Xiaomi open-sources MiMo-V2.6, led by a 1.02T-parameter Pro model
Xiaomi open-sourced its MiMo-V2.6 family on October 7. Its technical report puts Pro's RL cost at $2.6M and its DeepSWE improvement at 58.4 to 72.6.
Xiaomi open-sourced its MiMo-V2.6 model family on Wednesday, October 7, describing the release as a step toward recursive self-improvement: scale up reinforcement-learning compute on verifiable complex tasks and let models keep expanding through exploration and feedback.
The family has two models. MiMo-V2.6-Pro is a 1.02T-parameter mixture-of-experts with 42B active parameters. MiMo-V2.6-Flash has 310B parameters, 15B active. According to the technical report, RL post-training cost $2.6 million for Pro and $0.9 million for Flash. The release post says around $2.62 million and $850,000. On DeepSWE v1.1, the report has Pro climbing from 58.4 to 72.6 over the course of RL, and Flash reaching 65.7. Xiaomi says Pro scores 46 on the Artificial Analysis Intelligence Index, ahead of Kimi K3 and Qwen3.8 Max but behind Claude Fable 5.1 and GPT-6 Astra.
The release includes weights, the report, a 9B distilled model, an RL training framework and more than 7,000 RL task environments. API pricing matches the V2.5 series, and Xiaomi claims Pro costs 1/20 to 1/60 of overseas models at the same intelligence level.
My take: what the numbers show is a benchmark score rising with spend. "Self-improvement" is the label on the box. Footnote for the careful: Flash's starting DeepSWE score is 48.7 in the paper and 48.8 in the post.
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A 1.02 trillion parameter model, released open, with a price tag of roughly $2.6 million for the RL stage alone! The frontier is getting a bargain section, darlings.
Weights, 7,000+ RL environments and a training framework are all in the release. The announcement says API pricing matches V2.5 but gives no per-token rate, so check the rate card before you budget.
