Every Xiaomi MiMo Model Explained and Compared [2026]

MiMo-V2.6-Pro: The Flagship

Artificial Analysis spent $0.13 per task running V2.6-Pro through its whole index, and the resulting score of 46 is the top mark for any open-weights model. The same page calls it slow: 42 tokens a second at the median provider.

It is the model to pick when the answer matters more than the wait.

Under the hood it is a 70-layer mixture-of-experts. Sixty layers use sliding-window attention over the last 128 tokens and ten use global attention, which keeps the KV cache small at a 1M-token context.

A 681M-parameter vision encoder and a 308M audio tokenizer sit in front of the language backbone, and a five-layer drafter predicts seven tokens per pass for speculative decoding. The MiMo-V2.6-Pro-RL model card lists all of it.

Xiaomi's own benchmark table puts the jump from V2.5-Pro at its widest on long agent tasks. DeepSWE v1.1, a long-horizon software engineering test, went from 19.0 to 71.9.

Terminal Bench 4.0 went from 1.5 to 34.9, still well short of the 49.0 the same table gives Claude Opus 5.

Five days after launch Xiaomi pushed a second checkpoint, MiMo-V2.6-Pro-MOPD. Its model card says the first release sometimes repeated the same tool call over and over inside agent loops, "appearing busy while making no progress".

The MOPD checkpoint folds a short specialized-teacher pass into training to cut that repetition. If you self-host V2.6-Pro for agents, start from the MOPD weights.

What the V2.6 Training Run Cost

Xiaomi streamed the V2.6 reinforcement learning run live and published the bill on its launch page.

Each model completed 30 RL steps over roughly 750,000 trajectories in under six days, with 1,568 samples per update and 3.5 to 3.7 billion tokens per step.

The two runs together cost about $3.47 million. Over those 30 steps, V2.6-Pro's DeepSWE score rose from 58.4 to 72.57 and Flash's from 48.8 to 65.68.

Xiaomi says it is releasing the technical report, training environments and RL code so others can reproduce the run. The report is a PDF inside the Pro model repository.

"In an era when compute is brutally scarce, we still chose to dedicate a team of several dozen people to one goal over an extended period: scaling up RL."

Fuli Luo, head of Xiaomi MiMo, quoted by VentureBeat, September 2026

MiMo-V2-Flash, V2-Pro and V2-Omni

The V2 generation is where MiMo turned from a research model into a product. Three months separated the first open MoE from the first trillion-parameter one.

MiMo-V2-Flash introduced the design every later model kept: five sliding-window layers to one global layer, a 128-token window, and three multi-token prediction layers that Xiaomi says roughly triple output speed.

It launched at 73.4 on SWE-bench Verified.

MiMo-V2-Pro raised the ratio to seven to one and the size past one trillion parameters, priced at $1 in and $3 out up to 256K tokens and double that beyond.

V2-Omni added image, video and audio understanding with a 256K window, and V2-TTS was a speech model pretrained on more than 100 million hours of audio. Only V2-Flash ever had open weights, and they are still on Hugging Face.

Speech Models: MiMo-V2.5-TTS and ASR

Xiaomi's speech line now sits on its own version track.

The MiMo-V2.5-TTS series launched on April 23, 2026 as three models: mimo-v2.5-tts with preset voices, mimo-v2.5-tts-voicedesign, which builds a new voice from a one-sentence description, and mimo-v2.5-tts-voiceclone, which copies a voice from a few samples.

All three are still free for a limited time. MiMo-V2.5-ASR is the open one: MIT weights for Mandarin, English, code-switched speech and dialects including Wu, Cantonese, Hokkien and Sichuanese. On the API it costs $0.074 per hour of audio.

MiMo Pricing and the Token Plan

Xiaomi has cut MiMo prices since launch. V2-Pro opened at $3 per million output tokens; the V2.6-Pro that replaced it costs $0.87. The one rate that went up is Flash input, from $0.10 on V2-Flash to $0.14 today.

V2-Flash requests have routed to the larger omnimodal V2.5 since June 18.

A cache hit on V2.6-Pro costs less than 1% of a miss, and cache writes are free for now, so long agent sessions that resend the same context cost a fraction of the list rate. Web search is billed on top at $5 per 1,000 calls outside China.

For developers who work inside coding agents, the Token Plan is a monthly subscription at $6, $16, $50 or $100, paid in credits.

A V2.6-Pro output token costs 600 credits and a Flash output token 200, and usage between 00:00 and 08:00 Beijing time burns 20% fewer credits. Xiaomi says it works with OpenCode, OpenClaw and Claude Code.

The models are also on OpenRouter at the same list prices.

License and Self-Hosting

MIT is as permissive as model licenses get. There is no revenue threshold, no attribution rule and no separate agreement for model-as-a-service businesses, which is not true of Kimi K3 or the big Qwen3.8 weights.

Our MiMo vs Kimi vs Qwen comparison sets the three licenses side by side.

For most teams the API is cheaper: a 1T-parameter model needs a multi-GPU server running all day, and only steady, heavy traffic pays for one.

Flash is the realistic self-hosting target: 15B active parameters is a lighter decode load, and Xiaomi's SGLang and vLLM recipes cover it.

Build on MiMo With the Right Engineers

Wiring MiMo into an agent harness, or serving a trillion-parameter model on your own GPUs, takes engineers who have done it before. We place vetted LLM developers from Asia, and our LLM developer cost guide shows what a hire costs.

Tell us what you are building and we will send a shortlist within 24 hours.