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- Hacker News
- 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
by vatsachak - I said this years ago, LLMs are a commodity (or were becoming one at the time). They are dime a dozen. Even the frontier ones. OpenAI and Anthropic have no moat.
No moat and competition is good for consumers though.
by toephu2 - 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).by nemothekid
- Pelicans for Flash: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
Pelicans for Pro: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
by simonw - 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
ExploitGymGPT 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
DeepSWE v1.1GPT 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 0.1DeepSeek 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 19.0by user43928 - Flash[1]: 309B total / 15B activated parameters
Pro [2]:, 1.02T total / 42B activated parameters
by stymaar - China will most probably win the AI race in the long run because of one major bottleneck the US has - energy. The electric energy and grid buildout in China has been massive since a long time and there is simply no way for the US to quickly catch up.
No matter how much cash you throw you can't just materialize a 100 nuclear reactors to power the data centers.
- 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!
by rao-v