened.

I have downloaded both the big Pro model and the smaller but multimodal MiMo-V2.5.

https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro

https://huggingface.co/XiaomiMiMo/MiMo-V2.5

https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro-Base

https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Base

The download of MiMo-V2.5-Pro takes 963 GB, while that of MiMo-V2.5 takes 295 GB.

For comparison, the download of Kimi-K2.6 takes 555 GB.

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

I could easily see us in a place 2 years from now where this coding application is fully commoditised.

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?

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.

As I said, you can blame the model, but it is nothing that the harness cannot take care of more deterministically.

The initial models were corrected by programmers which gave a very high quality feedback signal. Whereas with vibe coding on the rise, you’ll lose that signal.

We know these models can solve much more difficult problems, something isn't right.

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.

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

What I do see in my own work and that of others around me, is that Claude consistently outperforms Gemini and to a lesser extent Codex.

With Claude eating tokens with declining return, concessions have to be made and Codex is a usable middle ground.

I use Kimi in Kagi's Assistant for non-code or generic programming questions and am quite happy with its no-bullshit responses.

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.

Now imagine a company burning 200,000/month on AI spend. Real numbers. Not every company is but some are.

Why such a company won't deploy an open weight model (Kimi 2.6 or Deepseek v4) on their own hardware (rented or otherwise) to save about 2.4 million dollars a year?

And these are the landmines Chinese cleverly did set up. Not saying intentionally or otherwise.

But end result is that good luck recouping your investments, you can pretty much kiss goodbye to any ROI. The bucket has a hole at the bottom and the bubble bust is guaranteed.

PS: Without open weight models too the economics do not make sense neither the code generated by these SOTA models is reliable enough to be deployed as is. Anyone claiming otherwise either hasn't worked on a real software stack with real users OR didn't use AI long enough to witness the AI slop and how hard it is to untangle or de-slopify the AI generated code therefore these trillion dollar valuations are absurd anyway.

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.

  * do they lie and gaslight

  *  do they start breaking down on very long chats (forget old context, just get dumber)

  * do they constantly try to tell me how smart I am vs solving the problem (yes man)

  * do they follow conventions, parameters set out early in the prompts, or forget them

  * if they cant read a given file (like pdf), do they lie about it

  * is there a branch function to go back to earlier state of conversation

  * what is the quality of the presentation of results (structure, wording, excessive use of tables, appropriate use of headings)

  * how does the bot deal with user frustration (empathy?)

Gemini doesnt feel quite as smart these days. It does well with very long conversations. Except it has bugs where all context gets lost or pruned, and it will lie and gaslight about it. Theres also no branching, so once context is lost you have to start over. Presentation is decent. Empathy is fairly good, except if users get frustrated, it gets more and more flustered and breaks down.