小米MiMo-V2.6評測：開源模型登頂，MIT免費商用 | Xiaomi MiMo-V2.6 Review: Open Model Tops the Leaderboard

9月 23, 2026

By Kit 小克 | AI Tool Observer | 2026-09-24

🇹🇼 小米MiMo-V2.6評測：開源模型登頂，MIT免費商用

小米在 2026 年 9 月 22 日開源了 MiMo-V2.6 系列模型，這是繼手機、電視、電動車之後，小米在 AI 基礎模型賽道打出的重手一擊。這次不只是開源權重，還一口氣公布訓練用的強化學習框架與超過 7,000 個任務環境，姿態擺得很高。

MiMo-V2.6 系列有什麼

這次一共放出四個版本：

MiMo-V2.6-Pro：旗艦版，原生全模態（文字、圖片、語音一起訓練，不是後製拼接）
MiMo-V2.6-Flash：輕量版，主打效率
Pro-UltraSpeed：加速版，號稱輸出速度比 Pro 快 20 倍，同樣品質下拚的是回應速度
MiMo-V2.6-Distill-Qwen-9B：蒸餾到 Qwen 架構的 9B 小模型，一般顯卡就跑得動

權重全部放上 Hugging Face，不用申請、不用等審核，MIT 授權，下載下來要商用、微調、轉售都可以，這點比不少「開源」但限制一堆的模型大方很多。

跑分真的贏了 Kimi K3、GLM-5.3？

Artificial Analysis 智慧指數上，MiMo-V2.6-Pro 拿到 46 分，Kimi K3 是 44 分、GLM-5.3 是 45 分，官方說法是「開放權重模型第一名」。數字上確實領先，但差距只有 1 到 2 分，用單一綜合分數判斷模型好壞本來就有失真風險，跑分領先不代表你的任務（寫程式、跑 agent、中文寫作）都會贏。這種微幅領先在下一輪更新就可能被反超，別把跑分當成唯一依據。

誰該關注這次開源

對一般開發者來說，真正實用的是 Distill-Qwen-9B 這個小模型 —— trillion 參數的 Pro 版本雖然權重公開，自己架設仍然需要多張高階顯卡，個人或小團隊很難跑。9B 版本走 Qwen 架構，社群工具鏈成熟，本地部署、微調都有現成教學可抄。

另外值得注意的是，小米這次也把訓練用的 RL 任務環境和框架一起開源，這對想研究「如何訓練出會用工具的模型」的團隊，比模型本身更有參考價值。

小結

小米砸資源做 MiMo-V2.6，背後盤算應該是餵養自家 HyperOS、AI Studio 這些產品線的 agent 功能，而不是單純做公益。MIT 授權免費商用是真材實料的誠意，但「開放權重榜首」這個稱號建議打個折扣看待。想知道好不好用，載 Distill-Qwen-9B 或 Flash 版本自己跑一輪最實在。

好不好用，試了才知道。

🇺🇸 Xiaomi MiMo-V2.6 Review: Open Model Tops the Leaderboard

On September 22, 2026, Xiaomi open-sourced its MiMo-V2.6 model series — the phone-and-EV maker's biggest swing yet at the foundation model race. It's not just weights this time: Xiaomi also released the reinforcement learning framework and more than 7,000 task environments used to train it.

What's in the MiMo-V2.6 Release

Four variants shipped together:

MiMo-V2.6-Pro — the flagship, natively omnimodal (text, image, and audio trained together, not bolted on)
MiMo-V2.6-Flash — a lighter, efficiency-focused variant
Pro-UltraSpeed — a distilled speed variant claiming 20x faster output than Pro at similar quality
MiMo-V2.6-Distill-Qwen-9B — a 9B model distilled onto the Qwen architecture, small enough to run on a consumer GPU

All checkpoints are on Hugging Face, ungated, under an MIT license — no application, no gatekeeping, free for commercial use, fine-tuning, or resale. That's more generous than plenty of "open" models that bury real restrictions in the fine print.

Does It Actually Beat Kimi K3 and GLM-5.3?

On the Artificial Analysis Intelligence Index, MiMo-V2.6-Pro scored 46, versus 44 for Kimi K3 and 45 for GLM-5.3 — Xiaomi is calling it the top-ranked open-weight model. That's a real lead, but only by 1-2 points on a composite benchmark score, which is a shaky basis for judging real-world quality. A narrow benchmark win doesn't guarantee it'll beat rivals on your actual workload — coding, agent tool-use, or Chinese writing — and the next model update could easily flip the ranking. Treat the leaderboard claim as a data point, not a verdict.

Who Should Actually Care

For most developers, the practical pick is the Distill-Qwen-9B model. The trillion-parameter Pro model has open weights, but self-hosting it still needs multiple high-end GPUs — out of reach for individuals or small teams. The 9B version runs on the mature Qwen tooling ecosystem, with plenty of existing guides for local deployment and fine-tuning.

Also worth noting: Xiaomi open-sourced the RL training environments and framework alongside the model. For teams researching how to train tool-using agents, that may be more valuable than the model weights themselves.

Bottom Line

Xiaomi's investment in MiMo-V2.6 is almost certainly meant to feed agent features into HyperOS, AI Studio, and its other product lines — not pure altruism. The MIT license and free commercial use are genuinely generous. The "top open-weight model" claim, though, deserves a discount. If you want to know whether it's actually good, download the Distill-Qwen-9B or Flash variant and run it yourself.

You won't know until you try it.

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