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I know we have strong views on what a truly open model is (open weights, open training data, open training code etc.) but I really like how transparent they’ve been about the training of this model.

The realtime dashboard they shared during training (https://mimo.xiaomi.com/rl/) was an incredible learning and teaching tool for me, and they’ve been unusually comprehensive in sharing details about their methodology (check out that tech report - it's got lots of clever behind the scene tricks like Google or Deepseek writeups) and benchmark scores (even the stuff they didn’t do well on).

If you’re releasing an open model going forward, please consider offering the community more of this transparency!

Anyone else more excited about Chinese models than American models these days? Big thing for me is affordability.

Pelicans for Flash: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...

Pelicans for Pro: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...

Flash[1]: 309B total / 15B activated parameters

Pro [2]:, 1.02T total / 42B activated parameters

[1]: https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL

[2]: https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL

Looking at the frontend design examples; why do these models seem to love the "01 - UPPERCASE TEXT" motif. It's everywhere now (see https://try.cloudflare.com/, which has '01 · QUICK TUNNELS', but no "02" anywhere).

I don't trust any of the benchmarks where Opus 5 surpasses Astra or Fable 5.1.

Maybe Terminal Bench 4.0 and ExploitGym are reasonable.

Terminal Bench 4.0

  GPT 6 Astra             59.6
  Claude Fable 5.1        55.1
  Claude Opus 5           49.0
  MiMo-V2.6-Pro           34.9
  MiMo-V2.6-Flash         28.8
  DeepSeek V4.1 Flash     26.8
  MiMo-V2.5-Pro            1.5

  GPT 6 Astra             42.4
  Claude Fable 5.1        30.4
  Claude Opus 5           22.1
  MiMo-V2.6-Pro           17.8
  MiMo-V2.6-Flash          6.0
  MiMo-V2.5-Pro            0.1

  DeepSeek V4.1 Flash     74.2
  Claude Opus 5           74.0
  GPT 6 Astra             74.0
  MiMo-V2.6-Pro           71.9
  Claude Fable 5          70.0
  MiMo-V2.6-Flash         67.9
  MiMo-V2.5-Pro           19.0

Wow, the chinese labs are getting good at advertising model releases. The moat is thin.

Some features of the release I like:

- Demonstration of diverse tasks, such as using a DAW

- Graphs from various benchmarks and price ranges

- Real world use of the model in scientific environments

All these new models are such tease for us folks with 128GB of shared memory. Buying another unit now to expand to 256GB is a mortgage payment but it’s getting tempting…

I've got a working recipe to run this model on Dual DGX Spark: https://github.com/volfco/spark-vllm-docker/blob/main/recipe...

Averages ~25-35tok/s which isn't bad for a first attempt.

Funny that all but one video has audio, the house 3D model one, where you can hear (what I assume are) Xiaomi's engineers talking about who knows what.

This looks great in terms of cost and capabilities, truly pushing the frontier forward in terms of open weight light weight models.

>Night 0.8x Usage, 00:00-08:00 -UTC+8

It's because offpeak electricity is cheaper?

Funnily it's perfect if you are in the Pacific Time Zone because you can use it daytime 9am to 5pm

I really liked MiMo 2.5, it was really affordable and actually had vision, unlike DeepSeek. (DeepSeek has only recently added it)

Just tried 2.6 flash on a really niche topic I specialise in and it has done a really good job. They’ve definitely polluted their training data with claudeslop, but looking past the slop there is a decent model.

This is a big week. Probably getting next OpenAI and Anthro models, Grok 4.7, Mimo, etc. These open source model releases are why I can't take the "slow down" crowd seriously. I pitted older Mimo, qwen, step, gpt-oss, and other models against each other playing games like Werewolf and Sketch.io-like games where I let them talk shit while they played against each other. Mimo was by far pareto frontier of game-playing for the models that were <$0.15/m input tokens on OpenRouter. Qwen was pareto frontier in the shit talking game though. Qwen's hilarious. https://www.tiktok.com/@clankerfights/video/7642862917582425...

Finally a lab that doesn't cheat on the charts

They mixed up DeepSeek 4.1 Flash with something else on this page, possibly DeepSeek 4.1 Flash means Gemini 3.8 Flash.

These benchmark are useless as they don't say whether they were done before or after Fable and Astra got nerfed.

Leaning into what it cost to train is hilarious and an obvious shot at US frontier labs spending tens to hundreds of millions or more to train their models.

The moat for OAI and anthropic seems to be very quickly shrinking. Chinese labs are now using RSI-like approaches and even without resorting to heavy distillation they're catching up in a couple of months vs. what would have been 6-12 months a year prior.

And as these models get better the pace of training is quickly speeding up too.

This doesn't bode particularly well for anthropic/OAI after they go public.

does anyone know what unnamed model is on paretto frontier picture right between MiMo 2.5 and 2.6?

so weird to acknowledge someone being on the front edge, but not name it

As for the stats that everyone wants:

MiMo-V2.6-Flash-310B-A15B roughly GPT-5.6 Luna / Claude 4.9 according to benchmarks MiMo-V2.6-Pro-1.02T-A42B roughly GPT-5.6 Sol / Opus 5 according to benchmarks

Perhaps with IQ2 flash will run on 128G M5?

ah, would you look at that. I was wondering why mimo 2.5 became "dumber" the last weeks. I was speculating they are probably about to release a new version of the model. because the model really acted out a lot. especially the last two weeks. dont know, was just a feeling, highly speculative.

but now I got my "proof".

In the chart they use "Pareto Line", which I think is wrong. Pareto is 20% effort leading to 80% results. Which could be interpreted as models costing 20% having 80% of peak intelligence, but that’s not what it looks like to me.

It looks like the "Frontier Line" to me, which is also often misinterpreted. frontier does not mean the best models. It means all models that are not strictly dominated, meaning in most cases: Not same price or cheaper and more intelligent.

I personally would like the word frontier to be used with more criterias: Open Weights, per use-case, etc etc. This would make model selection easier, but I understand it’s not an easy thing to do.