Xiaomi: MiMo-V2.5-Pro
xiaomi/mimo-v2.5-pro
Try MiMo-V2.5-Pro for free in your browser, and add it to your app for free with Puter.js AI API.
Try it free Add to your app// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms", {
model: "xiaomi/mimo-v2.5-pro"
}).then(response => {
document.body.innerHTML = response.message.content;
});
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat("Explain quantum computing in simple terms", {
model: "xiaomi/mimo-v2.5-pro"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
Model Card
MiMo V2.5 Pro is Xiaomi's most capable model, built for complex software engineering, long-horizon agentic tasks, and autonomous multi-step workflows spanning over a thousand tool calls.
It scores 57.2 on SWE-bench Pro, 63.8 on ClawEval, and 72.9 on τ3-Bench — placing it alongside Claude Opus 4.6 and GPT-5.4 across most agentic evaluations. Notably, it achieves this while using roughly 40–60% fewer tokens per trajectory than comparable frontier models. The 1M-token context window and 131K max output support entire codebases and extended autonomous sessions.
Priced at $1.00 per million input tokens and $3.00 per million output tokens, MiMo V2.5 Pro targets developers building autonomous agents, code-generation pipelines, and complex tool-use workflows where sustained coherence over long contexts is critical.
Context Window 1M
tokens
Max Output 131K
tokens
Input Cost $1
per million tokens
Output Cost $3
per million tokens
Release Date Apr 22, 2026
Try MiMo-V2.5-Pro for free
Try MiMo-V2.5-Pro instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
Benchmarks
How MiMo-V2.5-Pro performs on standard evaluations.
| Benchmark | Score |
|---|---|
| GPQA Diamond Graduate-level science Q&A | 86.6% |
| Humanity's Last Exam Cross-domain reasoning | 35.7% |
| SciCode Scientific programming | 50.6% |
| IFBench Instruction following | 79.9% |
| LCR Long-context reasoning | 79.7% |
| Terminal-Bench Hard Agentic terminal tasks | 43.2% |
| τ²-Bench Tool use / agents | 94.2% |
Scores sourced from Artificial Analysis.
Find other Xiaomi models →
MiMo-V2.6-Flash
MiMo-V2.6-Flash is Xiaomi's cost-efficient omnimodal model in the MiMo-V2.6 series, a sparse Mixture-of-Experts model with 309B total parameters (15B active) that processes text, image, video, and audio in a single architecture with a 1M-token context window. It uses a hybrid attention design that interleaves sliding-window and global attention with a lightweight Multi-Token Prediction module, and scores 87.6 on Terminal-Bench 2.1 and 95.1 on CyberGym, staying close to its larger sibling MiMo-V2.6-Pro on several agent benchmarks despite the lower price. Priced at $0.14 per million input tokens and $0.28 per million output tokens, MiMo-V2.6-Flash fits agent pipelines that need multimodal input and long context without paying flagship rates.
ChatMiMo-V2.6-Pro
MiMo-V2.6-Pro is Xiaomi's flagship model in the MiMo-V2.6 series, a sparse Mixture-of-Experts model with 1.02 trillion total parameters (42B active) and a 1M-token context window, built for long-horizon agentic work across coding, tool use, and multimodal reasoning. It scored 46 on the Artificial Analysis Intelligence Index, the highest reported for an open-weight model at release, and reaches 89.9 on Terminal-Bench 2.1 and 94.0 on CyberGym. Artificial Analysis measured its cost per index task at $0.13, against $5.86 for Claude Opus 5. Priced at $0.44 per million input tokens and $0.87 per million output tokens, MiMo-V2.6-Pro targets developers who want agentic performance close to frontier proprietary models at a fraction of the cost.
ChatMiMo-V2.6-Pro-UltraSpeed
MiMo-V2.6-Pro-UltraSpeed is a latency-optimized serving variant of MiMo-V2.6-Pro, built from the same 1.02-trillion-parameter checkpoint and the same 1M-token context window, tuned for response speed rather than lowest cost per token. Xiaomi describes it as running MiMo-V2.6-Pro at up to 20x faster output, with early third-party measurements putting the gain closer to 10x throughput. Because it shares Pro's weights, its accuracy and benchmark scores track the base model; only the serving path changes. Priced at $4.35 per million input tokens and $8.70 per million output tokens, roughly ten times standard Pro pricing, MiMo-V2.6-Pro-UltraSpeed is meant for latency-sensitive agent and chat applications, not cost-sensitive batch workloads.
Frequently Asked Questions
You can access MiMo-V2.5-Pro by Xiaomi through Puter.js AI API. Include the library in your web app or Node.js project and start making calls with just a few lines of JavaScript — no backend and no configuration required. You can also use it with Python or cURL via Puter's OpenAI-compatible API.
MiMo-V2.5-Pro is free to try with a Puter account. Every account includes a free AI allowance, and you can chat with it in the playground on this page. You can upgrade your account anytime for a larger allowance.
MiMo-V2.5-Pro is free to integrate using the Puter.js AI API. With the User-Pays Model, you can add AI to your app for $0, since users cover their own AI usage through their Puter account.
| Price per 1M tokens | |
|---|---|
| Input | $1 |
| Output | $3 |
MiMo-V2.5-Pro was created by Xiaomi and released on Apr 22, 2026.
MiMo-V2.5-Pro supports a context window of 1M tokens. For reference, that is roughly equivalent to 2,097 pages of text.
MiMo-V2.5-Pro can generate up to 131K tokens in a single response.
MiMo-V2.5-Pro scores 26.0 on the Artificial Analysis Intelligence Index, outperforming 78% of tracked models. On coding, it scores 60.2 (outperforms 69% of models).
Yes — the MiMo-V2.5-Pro API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.
Add MiMo-V2.5-Pro to your app for free
Developers can integrate MiMo-V2.5-Pro for free using the Puter.js AI API.
With the User-Pays Model, each user covers their own AI usage instead of the developer.