MiMo-V2.6-Pro: Model Documentation & Integration Guide

MiMo-V2.6-Pro is a flagship sparse Mixture-of-Experts (MoE) model built for omnimodal intelligence and autonomous agentic workflows. It has 1.02 trillion total parameters, with 42 billion activated during inference, and is natively built to process text, image, video, and audio within a single, unified architecture.

With a 1,048,576-token (1M) context window and optimization via large-scale mixed reinforcement learning, MiMo-V2.6-Pro is designed for demanding technical tasks, including long-horizon coding, complex multi-tool interactions, and enterprise-grade cybersecurity analysis.

Key Innovations and Recent Updates

Xiaomi has positioned MiMo-V2.6-Pro as the flagship of the V2.6 series, introducing several methodologies aimed at AI self-improvement and multimodal reasoning.

Mixed Reinforcement Learning: “You Only RL Once”

At the heart of MiMo-V2.6-Pro is a training approach Xiaomi calls “You Only RL Once.” Rather than running separate reinforcement learning (RL) passes for different domains, the model is trained with a single, mixed RL run across coding, general agent tasks, visual processing, and cybersecurity. This lets capabilities reinforce one another, with the goal of transferring strategies to scenarios not directly covered during training.

This is paired with a self-improvement loop Xiaomi calls Groupwise Agentic Grading, which uses fully asynchronous Group Relative Policy Optimization (GRPO). The system evaluates and ranks the model’s own trajectories, steering it toward more efficient problem-solving paths while applying adversarial screening for alignment.

Omnimodal Capabilities

MiMo-V2.6-Pro includes a dedicated 681-million-parameter MiMo Vision Transformer (ViT) and separate audio encoders, allowing simultaneous analysis of multiple data types. Combined with the 1M-token context window, the model can take in entire code repositories, long tool traces, and multi-session agent runs without losing context.

Ecosystem Integration

Weights are available on Hugging Face and ModelScope. The model is integrated into Xiaomi’s own AI Studio and MiMo Code, and supports deployment via the SGLang and vLLM serving frameworks.

Performance Benchmarks

The table below reflects Xiaomi’s own published benchmark comparisons against Claude Opus 5 and GPT-5.6 Sol; these are vendor-reported figures rather than independently reproduced third-party results, and should be read accordingly.

Specialized Strengths

The following are also Xiaomi-reported figures:

• Cybersecurity: 94.0 on CyberGym and 80.2 on the MiMo Cyber Bench, aimed at exploit verification and autonomous threat mitigation.
• Scientific research: In “co-scientist” use cases, Xiaomi reports the model has been used to help design metal-organic frameworks for environmental adsorption, including literature review and hypothesis generation.
• Creative engineering: Native support for MIDI generation, orchestral composition, and end-to-end video creation, including beat-synced editing.

Technical Architecture

To keep inference fast despite its scale, MiMo-V2.6-Pro uses several architectural optimizations:

• Sparse MoE: 70 total layers (60 sliding-window attention / 10 global attention), balancing efficiency against long-range dependency modeling.
• Multi-Token Prediction (MTP): A 5-layer speculative decoder predicts up to 7 tokens per forward pass, improving throughput.
• Speculative decoding: An EAGLE-style algorithm verifies multiple tokens in parallel to reduce latency.

Getting Started with the API

The MiMo-V2.6-Pro API uses standard OpenAI-compatible HTTP POST endpoints, so it integrates easily into existing workflows.

Authentication

Requests require an API key passed via the standard HTTP Authorization header.

API Endpoints

• Hosted (DeepInfra): https://api.deepinfra.com/v1/openai/chat/completions
• Self-hosted (SGLang/vLLM): http://<host>:<port>/v1/chat/completions
• Model identifiers: XiaomiMiMo/MiMo-V2.6-Pro or XiaomiMiMo/MiMo-V2.6-Pro-RL, listed alongside every other hosted model in the DeepInfra model catalog

Implementation Example (Python)

import os
import requests
 
api_key = os.environ.get("DEEPINFRA_API_KEY")
url = "https://api.deepinfra.com/v1/openai/chat/completions"
 
payload = {
    "model": "XiaomiMiMo/MiMo-V2.6-Pro",
    "messages": [{"role": "user", "content": "Explain the role of sparse MoE in LLMs."}],
    "temperature": 1.0,
    "top_p": 0.95
}
 
response = requests.post(url, headers={"Authorization": f"Bearer {api_key}"}, json=payload)
print(response.json())
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Common Parameters

Pricing and Usage

Pricing scales with consumption and is billed per 1 million tokens, with a substantial discount on cached input tokens. Full current rates for MiMo-V2.6-Pro and every other hosted model are on the DeepInfra pricing page.

• Usage-based billing: Pricing scales directly with consumption, billed per 1 million tokens.
• UltraSpeed mode: Available via MiMo Desktop and API, offering substantially faster output speed for real-time interactions. Confirm current throughput figures for your workload, as serving-mode performance can vary by provider.

Conclusion

MiMo-V2.6-Pro combines a trillion-parameter MoE architecture with native omnimodal support and a 1M-token context window, giving developers the building blocks for long-horizon agentic applications and security tooling. Whether the workload is complex software engineering or advanced scientific research, MiMo-V2.6-Pro is built for reasoning depth and throughput at production scale.

For a deeper look at how it compares to other recent releases on cost and benchmarks, see our coverage of DeepSeek V4 Pro’s pricing and providers. For further technical detail, refer to the MiMo-V2.6 technical report or the developer community on Discord and Telegram.