MiMo-V2.5, the Xiaomi (MiMo) model in numbers

MiMo-V2.5 at a glance

Xiaomi (MiMo) released MiMo-V2.5 on April 22, 2026. It has a reasoning mode: it can "think" before answering, which helps with multi-step problems but uses more tokens, and therefore more money. Its weights are published: you can download it and run it on your own servers, or use one of the 28 API providers that list it on models.dev. That makes it 6 months old as of October 22, 2026.

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

What it costs

MiMo-V2.5 costs $0.14 per million input tokens and $0.28 per million output tokens (developer's price, checked on October 22, 2026).

To put it in perspective: reading 20,000 tokens (about thirty pages) and writing 2,000 costs roughly $0.003; a thousand of those tasks, $3.36.

That is 3.6 times cheaper than the median output price of open-weight models ($1).

Tokens read back from cache cost $0.003 per million, which helps an agent that keeps resending the same context.

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

Measured performance

As of October 22, 2026, Epoch AI publishes no score for MiMo-V2.5 on our basket's benchmarks. To place it, rely on its specifications and price, then test it on your own cases.

Context and capabilities

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. Its knowledge stops at December 2024: beyond that, it needs web search or your documents.

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

The weights are published on Hugging Face (XiaomiMiMo/MiMo-V2.5). See also our list of open-source models.

Who is it for?

Given its category, MiMo-V2.5 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.

Its very low cost (under half a cent for our sample task) means it can be called thousands of times a day.

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

Comparable models

In the same category, the models closest to MiMo-V2.5 are:

• DeepSeek V4 Flash 0731 (DeepSeek, index 68.2)
• MiMo-V2.6-Flash (Xiaomi (MiMo), $0.28 per million output tokens)
• Granite 4.2 8B (IBM (Granite), $0.25 per million output tokens)
• Gemma 4 26B A4B IT (Google DeepMind, $0.34 per million output tokens)