MiMo-V2-Pro is a large language model built by Xiaomi — yes, the phone company. It has over 1 trillion total parameters, a 1 million token context window, and it’s specifically designed for autonomous AI agent tasks. It launched on March 18, 2026, after a week of anonymous stealth testing on OpenRouter under the codename “Hunter Alpha.”
If you’ve been following AI news, you probably heard about the mystery model that everyone thought was DeepSeek V4. It wasn’t. It was Xiaomi.
The basics
MiMo-V2-Pro is a mixture-of-experts (MoE) model. That means it has 1 trillion total parameters split across many “expert” sub-networks, but only activates 42 billion parameters for any given request. This is the same architectural approach used by DeepSeek V3 and Mixtral — it delivers near-frontier performance at a fraction of the compute cost.
| Spec | Value |
|---|---|
| Total parameters | ~1 trillion |
| Active parameters | 42 billion (MoE) |
| Context window | 1 million tokens |
| Max output | 32,000 tokens |
| Architecture | Hybrid attention MoE |
| Built by | Xiaomi (MiMo AI division) |
| Led by | Luo Fuli (ex-DeepSeek) |
| Released | March 18, 2026 |
How it works
Like all large language models, MiMo-V2-Pro predicts the next token in a sequence. What makes it different is the MoE architecture and its focus on agentic tasks.
Mixture of Experts: Instead of running every parameter for every request (like GPT-5.4 or Claude Opus), MiMo-V2-Pro routes each token through a subset of specialized “expert” networks. Only 42 billion of the 1 trillion parameters activate per inference. This means you get the knowledge capacity of a trillion-parameter model at the inference cost of a ~40B model.
Hybrid attention: The model uses a mixed attention mechanism optimized for long-context reasoning and tool use. This is what enables the 1 million token context window without the quality degradation that some models show at extreme context lengths.
Agent-first design: MiMo-V2-Pro was built from the ground up for multi-step autonomous tasks — not chat. It’s designed to plan, use tools, execute sequences, and recover from errors. Think: coding agents, research pipelines, automated workflows.
The Hunter Alpha story
On March 11, 2026, a model called “Hunter Alpha” appeared on OpenRouter with no attribution. It was free, had a 1M context window, and performed surprisingly well. The AI community immediately assumed it was DeepSeek V4 doing a stealth test.
The speculation made sense — the model had a similar “feel” to DeepSeek’s architecture, and it was clearly Chinese-built. It processed over 1 trillion tokens during its anonymous run, topping OpenRouter’s usage charts.
On March 18, Xiaomi revealed the truth: Hunter Alpha was an early test build of MiMo-V2-Pro. The DeepSeek connection wasn’t wrong — it was just indirect. Luo Fuli, who leads Xiaomi’s MiMo AI division, was a core contributor to DeepSeek’s R1 and V-series models before joining Xiaomi in late 2025.
Where it ranks
MiMo-V2-Pro has posted strong benchmark results across agent-focused evaluations:
| Benchmark | Score | Global Rank |
|---|---|---|
| Artificial Analysis Intelligence Index | 49 | #8 worldwide, #2 Chinese |
| PinchBench | ~81–84 | #3 globally |
| ClawEval | 61.5 | #3 globally |
| GDPval-AA (agentic Elo) | 1434 | #1 Chinese model |
For context, PinchBench and ClawEval are agent-focused benchmarks that test multi-step task completion — not just chat quality. MiMo-V2-Pro ranks right behind Claude Opus 4.6 variants on these tests, and ahead of several GPT-5.x iterations.
Pricing
This is where it gets interesting. MiMo-V2-Pro is aggressively priced:
| Context length | Input (per 1M tokens) | Output (per 1M tokens) |
|---|---|---|
| ≤ 256K tokens | $1.00 | $3.00 |
| 256K – 1M tokens | $2.00 | $6.00 |
For comparison, Claude Opus 4.6 costs $5/$25 and GPT-5.4 costs $2.50/$15. MiMo-V2-Pro is roughly 5–8x cheaper on output than Opus and 3–5x cheaper than GPT-5.4.
The MiMo-V2 family
MiMo-V2-Pro isn’t alone. Xiaomi launched three models simultaneously:
- MiMo-V2-Pro — The flagship text reasoning model (this article)
- MiMo-V2-Omni — A multimodal model that processes text, images, video, and audio natively. Priced at $0.40/$2.00 per million tokens.
- MiMo-V2-TTS — A text-to-speech model with emotion control, singing capability, and Chinese dialect support.
There’s also MiMo-V2-Flash, an open-source smaller model (309B total, 15B active) that scores 73.4% on SWE-bench Verified — making it the top open-source coding model.
How to access it
MiMo-V2-Pro is available through:
- Xiaomi’s MiMo API platform at platform.xiaomimimo.com
- OpenRouter (where it originally appeared as Hunter Alpha)
- MiMo Studio (Xiaomi’s web interface)
The API is OpenAI-compatible, so you can swap it into existing code that uses the OpenAI SDK with minimal changes.
Why it matters
Three reasons MiMo-V2-Pro is significant:
1. A phone company built a frontier model. Xiaomi isn’t an AI lab. They’re a consumer electronics company. The fact that they can build a model that competes with Anthropic and OpenAI on agent benchmarks — while pricing it at a fraction of the cost — says something about how accessible frontier AI development has become.
2. The agent era is real. MiMo-V2-Pro is explicitly not a chatbot. It’s an agent model. Every major lab is now building for autonomous multi-step execution, not conversation. If you’re still thinking of AI as “a thing I chat with,” you’re behind.
3. Price pressure is accelerating. At $1/$3 per million tokens, MiMo-V2-Pro puts serious pressure on Western pricing. Even if the quality is slightly below Opus 4.6, the 5–8x cost difference makes it viable for high-volume production workloads where “good enough” beats “best but expensive.”
Should you use it?
If you’re building AI agents, automated pipelines, or high-volume processing tasks — absolutely worth testing. The price-to-performance ratio is compelling, especially for tasks that don’t require the absolute best model.
For critical coding tasks where accuracy matters most, Claude Opus 4.6 and GPT-5.4 still have the edge. But for everything else, MiMo-V2-Pro just made the conversation a lot more interesting.
For a detailed head-to-head, see MiMo-V2-Pro vs Claude vs GPT: Where Xiaomi’s Model Actually Stands, MiMo-V2-Pro vs Claude Opus 4.6, or MiMo-V2-Pro vs DeepSeek V3.
Related: AI Model Comparison 2026: Claude vs ChatGPT vs Gemini
Related: AI Dev Weekly Extra: Xiaomi’s Hunter Alpha Was Never DeepSeek V4