Kimi K2.6 just beat Claude, GPT-5.5, and Gemini in a coding challenge

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.

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.

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.

Slashdot... Now that's a name I haven't heard in a long time. A long time.

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.