MiMo V2.6 Flash API

What Is MiMo-V2.6-Flash?

MiMo-V2.6-Flash is Xiaomi's efficiency-focused model in the MiMo-V2.6 family. It is a native multimodal reasoning model designed for high-frequency calls, large-scale workloads, coding, automation, and long-horizon agent tasks.

The model accepts text, images, video, and audio as input and produces text output. Xiaomi describes it as a full-modality model optimized for real-world workflows, with a 1-million-token context window, deep thinking, tool calling, structured output, streaming, web search, and context caching capabilities.

The open-weight MiMo-V2.6-Flash-RL checkpoint uses a sparse Mixture-of-Experts architecture with 309 billion total parameters and 15 billion activated parameters. Its model card describes a 1M-token context length, a 681M-parameter vision encoder, audio encoders, and a five-layer speculative decoder.

Technical Specifications

The official Xiaomi model page lists a 1M-token context window, up to 128K output tokens, 100 RPM, and 10M TPM.

Main Features of MiMo-V2.6-Flash

1. Native Full-Modality Understanding

MiMo-V2.6-Flash is designed to process text, images, video, and audio within the same model. This makes it suitable for applications where information is distributed across multiple media formats rather than contained entirely in text.

Typical applications include multimodal document analysis, visual inspection, video understanding, audio-informed research, and workflows that combine different types of evidence.

2. 1M-Token Long Context

The model supports a context window of up to 1 million tokens, making it suitable for large repositories, lengthy documents, extended tool traces, and multi-session agent workflows. Xiaomi specifically positions the model for long-horizon tasks and multi-agent collaboration.

This large context capacity can reduce the need to repeatedly summarize or split large bodies of information before processing.

3. Reasoning and Agentic Workflows

MiMo-V2.6-Flash is built around reasoning-oriented and agentic workloads. Its official capability list includes deep thinking and tool calling, while the model card describes mixed reinforcement learning across coding, general agents, visual tasks, and cybersecurity.

This makes the model relevant to applications such as:

• Multi-step research agents
• Coding agents
• Tool orchestration
• Computer-use workflows
• Automated analysis
• Long-running planning tasks

4. Tool Calling and Structured Output

The model supports tool calling and structured output, allowing it to participate in applications where the model needs to invoke external functions or return information in a predictable machine-readable format.

This is particularly useful for agent systems that connect an LLM to search, databases, APIs, business tools, or other application functions.

5. Multimodal Agent Capabilities

MiMo-V2.6-Flash combines multimodal understanding with agent-oriented reasoning. Instead of treating image, video, audio, and text processing as separate model tasks, it provides a unified model interface for these inputs.

This architecture is useful for workflows such as reviewing a technical document together with diagrams, analyzing recorded meetings, inspecting video evidence, or combining screenshots with textual instructions.

6. Efficiency-Oriented MiMo-V2.6 Design

The Flash variant is positioned by Xiaomi as the efficiency-balanced member of the MiMo-V2.6 family. Its sparse MoE design activates a subset of parameters for each token rather than using the entire parameter pool on every token.

The model also uses a five-layer speculative decoder that predicts multiple subsequent tokens for parallel verification, an architectural approach intended to improve inference efficiency.

Benchmark Performance

Xiaomi's published evaluation table reports MiMo-V2.6-Flash results across coding, general-agent, cybersecurity, and visual-agent benchmarks. These are provider-published benchmark results, so they should not be treated as independent third-party rankings.

For example, Xiaomi reports 87.6 on Terminal Bench 2.1, 80.8 on OSWorld-Verified, 73.6 on Toolathlon-Verified, and 95.1 on CyberGym.

The benchmark profile suggests that the model is particularly designed around agentic, coding, tool-use, and multimodal workflows rather than being positioned solely as a conventional conversational model.

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

MiMo-V2.6-Flash and MiMo-V2.6-Pro belong to the same model family and share the native multimodal and 1M-context design, but they target different points in the model-efficiency spectrum.

The published architecture shows the Pro model at 1.02T total / 42B activated parameters, compared with Flash at 309B total / 15B activated parameters.

MiMo-V2.6-Flash vs Other Models

MiMo-V2.6-Flash occupies a distinctive position among modern multimodal reasoning models because it combines four characteristics in one model: long context, native multimodal input, reasoning, and agent-oriented tool use.

Compared with a conventional text-only reasoning model, MiMo-V2.6-Flash can directly incorporate images, video, and audio. Compared with a smaller multimodal model, its 1M-token context makes it more suitable for large repositories and extended agent trajectories. Compared with the larger MiMo-V2.6-Pro, Flash emphasizes a more efficiency-oriented architecture.

Representative Use Cases

Coding and Software Engineering

MiMo-V2.6-Flash can be used for code generation, debugging, repository analysis, software planning, and agentic development workflows.

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

Long-Context Document Analysis

The 1M-token context window makes the model suitable for analyzing large collections of documents, technical specifications, research materials, or lengthy project histories without aggressively reducing the available context.

Multimodal Research

Because the model accepts text, image, video, and audio, it can combine information from different media sources in a single reasoning workflow.

Agent and Tool Automation

Tool calling, structured output, reasoning, and long context make the model suitable for agents that need to plan tasks, invoke external tools, inspect results, and continue reasoning over multiple steps.

Computer-Use Workflows

The published OSWorld-Verified result of 80.8 and other agent benchmarks demonstrate that Xiaomi evaluates the model on interactive agent tasks in addition to traditional language-model benchmarks.

Multimodal Business Workflows

MiMo-V2.6-Flash can support workflows involving reports, screenshots, recordings, presentations, documents, and other mixed-media business information where a single model needs to synthesize multiple input types.

Limitations

MiMo-V2.6-Flash is a newly released model, so benchmark coverage and independent third-party evaluation are still developing. Xiaomi's published benchmark table provides substantial evidence across agentic and multimodal tasks, but these results are provider-reported rather than a substitute for application-specific testing.

The model also has a large underlying architecture despite sparse activation. Organizations considering self-hosting should therefore evaluate hardware, inference-engine compatibility, quantization, throughput, and operational requirements rather than assuming that the 15B activated-parameter figure represents the total deployment footprint. The official model card provides deployment guidance for SGLang and vLLM.

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

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

Create or access your CometAPI account and open the API management area. Generate an API key from your CometAPI account and keep it available for your application configuration.

Step 2: Select MiMo-V2.6-Flash and Configure Your Application

Find MiMo-V2.6-Flash in the available model catalog and confirm the model ID as mimo-v2.6-flash. Set MiMo-V2.6-Flash as the target model in your CometAPI integration. Configure the request according to the multimodal inputs and agent capabilities required by your application.

Step 3: Send Requests

Submit text or supported multimodal inputs through the CometAPI integration and use the returned model response within your application workflow.

Step 4: Process the Response

Handle the generated text, structured output, reasoning results, or tool-call information according to your application's workflow.