MiMo-V2.5 API

Technical Specifications of MiMo-V2.5

The 310B/15B architecture is especially important. MiMo-V2.5 is not a 15B model in the conventional sense; 15B is the number of parameters activated for each token, while the complete MoE contains 310B parameters. The model uses 256 routed experts and activates eight experts per token.

What Is MiMo-V2.5?

MiMo-V2.5 is Xiaomi's next-generation multimodal model built specifically around omni-modal perception and agentic applications.

Unlike a conventional text-only LLM, MiMo-V2.5 natively understands images, videos, audio, and text. Xiaomi positions it for long-context and multimodal agent scenarios where the model needs to perceive information, reason over it, and subsequently use tools or perform actions.

There is also an important current-version consideration for developers: Xiaomi deprecated the older MiMo-V2 models on June 30, 2026, recommending migration to the V2.5 series.

That makes mimo-v2.5 the relevant model ID for new integrations rather than the older mimo-v2-pro, mimo-v2-omni, or mimo-v2-flash.

What Are the Main Features of MiMo-V2.5?

Native Omni-Modal Understanding

The defining feature of MiMo-V2.5 is that multimodality is integrated directly into the model.

It accepts:

• Text
• Images
• Video
• Audio

This allows developers to create applications that reason across multiple information types instead of independently processing each modality and passing the results to a text LLM. Xiaomi describes this as native full-modal perception.

A practical example is a video-analysis agent that receives a long video together with an audio track and a textual question, then identifies events, explains what happened, and produces a structured answer.

1M-Token Context Window

MiMo-V2.5 supports 1 million tokens of context and up to 128K output tokens.

This makes the model particularly interesting for:

• Large document analysis
• Long video understanding
• Repository-level coding
• Long research sessions
• Multi-step agents
• Extended conversations
• Large enterprise knowledge bases

The 1M context is not merely a marketing number. Xiaomi specifically identifies long-video tracking, lengthy document analysis, and extended temporal reasoning as target workloads.

Agentic Tool Use

MiMo-V2.5 supports function calling, web search, structured output, and streaming.

That makes it substantially more useful for agent applications than a model limited to text generation.

A typical workflow can look like:

Perceive → Reason → Search → Call Tool → Analyze Result → Continue

This is particularly useful for research agents, coding assistants, customer-support agents, and multimodal automation.

Sparse MoE Efficiency

MiMo-V2.5 uses a 310B-parameter sparse MoE architecture with only 15B activated parameters per token.

Its backbone contains 48 layers, including 39 sliding-window-attention layers and nine full-attention layers. The hybrid design is intended to make very long context more computationally manageable.

The important distinction for developers is that activated parameter count should not be interpreted as total memory requirements. Self-hosting a 310B-parameter model remains a substantial infrastructure undertaking.

Hybrid Sliding-Window Attention

MiMo-V2.5 combines sliding-window attention with global attention.

Its architecture uses a 128-token SWA window, with 39 SWA layers and nine full-attention layers.

This architectural choice is particularly relevant to the model's 1M-token context capability because maintaining full attention across every layer would make long-context inference significantly more expensive.

Multi-Token Prediction

MiMo-V2.5 includes three MTP layers with approximately 329M parameters. The MTP design is intended to improve inference efficiency through speculative decoding and support more efficient reinforcement-learning training.

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How Does MiMo-V2.5 Perform on Benchmarks?

MiMo-V2.5 has published benchmark results covering coding, terminal agents, research agents, and multimodal agent tasks. The current Hugging Face model card reports the following results:

These numbers need to be interpreted carefully.

The 56.1 SWE-Bench Pro result indicates meaningful software-engineering capability, while the 65.8 Terminal-Bench 2.0 result is particularly relevant for agents that interact with terminals and development environments.

At the same time, the difference between the general Claw-Eval score (62.1) and the multimodal score (23.8) is a useful warning: strong general agent performance does not automatically mean equally strong performance on every multimodal agent task.

For production evaluation, developers should therefore test their own workload rather than relying on a single leaderboard number.

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How Does MiMo-V2.5 Compare With MiMo-V2.5-Pro?

MiMo-V2.5 and MiMo-V2.5-Pro belong to the same V2.5 generation but have different optimization targets.

The distinction is straightforward:

Choose MiMo-V2.5 for native multimodal understanding and efficient general-purpose agents. Choose MiMo-V2.5-Pro for the hardest reasoning, coding, and long-horizon agent workloads.

Xiaomi's own model-selection guide recommends mimo-v2.5 specifically for understanding image, audio, and video content, while recommending mimo-v2.5-pro for complex reasoning and long-document processing.

Use Cases for MiMo-V2.5

Multimodal AI Agents

MiMo-V2.5 can serve as the central model for agents that need to inspect documents, images, videos, and audio before deciding what action to take.

Long-Context Document Analysis

The 1M-token context makes it suitable for analyzing:

• Large legal document collections
• Technical documentation
• Research archives
• Large codebases
• Enterprise knowledge bases

Coding Agents

The model's agent benchmarks and tool-use capabilities make it suitable for coding assistants that need to inspect repositories, reason about bugs, execute tools, and iterate over solutions.

Video Understanding

Instead of treating video generation as its primary purpose, MiMo-V2.5 uses video as an input modality for understanding.

Potential applications include:

• Video summarization
• Long-video question answering
• Video search
• Event detection
• Educational video analysis
• Security footage analysis

Audio-Visual Assistants

Because the model accepts both audio and visual information, developers can build assistants capable of interpreting spoken instructions together with visual context.

Long-Running Autonomous Agents

The combination of long context and agentic training makes MiMo-V2.5 suitable for workflows where the model must maintain state over many intermediate steps rather than completing a task in a single response.

Limitations of MiMo-V2.5

MiMo-V2.5's specifications are impressive, but several practical limitations deserve attention.

First, 1M context does not mean every application will achieve optimal performance at the maximum window. Long-context inference still involves significant memory, bandwidth, and latency considerations.

Second, the model is a very large MoE system. Although only 15B parameters are activated per token, the complete model contains approximately 310B parameters, making self-hosting considerably more demanding than running a small dense model.

Third, multimodal benchmark performance can vary significantly by task. The Claw-Eval results show a much lower multimodal score than the general agent score, reinforcing the need for application-specific testing.

Finally, the model ecosystem is still newer than the mature ecosystems surrounding GPT, Claude, and Gemini. Developers should therefore evaluate tooling, provider compatibility, structured output behavior, and tool-calling reliability before using it in mission-critical production systems.

How to Use MiMo-V2.5 API on CometAPI

MiMo-V2.5 is particularly suitable for an API aggregation platform because it follows API patterns compatible with established LLM ecosystems. Xiaomi itself provides OpenAI- and Anthropic-compatible API access.

With CometAPI, the integration can follow the same basic workflow used for other supported models.

Step 1: Get a CometAPI API Key

Create or log into your CometAPI account and obtain your API key.

import os

COMETAPI_KEY = os.environ["COMETAPI_KEY"]

Step 2: Configure the MiMo-V2.5 Model

Set the model to:

mimo-v2.5

and use CometAPI's compatible API endpoint.

The exact endpoint and request schema should be checked against the current CometAPI model/API documentation before deployment.

Step 3: Build Multimodal and Agent Workflows

The more interesting use of mimo-v2.5 is not ordinary text chat. Developers can combine its multimodal reasoning with external tools to create workflows such as:

User input → image/video/audio understanding → reasoning → tool call → result analysis → next action

This makes the model particularly attractive for autonomous research agents, coding agents, multimodal customer-support systems, and long-context enterprise assistants.

Why Use MiMo-V2.5 Through CometAPI?

MiMo-V2.5 is especially useful in a multi-model API environment because developers may want to compare it against GPT, Claude, Gemini, DeepSeek, or other agent models without building a separate infrastructure stack for every provider.

CometAPI can be useful when your application needs:

• A unified API layer
• Access to multiple AI model families
• Easier model switching
• Centralized API-key management
• Rapid model comparison
• A single integration layer for experimentation and production

For teams evaluating agent performance versus inference cost, MiMo-V2.5 is worth testing alongside the more expensive flagship models rather than assuming that the largest proprietary model will automatically be the best choice.