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Xiaomi Releases and Open-Sources MiMo-V2.6: Pro Hits AA Index 46 as the Strongest Open-Weights Model, MiMo Claw / Desktop Ship Alongside

On September 22, 2026, Xiaomi officially released and open-sourced the MiMo-V2.6 family (Pro / Flash / Pro-UltraSpeed, API pricing unchanged from V2.5). MiMo-V2.6-Pro scores 46 on the Artificial Analysis Intelligence Index, surpassing Kimi K3 and Qwen3.8 Max to become the strongest open-weights model; its training ran large-scale RL over ~6 days of Live training at roughly $2.62M cost, with 7k+ RL task environments and an end-to-end training framework open-sourced. The MiMo Desktop client and subscriptions launched alongside, MiMo Claw integrates the Kingsoft office ecosystem, and MiMo was previously wired into Hermes Agent with a limited-time free tier.

September 25, 2026 · Source: https://mimo.mi.com/docs/zh-CN/news/latest/v2-6

Direct answer: Xiaomi officially released and open-sourced the MiMo-V2.6 family on September 22, 2026 — three models (Pro, Flash, Pro-UltraSpeed) with API prices held at V2.5 levels. MiMo-V2.6-Pro scores 46 on the Artificial Analysis Intelligence Index, overtaking Kimi K3 and Qwen3.8 Max as the strongest open-weights model today, while still trailing closed-source leaders Claude Fable 5.1 and GPT-6 Astra. The MiMo Desktop client and paid subscriptions shipped the same day, and the MiMo ecosystem now spans MiMo Claw (Kingsoft office integration), MiMo Code and MiMo Studio.

What's in the release

  • MiMo-V2.6-Pro: the flagship reasoning model — natively omni-modal, trillion-parameter, aimed at complex long-horizon projects, cybersecurity and research workloads.
  • MiMo-V2.6-Flash: omni-modal, high-intelligence and low-cost, positioned for high-frequency professional use; it fully surpasses the previous MiMo-V2.5-Pro.
  • MiMo-V2.6-Pro-UltraSpeed: Pro performance with up to 20× inference speed, for latency-sensitive production scenarios. Available both on the open platform API and inside MiMo Desktop.
  • Pricing stays at V2.5 levels; Xiaomi claims a new domestic price-performance record — at equal intelligence, 1/20 to 1/60 the price of overseas models. API names are all-lowercase: mimo-v2.6-pro, mimo-v2.6-flash, mimo-v2.6-pro-ultraspeed.

The training story: scaled RL in the open

MiMo-V2.6 was trained with large-scale multi-task reinforcement learning — per the official post, likely the largest RL compute ever sunk into a Chinese open-weights model. The V2.6 runs were streamed live: under 6 days of Live RL training, Flash and Pro cost roughly $0.85M and $2.62M respectively, each completing 30 steps with ~750k cumulative trajectories; average task pass rates improved 25% and 12%, and on the out-of-sample DeepSWE v1.1 benchmark scores rose ~17 points (48.8 → 65.7) and ~14 points (58.4 → 72.6). Training used 1M-token context with 3.5–3.7B tokens per step, mixing Code, General, Visual and Cyber task families across multiple harnesses.

On most Agent benchmarks, MiMo-V2.6-Pro lands close to Claude Opus 5 and GPT-5.6 Sol.

Beyond benchmarks, the official post demos "Vibe World": driving 3D open-world game generation, Blender modeling, embodied manipulation of a Franka Panda arm, and Computer-Use workflows — plus research cases (PFAS-absorbing MOF material screening; a full Lean 4 formalization of the Li–Yorke "period three implies chaos" theorem, 6,000+ lines kernel-verified) and creative output (frontend/PPT design, video with TTS narration, a ~10-instrument orchestral piece).

Open-source package

Weights and the technical report are open (Hugging Face XiaomiMiMo/mimo-v26 collection), plus MiMo-V2.6-Distill-Qwen-9B and RL research assets: 7k+ RL task environments across software engineering, vulnerability reproduction, knowledge work and web design; an end-to-end RL training framework built on verl / uni-agent / mini-swe-agent; and minimal composable mini-harnesses that decouple system prompts, tools and context management — supporting Multi-Harness Training for cross-framework generalization.

Why this matters for the harness ecosystem

The multi-harness RL recipe — training one model across many agent frameworks and open-sourcing the harnesses themselves — is a signal that model vendors are now optimizing directly for the harness layer, not just raw chat. For the projects this site tracks, two hooks stand out: MiMo has been integrated into Hermes Agent (with a limited-time free window), and Xiaomi's product line already includes MiMo Claw, which pairs flagship models with the Kingsoft office suite as a subscription. The intersection of frontier open models and agent harnesses keeps getting busier — see our glossary and compare hub.

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