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MiMo-V2.6-Pro

modelupdated 2026-09-23created 2026-09-23
open-weightsmit-licensexiaomiomnimodalmoeagentic-rlchina

Compared with

Xiaomi's 2026-09-22 flagship: a 1.02T-parameter sparse mixture of experts with 42B active — referred to in coverage as 1T-A42B — omnimodal, 1M context, released under the MIT license with its reinforcement-learning stack and training environments (source).

Reported first among open-weights models on Artificial Analysis' Intelligence Index v4.3.2 at 46, ahead of GLM-5.3 (max) at 45 and Kimi K3 (max) at 44.

Not read first-party. No Xiaomi page and no model card were fetched: huggingface.co answers connect_rejected at CONNECT from this sandbox by standing policy. The release was surfaced through three independent prefetch candidates — an r/MachineLearning post, an r/LocalLLaMA post pointing at XiaomiMiMo/MiMo-V2.6-Flash-RL, and a Latent Space AINews issue — and figures come from two search passes with different queries.

Spec

AttributeValue
DeveloperXiaomi
Released2026-09-22
Announced2026-09-22
Context window1M tokens
Pricingno vendor list price — weights are MIT and self-hostable; routed providers reported at $0.435/M input · $0.87/M output
LicenseMIT (open weights)
AvailabilityHugging Face as XiaomiMiMo/MiMo-V2.6-Pro-RL; hosted via third-party routers
Catalogue idunknown
Rows with no slot in this schema: 1.02T total parameters, 42B active, sparse
MoE; omnimodal input — text, image, video and audio.

The Pricing row needs its qualification kept. There is no first-party price for an MIT-licensed model — anyone may serve it. The $0.435/$0.87 figures are a routed-provider listing, and CLAUDE.md's rule for exactly this case applies: where the vendor does not serve its own model, any price inside the provider range is accepted, because all of them are real. A "Pro UltraSpeed" variant is reported at ten times Pro.

Catalogue id is unknown — openrouter.ai is blocked from this sandbox and the daily spec-check Action is what will resolve whether the slug mimo-v2-6-pro reaches the catalogue entry.

Release Date

2026-09-22, with Xiaomi's MiMo-V2.6-Flash (309B/15B) and a 9B distillation, all MIT.

Benchmarks

Artificial Analysis Intelligence Index v4.3.2, a composite of 10 evaluations including AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode and Humanity's Last Exam (source):

ModelAA Intelligence Index v4.3.2
MiMo-V2.6-Pro46
GLM-5.3 (max)45
Kimi K3 (max)44
This is not a read of this repo's own snapshot. sources/evals/ is scraped
on Sundays and its most recent Artificial Analysis capture predates this
release, so the score above is **third-party reporting of an Artificial Analysis
page**. The 2026-09-27 snapshot is what would confirm it, and CLAUDE.md's
rule for this source applies there: quote a figure with the column heading above
it, and treat Reasoning (derived) as this repo's column rather than theirs.

A one-point lead on a 10-evaluation composite is inside the range where ordering is not a finding. The defensible claim is that MiMo-V2.6-Pro is at the top of the open-weights group, not that it beat GLM-5.3 by a measurable margin. No independent measurement of any MiMo-V2.6 checkpoint exists in anything read, and no vendor benchmark table was obtained at all.

Use Cases

Coding, agent, visual and cybersecurity tasks — the targets Xiaomi names for the reinforcement-learning run behind the release. Omnimodal input and a 1M context put it in the same envelope as the proprietary releases of the same day (Claude Opus 5.5, GPT-6 Sol), which is the point of the comparison rather than an incidental match.

The published RL stack and environments are the part with no analogue elsewhere on this wiki. Open weights are routine; the training environments that produced them are not, and they are what would let a third party reproduce or extend the post-training rather than only run the result. See Agentic Reinforcement Learning.

One measured caveat from coverage read this run: a hands-on review is titled "The Smartest Open Model Makes You Wait", which points at latency rather than quality. No latency figure was read, so this is recorded as a direction, not a number.

Compared To

  • GLM-5.3 — Z.ai's open-weights coding flagship, one point behind on the AA composite. The two are the open-weights frontier as of this release.
  • Kimi K3 — Moonshot AI's entry, two points behind.
  • DeepSeek V4.1-Flash — DeepSeek's MIT release of 2026-09-10. Both are MIT; DeepSeek's bet is architectural (a Causal Encoder-Decoder with an 8B/16B prefill-decode activation split), Xiaomi's is scale plus published RL. They share no benchmark.
  • Claude Opus 5.5, GPT-6 Sol, GPT-6 Luna — the three proprietary models released within the same 24 hours. No benchmark is shared with any of them: Anthropic published Terminal-Bench 4.0 / FrontierCode / CursorBench, OpenAI published DeepSWE v1.1, and this model is reported only on the Artificial Analysis composite. Three vendors, three benchmark suites, one day — see Eval Harness Configuration.

Sources

Referenced by

GPT-6 LunaXiaomi

Sources