Xiaomi MiMo-V2.5-Pro: The Best Open-Source LLM?

Official MiMo-V2.5-Pro benchmark results — Source: Xiaomi / Hugging Face

Xiaomi just released a model that beats Claude Opus 4.6 and GPT-5.4 on the benchmark that actually matters for agentic work. It uses 40 to 60% fewer tokens to get the job done. It is MIT licensed. And you can run it for free today. This is not a “good for its size” story. MiMo-V2.5-Pro competes at the frontier, and it does it cheaper than every closed model on the list.

What is MiMo-V2.5-Pro?

MiMo-V2.5-Pro is Xiaomi’s latest open-source model, released in late April 2026. It is a 1.02 trillion parameter Mixture-of-Experts (MoE) model with a 1 million token context window.

Key specs at a glance:

• 1.02T total parameters, 42B active per token
• 1M token context window (256K base version also available)
• Pre-trained on 27 trillion tokens
• MIT license — commercial use, fine-tuning, modification all allowed
• Available on Hugging Face: XiaomiMiMo/MiMo-V2.5-Pro

The key insight: 1 trillion parameters sounds expensive to run. It is not. That is the whole point of the MoE design.

How 1 Trillion Parameters Run on 42 Billion

Most people see “1T parameters” and assume it is slow and expensive. MoE flips that assumption.

Here is how it works: the model is divided into many specialized expert networks. For each token, a lightweight router picks only the top few experts to activate. The rest sit idle. So you get the knowledge of a trillion-parameter model while only paying the compute cost of a 42B one.

fig-1

MiMo-V2.5-Pro architecture — Source: Xiaomi / Hugging Face

MiMo also adds a hybrid attention design: local sliding window attention (128-token window) and global attention are interleaved at a 6:1 ratio. This cuts KV-cache storage by nearly 7x at long context without losing performance. That is the reason it can handle 1M tokens without destroying your hardware budget.

The Benchmark That Actually Matters

Most LLM benchmarks test knowledge recall. ClawEval tests something harder: can the model complete a real agentic task, across many tool calls, without breaking?

MiMo-V2.5-Pro scores 64% Pass³ on ClawEval, using roughly 70,000 tokens per trajectory.

Same score. 40 to 60% fewer tokens. In production agentic systems that run thousands of tasks a day, that is not a marginal improvement. It is a cost structure change.

What It Actually Built

Benchmarks are one thing. Here is what MiMo-V2.5-Pro shipped in autonomous test runs:

• Full SysY compiler in Rust — lexer, parser, and RISC-V assembly backend — built from scratch, 233/233 tests passed, in 4.3 hours across 672 tool calls
• 8,192-line video editor — multi-track timeline, effects engine, built across 1,868 tool calls
• Analog FVF-LDO circuit design — optimized a regulator meeting 6 simultaneous performance constraints

These are not toy tasks. These are the kinds of projects that take a mid-level engineer days. MiMo-V2.5-Pro ran them end-to-end, unsupervised, and shipped working code.

How to Use MiMo-V2.5-Pro for Free

Option 1: AI Studio (no setup, free)

1. Go to aistudio.xiaomimimo.com
2. Sign in and start a session
3. Full 1M context window, no API key needed

Option 2: OpenRouter API (cheapest paid option)

• Model tag: xiaomi/mimo-v2.5-pro
• Input: $1 per million tokens
• Output: $3 per million tokens
• Works with any OpenAI-compatible client

from openai import OpenAI

client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key="your_openrouter_key"
)
response = client.chat.completions.create(
    model="xiaomi/mimo-v2.5-pro",
    messages=[{"role": "user", "content": "Build me a compiler"}]
)

Option 3: Self-host (full control, MIT license)

# Pull weights from Hugging Face
huggingface-cli download XiaomiMiMo/MiMo-V2.5-Pro

# Serve with vLLM or SGLang (both officially supported)
python -m vllm.entrypoints.openai.api_server \
  --model XiaomiMiMo/MiMo-V2.5-Pro \
  --tensor-parallel-size 8

Option 4: Use the lighter MiMo-V2.5 If you do not need the full Pro model, the base MiMo-V2.5 costs $0.40/M input and $2/M output on OpenRouter. Same architecture, lower price.

Final Thoughts

The narrative around open-source AI has always been “good enough, but not quite frontier.” MiMo-V2.5-Pro ends that argument, at least for agentic coding work.

Same benchmark scores as the top closed models. 40 to 60% cheaper to run. MIT licensed so you own the deployment. That combination has not existed before at this capability level.

We are moving from open-source models that approximate frontier to open-source models that are the frontier. MiMo-V2.5-Pro is the clearest proof of that shift so far.

Try it on AI Studio today. It is free, it takes 30 seconds to start, and the 1M context window means you can throw your entire codebase at it.

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