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
Curious if Verizon / ATT still force apps on your phone, eg. NFL and Amazon apps, Fi service is subpar
I'm sure they're doing all kinds of terrible things, like all major companies. I just can't help but like them. Also, this model looks great, and I'll give their subscription a shot next month.
I ask because my wife has the 15T and the camera is better than my iPhone 17 Pro. And while toying around with it I didn't notice any bloat.
Plus hers support native split screen which I kinda need to multitask on the go.
I'm so pissed at how bad Siri is compared to her android phone that I'm thinking about selling the iPhone to get a Huawei Pura Ultra.
but now I got my "proof".
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!
Pro [2]:, 1.02T total / 42B activated parameters
[1] https://sebastianraschka.com/llm-architecture-gallery/per-la...
[2]: See DS 4.1-Flash and Qwen-3.8-Next.
so weird to acknowledge someone being on the front edge, but not name it
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?
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.
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.
- Pareto efficiency/Pareto curves: Basically the convex hull of points along the edge of a graph, indicating the best tradeoff between the axes. This is what the post is talking about.
- Pareto principle: this is the 80/20 rule you're talking about
But at least I can run Chinese models locally, and strip a lot of that censorship/refusal.
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 19.0Pelicans for Pro: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
Qwen3.8-27b pelican was amazing on Mac.
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
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.
It’s an NVFP4 quant, but it fits, and is surprisingly capable.
(or is it somewhere else)
What you call "frontier line" is also called "Pareto frontier" https://en.wikipedia.org/wiki/Pareto_front
Your description of it is basically correct though
And later they further cut Sol and Terra pricing by 20% (maybe only in the API) and Luna by 80%.
In fact Luna still outperformed DeepSeek Flash 4.1 in cost per task on Artificial Analysis when I last checked.
However, Luna is slightly less intelligent. I have a feeling that it's pretty dumb and prone to hallucination unless running at xhigh or max effort, where it somehow manages to work quite well.
I did not personally test the open weight models beyond the old Qwen 3.6 27B, which produced unusably bad results for me.
The competition is great, and I hope Chinese models will continue to force leading US labs to offer models at a low price point.
That said, I don't think the Chinese labs have anything over OpenAI and Anthropic when it comes to capability or efficiency - I have no reason not to believe the US labs have even lower cost to serve the models.
debatable if a turn around is possible before '29
algoth1•59m ago