MiMo-V2.6-Pro

Spec

Attribute	Value
Developer	Xiaomi
Released	2026-09-22
Announced	2026-09-22
Context window	1M tokens
Pricing	no vendor list price — weights are MIT and self-hostable; routed providers reported at $0.435/M input · $0.87/M output
License	MIT (open weights)
Availability	Hugging Face as XiaomiMiMo/MiMo-V2.6-Pro-RL; hosted via third-party routers
Catalogue id	unknown
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):

Model	AA Intelligence Index v4.3.2
MiMo-V2.6-Pro	46
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

2026-10-12 — first-party training evidence. The MiMo-V2.6 series report is now captured from arXiv; Agentic Reinforcement Learning records its training design. The abstract covers the series rather than a Pro-only benchmark table and does not settle this page’s Flash parameter-count conflict or validate the reported Artificial Analysis ranking. (source)

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.

Conflicting Reports

Is MiMo-V2.6-Flash 309B or 310B total parameters? (2026-09-30)

This page and Xiaomi record the sibling MiMo-V2.6-Flash at 309B/15B, from the third-party reporting available on 2026-09-23. Groupwise Agentic Grading and Advantage Redistribution for Code Agent RL, captured 2026-09-30, states "pre-RL SFT checkpoints of MiMo-V2.6-Flash (310B total parameters) and MiMo-V2.6-Pro (1.02T total parameters)" (source).

Not resolved here. The difference is 1B on a 310B model and is most likely rounding, but the paper is the better-attributed source — pre-RL checkpoints of a 1.02T model are not available outside the team that trained it, so this is effectively the developer's own figure against a journalist's. Per the conflict rule this wiki does not silently overwrite, and the two figures are both recorded until a first-party model card is readable. huggingface.co is blocked from this pipeline, which is where that card would be.

The 1.02T figure for this model is now independently confirmed by the same sentence, which is the more useful half of this entry: this page's headline parameter count came from reporting and now has a second, closer source agreeing exactly.

Sources

• (snapshot) — capture note, provenance of every figure, and what is not established
• 'Better than DeepSeek': Xiaomi's MiMo-V2.6-Pro debuts as the top open weights model in the world alongside cheaper V2.6-Flash
• [AINews] Xiaomi MiMo-V2.6-Pro 1T-A42B: the new top Open Weights model, trained for $3M