MiMo v2.6

algoth1

Finally a lab that doesn't cheat on the charts

vatsachak

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

DanMcInerney

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...

ddxv

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

omani

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".

nemothekid

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).

rao-v

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!

stymaar

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

syntaxing

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…

bertili

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

NooneAtAll3

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

spwa4

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?

MisterMunchkin

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.

jwpapi

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.

lwansbrough

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

user43928

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 ExploitGym 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 DeepSWE v1.1 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

simonw

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

thrownawaysz

>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

alfalfasprout

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.

gigatexal

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.