Chinese open-source models eroding closed-model moats
MiMo v2.6 and MiMo-v2.6-Pro both landed this week with strong benchmark numbers and, notably, transparent methodology. The HN thread praised the release for not 'cheating on charts' and highlighted diverse task demonstrations including DAW use. A separate analysis thread on artificialanalysis.ai confirmed the model punches above its weight, though noted it's slower than DeepSeek in practice.
The pattern here is acceleration from Chinese labs. Commenters in the MiMo thread pointed directly at this: 'The moat is thin.' Every week brings another capable open-weight release that closes the gap on OpenAI and Anthropic. The 'slow down' crowd is losing the argument empirically, at least in terms of capability curve.
The counterpoint from the discussion: speed still matters at inference time, and MiMo-v2.6-Pro trails DeepSeek meaningfully there. Capability and deployability aren't the same thing. But the direction of travel is clear.
So what?
If you're building on top of a closed model and treating that as a defensible technical layer, the ground is shifting under you. Open-weight models are catching up fast, which means your actual moat has to be data, distribution, or workflow integration, not model access.