Xiaomi Releases MiMo-V2.6, an Open-Weight Model Claiming Top Benchmark Score

Xiaomi released MiMo-V2.6 Pro and Flash, a family of omnimodal models trained through scaled reinforcement learning, and open sourced both the weights and the RL environments used to train them, not just the checkpoints. Xiaomi claims Pro matches Claude Opus 5 and GPT 5.6 on agent benchmarks.

Artificial Analysis backs the headline claim. The 1.02 trillion parameter Pro variant is now the top scoring open weights model on its Intelligence Index, at roughly $0.13 per task, putting it on the cost efficiency frontier rather than just the capability frontier. OpenRouter lists the family, including a roughly 10x faster UltraSpeed variant of Pro for latency sensitive work, with support for text, image, video, and audio across a 1M token context. Xiaomi also shipped smaller siblings: a 9B distilled model tuned for tool use and agentic coding, ready to serve through vLLM, SGLang, or Transformers, and an MIT licensed Flash RL variant for lower latency multimodal inference.

For practitioners choosing an open weights model to self host, MiMo-V2.6 is now a fully inspectable alternative to closed frontier models for coding and computer use tasks, with the training method itself available to study, not only the results.