MiMo V2.6 Pro API

What Is MiMo-V2.6-Pro?

MiMo-V2.6-Pro is Xiaomi MiMo's flagship multimodal reasoning model, built for complex projects, long-horizon tasks, professional workflows, research, cybersecurity, coding, and agentic applications.

The model supports native understanding of text, images, video, and audio within a unified model. It provides a 1-million-token context window and supports up to 128K output tokens, making it suitable for large repositories, lengthy documents, extended tool traces, and multi-session agent workflows.

The open-weight MiMo-V2.6-Pro-RL checkpoint uses a sparse Mixture-of-Experts architecture with 1.02 trillion total parameters and 42 billion activated parameters. Its architecture includes a 681M-parameter vision encoder, dedicated audio encoders, and a five-layer speculative decoder.

Technical Specifications

Xiaomi's official model page lists 100 RPM and 10M TPM for the model API.

Main Features of MiMo-V2.6-Pro

1. Native Omni-Modal Understanding

MiMo-V2.6-Pro is designed to jointly understand text, images, video, and audio rather than limiting multimodal processing to a separate auxiliary workflow.

This allows applications to combine different forms of information within a single reasoning process, including documents with images, video-based analysis, audio content, screenshots, and textual instructions.

2. 1M-Token Long Context

The model provides a context window of up to 1 million tokens. This makes it suitable for workflows involving large codebases, long documents, extensive research material, persistent agent histories, and large tool-call traces.

A long context window is particularly useful for agentic systems because substantial task history and intermediate results can remain available without repeatedly compressing the conversation.

3. Advanced Reasoning and Agent Capabilities

MiMo-V2.6-Pro is designed around reasoning-intensive and long-horizon workflows. Xiaomi highlights complex projects, multi-agent collaboration, and professional workloads as key use cases. The model also supports deep thinking and tool calling.

These capabilities make it suitable for:

• Complex software engineering
• Research agents
• Multi-step planning
• Tool orchestration
• Computer-use workflows
• Cybersecurity analysis
• Knowledge-intensive tasks

4. Tool Calling and Structured Output

MiMo-V2.6-Pro supports tool calling and structured output, allowing applications to connect the model with external APIs, databases, search systems, business tools, and other software components.

Structured output is particularly useful when model responses need to be consumed by downstream application logic rather than displayed only as natural-language text.

5. Long-Horizon Agent Workflows

The combination of long context, multimodal understanding, reasoning, and tool use allows MiMo-V2.6-Pro to operate on workflows that require multiple stages of planning and execution.

Xiaomi describes the MiMo-V2.6 series as being optimized for multimodal, multi-agent, and multi-harness collaboration. Its official examples include coding, office work, design, research, content creation, cybersecurity, and computer operation.

6. Open-Weight Model Architecture

MiMo-V2.6-Pro-RL is released as an open-weight checkpoint. The model card documents a sparse MoE architecture with 1.02T total parameters and 42B activated parameters, alongside dedicated vision and audio encoders.

The model card also documents the reinforcement-learning methodology used to improve coding, general-agent, visual-agent, and cybersecurity capabilities.

Benchmark Performance

Xiaomi's published evaluation covers coding agents, general agents, cybersecurity, and visual-agent tasks. These are provider-published evaluation results, so they should be distinguished from independent third-party benchmark studies.

Notable reported results include 89.9 on Terminal Bench 2.1, 82.0 on OSWorld-Verified, 76.9 on Toolathlon-Verified, and 94.0 on CyberGym.

Xiaomi's official model page also reports an Artificial Analysis composite intelligence index score of 46.32. Because this is a third-party evaluation referenced by Xiaomi, it is best treated separately from Xiaomi's own benchmark suite.

MiMo-V2.6-Pro vs MiMo-V2.6-Flash

MiMo-V2.6-Pro and MiMo-V2.6-Flash share the same broad multimodal and long-context design, but they target different model scales.

The main architectural distinction is model scale: the Pro checkpoint has 1.02T total / 42B activated parameters, compared with 309B total / 15B activated parameters for Flash.

MiMo-V2.6-Pro vs Other Reasoning Models

MiMo-V2.6-Pro combines several capabilities that are often distributed across different model categories: multimodal understanding, long-context reasoning, coding, tool use, and agentic execution.

Its 1M-token context differentiates it from models with substantially smaller context windows, while its native image, video, and audio inputs extend it beyond text-centric reasoning systems. Its sparse MoE architecture also separates total model capacity from the number of parameters activated for each token.

For agent-oriented applications, its published evaluation suite covers software engineering, general tool use, computer interaction, cybersecurity, and visual coding rather than focusing exclusively on conventional language benchmarks.

Representative Use Cases

Complex Software Engineering

MiMo-V2.6-Pro can support code generation, repository analysis, debugging, software architecture, task planning, and autonomous coding workflows.

Its published evaluations include DeepSWE v1.1, ProgramBench, MiMo Code Bench, and Terminal Bench.

Long-Context Research

The 1M-token context makes the model suitable for large research projects involving lengthy papers, technical documentation, datasets, notes, and accumulated research history.

Multimodal Analysis

Applications can combine text, images, video, and audio within a unified workflow. This is useful for analyzing multimedia documents, recorded material, visual evidence, screenshots, and mixed-format research data.

AI Agents and Tool Automation

Tool calling, structured output, deep thinking, and long context make MiMo-V2.6-Pro suitable for agents that need to plan, call external tools, inspect results, and continue working across multiple steps.

Cybersecurity

Cybersecurity is one of the evaluation areas included in Xiaomi's published model results. The model reports 94.0 on CyberGym, 80.2 on MiMo Cyber Bench, 47.9 on ExploitBench, and 66.3 on SEC Bench Pro.

Computer-Use Workflows

MiMo-V2.6-Pro is evaluated on OSWorld-Verified, where Xiaomi reports a score of 82.0. This makes computer-interaction and UI-oriented automation another relevant application area for the model.

Scientific and Professional Workflows

Xiaomi showcases MiMo-V2.6-Pro in scientific research workflows, including materials research, as well as design, content creation, office work, and other complex professional tasks.

Limitations

MiMo-V2.6-Pro's benchmark results are primarily published by Xiaomi and should be interpreted in the context of each benchmark's methodology rather than as a universal measure of application performance.

The model's 1.02T total-parameter architecture also represents a substantial underlying model scale. The 42B activated-parameter figure describes sparse activation rather than the complete hardware or deployment footprint. Organizations considering self-hosting should therefore evaluate inference-engine support, memory requirements, quantization, throughput, and hardware requirements independently.

For API-based applications, the hosted model can be accessed through Xiaomi's API platform, while the open-weight checkpoint provides a separate path for organizations that want to manage their own inference infrastructure.

How to Access MiMo-V2.6-Pro API via CometAPI

Step 1: Sign Up for CometAPI and Get Your CometAPI API Key

Create or log in to your CometAPI account and open the API management area. Generate an API key from your CometAPI account and prepare it for your application.

Step 2: Select MiMo-V2.6-Pro

Find MiMo-V2.6-Pro in the CometAPI model catalog and confirm the model ID as mimo-v2.6-pro. Set MiMo-V2.6-Pro as the target model in your CometAPI integration. Configure the application according to the text, image, video, audio, reasoning, or tool-calling capabilities required by your workflow.

Step 3: Send Requests

Submit requests through the CometAPI integration and provide the required inputs for your application.

Step 4: Process the Response

Use the returned model output, structured data, reasoning results, or tool-call information within your application's workflow.