Everything about MiMo-V2.6-Pro

MiMo-V2.6-Pro at a glance

MiMo-V2.6-Pro is a language model from Xiaomi (MiMo), released on September 22, 2026. It is a very recent release. It is an open-weight model: you can host it yourself on infrastructure you control, and 25 API providers listed on models.dev also offer it. It has a reasoning mode: it can "think" before answering, which helps with multi-step problems but uses more tokens, and therefore more money.

We file it under open-weight models (68 models in this category on the site).

How much does MiMo-V2.6-Pro cost?

MiMo-V2.6-Pro costs $0.44 per million input tokens and $0.87 per million output tokens (developer's price, checked on October 22, 2026).

In practice, an agent task that reads 20,000 tokens and writes 2,000 costs about $0.01, or $10.44 for 1,000 such tasks.

That is in line with open-weight models, whose median output price is $1.

Tokens read back from cache cost $0.004 per million, which helps an agent that keeps resending the same context.

To estimate your own budget, use the agent cost calculator.

What do independent benchmarks say?

Epoch AI publishes a single result for MiMo-V2.6-Pro on our basket's benchmarks: too few to compute a reliable capability index.

• APEX-Agents (professional tasks carried out by an agent): 54.4% — the best tracked model, Gemini 4 Argon, reaches 82.4%

Technical specifications

Its context window reaches 1,048,576 tokens, roughly 1,573 pages of text at 500 words per page: enough to load a whole book or a mid-sized codebase. A single answer can run to 131,072 tokens.

It accepts as input: text, image, audio and video.

It can call tools (functions, APIs) and return structured JSON output, which makes it a possible foundation for an AI agent.

The weights are published on Hugging Face (XiaomiMiMo/MiMo-V2.6-Pro-RL). See also our list of open-source models.

Who is it for?

Given its category, MiMo-V2.6-Pro is mainly aimed at projects where you want control over your data or hosting, or want to adapt the model to your field.

Its very large context window makes it worth considering for analysing large sets of documents in a single request.

Before adopting it, test it on your own examples: benchmarks measure standard tasks, not your job.

What could replace it?

In the same category, the models closest to MiMo-V2.6-Pro are:

• MiMo-V2.5-Pro (Xiaomi (MiMo), $0.87 per million output tokens)
• Trinity Large Thinking (Arcee AI, $0.80 per million output tokens)
• Llama-3.3-70B-Instruct (Meta, $0.76 per million output tokens)
• Qwen3.6 35B-A3B (Alibaba Cloud (Qwen), index 55.3)