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 as an industry cannot determine if one software engineer is objectively better than another, on practically any dimension, so why do we think we can come to an objective ranking of models?
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.
What if you have a good enough model but the cloud model providers are better in procuring hardware for interference?
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.
Fits with another comment from yesterday on here who said the flash models are just better at tool calling.
Planning with gpt55 and implementation with a flash model could be bang for the buck route.
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 just had an issue where Claude CLI with Opus 4.7 High could not figure out why my Blazor Server program was inert, buttons didn't do anything etc. After several rounds, I opened the web console and found that it failed to load blazor.js due to 404 on that file. I copied the error message to Claude CLI and after another several unproductive rounds I gave up.
I then moved the Codex, with ChatGTP 5.5 High. I gave it the code base, problem description and error codes. Unlike Claude CLI it spun up the project and used wget/curl to probe for blazor.js, and found indeed it was not served. It then did a lot more probing and some web searches and after a while found my project file was missing a setting. It added that and then probed to verify it worked.
So Codex fixed it in about 20 minutes without me laying hands on it (other than approve some program executions).
However, I'm not convinced this shows GPT 5.5 being that much better than Opus 4.7. It could very well be the harness around it, the system prompts used in the harness and tools available.
For reference this was me just trying to see how good the vibecoding experience is now, so was trying to do this as much hands-off as possible.
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.
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.
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.
Maybe it’s better in one particular case here and there and I think this blog post is example of that.
The current ranking of all tests makes more sense (well, except for how well Gemini does)
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 value of open source is not that you will run it locally, it's that anyone can run it at all.
Even if you can't afford to purchase the hardware to run large open source models, someone would, price it at half the cost of the closed source models and still make a profit.
The only reason you are not seeing that happen right now is because the current front-running token-providers have subsidised their inference costs.
The minute they start their enshittification the market for alternatives becomes viable. Without open-source models, there will never be a viable alternative.
Even if they wanted to charge only 80% of what a developer costs, the existence of open source models that are not far behind is a forcing function on them. There is no moat for them.
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.
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.
That said, I do fully agree that it is valuable to have open near-frontier models, as a balance to the closed ones.
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.
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.
Still interesting though. The fact that an open weight model is close enough for that to matter is probably the real story.
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?
https://www.maxtaylor.me/articles/i-benchmarked-caveman-agai...
> 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.
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.
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.
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 have to use a supposedly frontier model at work and I hate it.
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
Getting a coding plan from Kimi.com will make coding 20x cheaper than using Anthropic.
BTW, I am using it with Claude Code.
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.
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.
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.
* 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.