MiMo-V2.5: Complete Specifications, Pricing, API Access & Use Cases (2026)

MiMo-V2.5 is Xiaomi’s multimodal, agent-oriented model for coding, long-context analysis and workflows combining text with visual or audio information. Released on April 22, 2026, it uses a sparse Mixture-of-Experts architecture and supports up to one million context tokens. This guide examines the model’s documented capabilities, Gate.AI-listed pricing, supported modalities, operational trade-offs, practical workloads, and API access as verified in July 2026.

What is MiMo-V2.5?

MiMo-V2.5 is a 310-billion-parameter sparse Mixture-of-Experts model developed by Xiaomi. Approximately 15 billion parameters are active during inference, allowing the model to use a large overall architecture without activating every parameter for every token.

Xiaomi presents the model as a unified system for agent execution, coding and multimodal reasoning. Its language backbone is supplemented by dedicated visual and audio encoders, enabling it to process text, images, audio and video rather than operating as a text-only language model. Xiaomi also states that the instruction-tuned version supports a context window of up to one million tokens.

MiMo-V2.5 differs from MiMo-V2.5 Pro in positioning and scale. The standard version has 310 billion total parameters and 15 billion active parameters, while the Pro model has 1.02 trillion total parameters and 42 billion active parameters. The standard version therefore targets a more cost-efficient balance of multimodal capability and agent performance.

What Are MiMo-V2.5’s Key Specifications and Pricing?

Xiaomi confirms the architecture, parameter count, training-token figure, context window and multimodal design. The model ID and token prices are presented separately as per the Gate.AI model-card and should not be interpreted as Xiaomi-direct API pricing.

For example, a request using two million uncached input tokens and 500,000 output tokens would have an estimated listed cost of:

(2 × $0.14) + (0.5 × $0.28) = $0.42

This calculation excludes cache discounts, retries, account-specific terms and other possible charges.

What Can MiMo-V2.5 Do That Makes It Useful in Production?

MiMo-V2.5’s production value comes from combining long-context processing, coding ability and multimodal understanding within one model.

A software agent could examine source files, technical documentation, interface screenshots and error logs before proposing a fix. This reduces the need to send visual information to one model and code to another. Xiaomi also positions MiMo-V2.5 for agentic tasks involving multiple steps, tool use and extended working context.

Its one-million-token context window can accommodate large repositories, lengthy documents or extensive interaction histories. However, submitting the maximum context for every request may increase cost and processing time. Retrieval, file selection and context compression remain useful even when the model supports a large theoretical limit.

The model can also interpret recorded demonstrations or support sessions. For example, it could connect spoken feedback with visible interface actions and convert the recording into a structured bug report. These workflows rely on multimodal understanding; MiMo-V2.5 is not documented as a native image, audio or video generation model.

What Are MiMo-V2.5’s Supported Modalities?

Xiaomi explicitly documents native visual and audio understanding and discusses performance across image, video and multimodal-agent tasks. The available sources support text generation as the primary output but do not confirm native media generation through this model.

Where Does MiMo-V2.5 Fall Short?

Several operational details are not publicly confirmed for the exact Gate.AI route, including maximum output length, rate limits, fine-tuning support, media-upload requirements and model-specific structured-output behavior.

The large context window can also encourage inefficient prompting. Providing an entire repository may cost more and produce less focused results than retrieving only relevant files.

Multimodal requests may contain faces, conversations, customer records, source code or credentials. Teams should apply redaction, access controls and retention policies before sending sensitive information.

Agent-generated code and tool actions require additional safeguards. Outputs should pass automated tests, dependency checks and human review, while tool permissions should follow least-privilege principles.

What Is MiMo-V2.5 Best Used For?

MiMo-V2.5 is well suited to:

• coding assistants that combine source files with screenshots or logs;
• long-document and repository analysis;
• multimodal debugging and interface review;
• recorded-workflow or product-demonstration analysis;
• cost-sensitive agents that require broad input support.

Choose MiMo-V2.5 when a workflow benefits from multiple input modalities, long context and lower listed token pricing. Consider another model when the task requires confirmed native media generation, highly specialized frontier-level coding, strict structured-output guarantees or fully documented enterprise controls.

How Does MiMo-V2.5 Compare to MiMo-V2.5 Pro and Gemini 2.5 Flash?

MiMo-V2.5 is the practical Xiaomi option when teams prioritize token efficiency and broad multimodal understanding. MiMo-V2.5 Pro is positioned for more demanding software-engineering and long-horizon tasks.

Gemini 2.5 Flash may suit teams already using Google’s model ecosystem. Selection should depend on verified API features, workload quality, latency and total session cost rather than context size alone.

How Do I Access MiMo-V2.5 Through Gate.AI?

Gate.AI documents an OpenAI-compatible API with the base URL https://api.gate.ai/openai/v1 . As per the Gate.AI listing, the model identifier is xiaomi/mimo-v2.5 .

Python

import os
from openai import OpenAI
client = OpenAI(
    api_key=os.environ["GATEAI_API_KEY"],
    base_url="https://api.gate.ai/openai/v1",
)
try:
    response = client.chat.completions.create(
        model="xiaomi/mimo-v2.5",
        messages=[
            {
                "role": "user",
                "content": "Review this Python service and identify reliability risks."
            }
        ],
    )
    print(response.choices[0].message.content)
except Exception as error:
    print(f"Request failed: {error}")

curl

curl "https://api.gate.ai/openai/v1/chat/completions" \
  -H "Authorization: Bearer $GATEAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "xiaomi/mimo-v2.5",
    "messages": [
      {
        "role": "user",
        "content": "Create a migration plan for this Python service."
      }
    ]
  }'

These examples use Gate.AI’s documented general chat-completions route. Confirm account availability and the current media-input schema before implementing image, audio or video requests.

FAQs

Does MiMo-V2.5 support one million tokens?

Yes. Xiaomi documents a context window of up to one million tokens for the instruction-tuned MiMo-V2.5 model.

Can MiMo-V2.5 process video and audio?

Yes. Xiaomi documents native visual and audio understanding and evaluates the model on video and multimodal tasks.

How much does MiMo-V2.5 cost through Gate.AI?

As per the supplied Gate.AI listing, input costs $0.14 per million tokens, output costs $0.28 per million and cache reads cost $0.0428 per million.

Is MiMo-V2.5 suitable for coding agents?

Yes. It is designed for coding and agentic workflows, but generated changes should still undergo testing, security review and human approval.

Is MiMo-V2.5 cheaper than MiMo-V2.5 Pro?

The Gate.AI listings position the standard model as the lower-cost option. MiMo-V2.5 Pro may be preferable when higher-end software engineering and longer autonomous workflows justify additional cost.