Xiaomi MiMo

Xiaomi MiMo

Freemium

Open-weight reasoning and coding models from Xiaomi.

LLM
Open-weight

Published 27 September 2026

Scores

Popularity3/5

The benchmark standing draws real attention, but as Xiaomi's first entry into the open-weight LLM race it has less developer mindshare and third-party adoption than DeepSeek, Qwen, or Moonshot.

Learning Curve3/5

The OpenAI-compatible API is a trivial base-URL swap and GGUF quantizations run on modest hardware, but self-hosting the full MoE model well requires multi-GPU serving and vLLM/SGLang experience.

Flexibility5/5

Open weights under MIT can be fine-tuned, quantized, and served on any stack, with base and instruct variants covering reasoning, speed, and speech recognition.

Performance5/5

Ranks among the strongest open-source models on agentic and coding benchmarks and completes comparable tasks with notably fewer tokens than frontier proprietary models.

Portability5/5

MIT-licensed Hugging Face weights with vLLM, SGLang, Transformers, and GGUF support mean it runs anywhere with no vendor lock-in.

About Xiaomi MiMo

MiMo is Xiaomi's family of open-weight language models, published on Hugging Face under the MIT license with no commercial-use restrictions. The current MiMo-V2.6 generation is natively omnimodal, taking text, image, video, and audio input, and comes in two sizes: MiMo-V2.6-Pro, a 1.02T-parameter mixture-of-experts model with about 42B active parameters, and MiMo-V2.6-Flash, with 309B total and 15B active parameters, both with a 1M-token context window. A hosted UltraSpeed mode serves the Pro weights with speculative decoding for much faster output, and a 9B distillation built on Qwen targets smaller deployments.

MiMo-V2.6-Pro scores highest among open-weight models on the Artificial Analysis Intelligence Index and matches or beats Claude Opus 5 on several agentic and coding benchmarks, including Terminal-Bench 2.1 and AutomationBench. Xiaomi also published the training code and about 7,000 reinforcement-learning task environments alongside the weights, an unusually open recipe for a model at this scale.

The weights self-host through vLLM or SGLang, and GGUF quantizations run in llama.cpp, Ollama, LM Studio, and Jan, though the full Pro model needs multi-GPU hardware. Hosted, OpenAI-compatible API access is available through Xiaomi's own platform (AI Studio, MiMo Code, and MiMo Desktop) and OpenRouter at low per-token prices, with the UltraSpeed mode priced at a premium for latency-sensitive work.

Key Features

  • MiMo-V2.6-Pro: 1.02T-param MoE (~42B active), omnimodal input, 1M-token context
  • MiMo-V2.6-Flash: 309B total / 15B active, same context, tuned for cost and latency
  • Pro-UltraSpeed hosted mode: much faster generation via speculative decoding
  • Top open-weight score on the Artificial Analysis Intelligence Index and strong agentic benchmarks
  • Training code and about 7,000 RL task environments published
  • MIT license with no commercial-use restrictions
  • Self-hostable via vLLM, SGLang, and GGUF (llama.cpp, Ollama, LM Studio, Jan)
  • OpenAI-compatible hosted API through Xiaomi's platform and OpenRouter

Pros

  • Fully open MIT weights with no commercial-use restrictions
  • Strong agentic/coding benchmark standing at a fraction of frontier-model token cost
  • Multiple deployment paths — self-host, GGUF quantization, or hosted API
  • OpenAI-compatible API makes it a drop-in swap for existing integrations

Cons

  • Xiaomi is a newcomer to the LLM space, so ecosystem tooling and community depth trail established open-weight labs
  • Self-hosting the full 1.02T Pro model requires substantial multi-GPU infrastructure
  • Third-party hosting availability is still thinner than for DeepSeek, Qwen, or Llama
  • Documentation is concentrated on Hugging Face model cards rather than a full docs site

Xiaomi MiMo Pricing

Freemium
Self-Hosted (Open Weight)Free
  • · MiMo-V2.6-Pro and MiMo-V2.6-Flash weights free to download under MIT license, no commercial-use restrictions
  • · Deploy via vLLM/SGLang, or GGUF quantizations for llama.cpp/Ollama/LM Studio/Jan
  • · Compute cost only, no platform fee
Hosted API — FlashContact sales
  • · $0.14 per 1M input tokens / $0.28 per 1M output tokens
  • · Lowest-cost tier; via Xiaomi's AI Studio/MiMo Code and OpenRouter
Hosted API — ProContact sales
  • · $0.435 per 1M input tokens / $0.87 per 1M output tokens
  • · Flagship omnimodal reasoning tier; via Xiaomi's platform and OpenRouter
Hosted API — Pro-UltraSpeedContact sales
  • · $4.35 per 1M input tokens / $8.70 per 1M output tokens
  • · Same Pro weights served with speculative decoding for up to 20x faster output, for latency-sensitive use

Tech Stacks with Xiaomi MiMo

OpenCode Dev Stack

Developer

OpenCode as the coding agent, in the terminal, the desktop app, an IDE extension, or the browser, running open-weight models such as DeepSeek, GLM, and Kimi, with your pick of GitHub or GitLab for version control.

LLM:
Version Control:
Server add-on:
Remote Access add-on:
Session Persistence add-on:
Terminal add-on:
Code Review add-on:
Model Aggregator add-on:
CI/CD add-on:
Containerization add-on:

Tools Related to Xiaomi MiMo

Works well with Xiaomi MiMo(4)

MiMo models are served through OpenRouter, giving applications hosted access without self-hosting the weights.

MiMo runs locally through Ollama via community GGUF quantizations, no cloud API required.

MiMo's open weights, tokenizer, and model cards are published on Hugging Face for download and deployment.

Unsloth publishes GGUF quantizations of Xiaomi MiMo and can fine-tune it, making the model runnable on smaller hardware.

Alternatives to Xiaomi MiMo(8)

Llama is the most widely supported open family, from edge sizes to Llama 4 MoE; Xiaomi MiMo is an MIT-licensed 1T MoE focused on agentic and omnimodal tasks. Llama for ecosystem support, MiMo for a permissive frontier-scale model.

Mistral spans edge models to a MoE flagship with European API and cloud availability; Xiaomi MiMo is an MIT-licensed 1T MoE aimed at agentic tasks. Mistral for EU deployment, MiMo for permissive frontier-scale weights.

Both are open-weight model families strong on reasoning and coding; teams choosing an open-weight LLM typically compare MiMo against DeepSeek on benchmarks and cost.

MiMo and Qwen are both open-weight model families with strong agentic/coding results — competing picks for a self-hostable or API-served open model.

MiMo and MiniMax are both recent open-weight MoE families topping open-source agentic leaderboards; usually evaluated head-to-head when selecting an open model.

Gemma focuses on compact models for self-hosting and edge devices; Xiaomi MiMo's 1T MoE flagship targets agentic tasks with omnimodal input under MIT. Gemma for lightweight deployment, MiMo for frontier-scale open weights.

Learning Resources

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Vendor

Tags

Open SourceSelf-hostableFree TierAI-poweredMachine Learning

Details

Maintained
Yes