Top-Tier Intelligence
The page states that MiMo-V2-Pro ranks 8th worldwide and 2nd among Chinese LLMs on the Artificial Analysis Intelligence Index.
Overview
According to the referenced Xiaomi page published on March 18, 2026, MiMo-V2-Pro is positioned as a flagship agent foundation model with top-tier global capability, stronger real-world task execution, and public API availability. It is explicitly designed not only to answer questions, but to complete tasks across production scenarios.
Trillion-Scale Base
MiMo-V2-Pro surpasses 1T total parameters with 42B active parameters, making it roughly three times larger than MiMo-V2-Flash in total scale.
1M Tokens
The model supports up to a 1M-token context window, giving it room for high-intensity long-horizon agent flows and complex production tasks.
This page is based on the official Xiaomi MiMo-V2-Pro page referenced by the user and stays within the product claims presented there, including architecture, benchmark positioning, coding capability, frontend examples, and API pricing.
Foundation and Architecture
MiMo-V2-Pro scales both model size and compute to strengthen the base model while preserving inference efficiency for real deployment.
Hybrid Attention at Larger Scale
MiMo-V2-Pro inherits Hybrid Attention from its predecessor and increases the hybrid ratio from 5:1 to 7:1. The official page presents this as a path to significantly greater scale while maintaining high inference efficiency.
Fast Generation Path
A lightweight MTP, or Multi-Token Prediction, layer is described as helping the model generate responses faster while operating at flagship scale.
From Chat to Agent
MiMo-V2-Pro is framed as moving beyond polished demos and question answering. Its goal is to act as the core brain behind systems and workflows that deliver real-world impact continuously.
Post-Launch Iteration
The Xiaomi page notes that the early internal build known as Hunter Alpha saw heavy usage on OpenRouter and that subsequent iteration improved long-context capability and agent-scenario stability.
Built for Agents
MiMo-V2-Pro is presented as deeply optimized for agentic scenarios, especially through training across complex and diverse agent scaffolds.
The Native Brain of OpenClaw
The official page describes MiMo-V2-Pro as fine-tuned with supervised fine-tuning and reinforcement learning across complex agent scaffolds, strengthening tool calls and multi-step reasoning for OpenClaw-style systems.
Benchmark Positioning
On the referenced page, MiMo-V2-Pro records 81.0 on PinchBench and 61.5 on ClawEval, both positioned as globally leading results, with ClawEval described as approaching Opus 4.6.
Tool Stability and Accuracy
Xiaomi states that tool-call stability and accuracy were significantly improved, with training optimized around practical user experience rather than benchmark-only outcomes.
Long-Horizon Application Support
With its 1M-token context window, MiMo-V2-Pro is positioned to support high-intensity real-world Claw application flows more comfortably.
Coding and Frontend Development
The official page gives MiMo-V2-Pro a strong software engineering and frontend execution positioning, extending beyond lightweight generation into serious development workflows.
Software Engineering Use
Xiaomi's internal engineering evaluation is described as putting the MiMo-V2-Pro experience near Claude Opus 4.6, with stronger system design, task planning, elegant code style, and efficient problem-solving paths.
Coding Workflow Adoption
During the Hunter Alpha testing phase, the top applications by call volume were said to be coding-focused tools, which Xiaomi uses as evidence of usability and reliability in developer workflows.
Framework Ecosystem
The page lists cooperation with OpenClaw, OpenCode, KiloCode, Blackbox, and Cline, alongside one week of free API access for developers worldwide.
Agentic Frontend Completion
In frontend scenarios, MiMo-V2-Pro is presented as capable of generating polished and fully functional web pages in a single query, balancing visual quality and practical usability across complex prompt styles.
1M Context and Open API
The official page states that the MiMo-V2-Pro API is publicly available with up to 1M-token context support and tiered pricing based on context range.
| Model Tier | Input / 1M Tokens | Output / 1M Tokens | Cache Read | Cache Write |
|---|---|---|---|---|
| MiMo-V2-Pro up to 256K | $1 | $3 | $0.20 | $0 |
| MiMo-V2-Pro 256K-1M | $2 | $6 | $0.40 | $0 |
Developer Access
Public API access is available through the Xiaomi MiMo platform, with the referenced page positioning MiMo-V2-Pro as a production-ready foundation for developer and agent systems.
Value Positioning
The official comparison on the page places MiMo-V2-Pro below the listed Claude Sonnet 4.6 and Claude Opus 4.6 token pricing while offering 1M-context access and zero-cost temporary cache write pricing.
Frequently Asked Questions
What makes MiMo-V2-Pro different from a standard chat model?
According to the official Xiaomi page, MiMo-V2-Pro is not positioned only for answers or demos. It is designed to complete tasks and act as the core intelligence behind agent systems and workflows.
How large is MiMo-V2-Pro?
Xiaomi states that MiMo-V2-Pro surpasses 1T total parameters with 42B active parameters and supports up to a 1M-token context window.
How is MiMo-V2-Pro positioned on agent benchmarks?
The referenced page places MiMo-V2-Pro at 81.0 on PinchBench and 61.5 on ClawEval, presenting it as globally leading and approaching Opus 4.6 on ClawEval.
Can MiMo-V2-Pro be used for coding and frontend generation?
Yes. Xiaomi explicitly presents MiMo-V2-Pro as usable in serious software engineering workflows and as capable of generating polished, functional frontend experiences from detailed prompts.
Official Resources
Primary Links
Navigation
Return to the MiMo-V2 family overview to compare MiMo-V2-Pro with MiMo-V2-Omni, MiMo-V2-TTS, and MiMo-V2-Flash.