MiMo-V2.5 API

xiaomi/mimo-v2.5
MiMo-V2.5 is Xiaomi's native omnimodal AI, trained from scratch to understand text, images, audio, and video together.
Context
1M tokens
Input
$0.14812 / 1M tokens
Output
$0.29624 / 1M tokens
Released
Apr 23, 2026

Overview

What is MiMo-V2.5 API

Released on April 22, 2026 by Xiaomi's AI team, MiMo-V2.5 picks up where MiMo-V2-Omni left off, with substantially better agentic performance, sharper visual reasoning, and native support for a 1 million token context window. It's designed for production — not benchmarks alone.

The model sits at the base of the V2.5 family. For teams that need the absolute ceiling on agentic tasks and software engineering, there's MiMo-V2.5-Pro. For general-purpose multimodal work at an honest price, MiMo-V2.5 is the practical starting point.

MiMo-V2.5 API Pricing:

  • Input: $0.40 / 1M tokens
  • Output: $2.00 / 1M tokens

What sets it apart

Native omnimodal architecture

Not a text model with adapters — all four modalities trained jointly from day one, so cross-modal reasoning is genuinely coherent.

Surpasses MiMo-V2-Pro on agentic tasks

The newer multimodal model beats the previous text-only flagship on general agentic benchmarks — at half the token cost.

1 million token context

No more chunking. Process entire research papers, long video transcripts, or sprawling codebases without losing coherence.

Agent-framework ready

Works natively with Claude Code, OpenCode, Kilo, and other popular scaffolds. Drop it in without rewiring your stack.

Stepped-up token efficiency

‍Strong multimodal results with significantly fewer tokens than closed-source competitors, which keeps production costs predictable.

Architecture overview

📝 Text
🖼️ Image
🎧 Audio
🎬 Video
↓
⚙️ Visual encoder + Audio encoder
↓
🧠 LLM backbone — joint alignment
↓
🔧 Agentic post-training (tool use, planning)
↓
✅ Output: text, code, structured data, tool calls

What people are actually building with it

A 1M-token context window and native four-modality input opens up workflows that simply weren't practical before. These are some of the patterns that map most directly to real production use.

Document intelligence

Feed in entire PDFs — financials, legal contracts, research papers — alongside embedded charts and tables, and get structured extractions or cross-document analysis in a single call. The 1M context means you don't have to split documents into chunks and reassemble the answers.

Video analysis and summarisation

Long-form video is finally tractable. Scene-level summarisation, entity tracking across a full-length interview, automated highlight reels from sports footage, meeting transcripts with action items — the temporal reasoning on Video-MME (87.7) translates directly into coherent long-video understanding.

Product catalog enrichment

Drop in product photography and get SEO-ready descriptions, attribute tags, and category suggestions at 1x token pricing. The cost delta versus premium closed-source models makes it viable to run on entire catalogs — not just flagship SKUs.

Agentic workflows

MiMo-V2.5 is designed to sit inside agent loops — not just answer questions at the end of one. It integrates with Claude Code, OpenCode, and Kilo, handles tool calls reliably, and keeps a coherent plan across long multi-step tasks without derailing into hallucination.

Visual reasoning and chart interpretation

Scientific figures, financial charts, dense dashboards, handwritten equations — the 81.0 on CharXiv RQ reflects genuine precision. Useful anywhere that visual data needs to be understood rather than just described.

Common questions

How is "native omnimodal" different from regular multimodal?

Most multimodal models process each modality through separate pipelines and combine the results. MiMo-V2.5 was trained across all four modalities simultaneously, which means it can reason about, say, the relationship between audio content and on-screen visuals in a video — something late-fusion models struggle with.

When should I choose MiMo-V2.5 over MiMo-V2.5-Pro?

For most production workloads — multimodal understanding, agentic tasks, everyday coding, document processing — MiMo-V2.5 delivers comparable output at half the cost. Upgrade to Pro when you need top-tier performance on long-horizon software engineering or the absolute ceiling on SWE-bench-class tasks.

Can I use MiMo-V2.5 inside my existing agent framework?

Yes. MiMo-V2.5 is compatible with Claude Code, OpenCode, and Kilo out of the box. It's also available via OpenRouter, making it straightforward to route into any stack that supports that interface.

What kinds of video content can it handle?

MiMo-V2.5 handles long-form video — scene tracking, temporal reasoning, visual grounding over minutes of footage. That covers meeting recordings, product demos, educational content, sports footage, and documentary-style material. The 87.7 Video-MME score reflects genuine comprehension, not just caption generation.

Uptime and performance

  • Uptime (24h): 100%
  • Uptime (7d): 100%
  • Availability (24h): 100%
  • Error rate (24h): 0%
  • End-to-end latency (24h median): 11.6s
  • Throughput (24h median): 4.5 tok/s

Measured on the AI/ML API gateway across live traffic. Figures are medians unless stated. Last updated 2026-10-12.

Use cases

MiMo-V2.5 is documented as suited to visual reasoning, including precise perception and complex chart analysis. It is also described as suited to deep multimodal understanding across text, image, video, and audio.

It is documented for general agent scenarios, with native agent execution across browsing, comprehension, reasoning, and operation. Xiaomi also describes it as supporting long-range tasks such as long video tracking, lengthy document analysis, and extended temporal reasoning.

It is additionally presented as useful for everyday task performance in agent workflows. The model is also described as a native full-modal model for understanding and translating understanding into action.

How to use MiMo-V2.5 API

Install any OpenAI-compatible SDK, point it at api.aimlapi.com/v1, and set the model to xiaomi/mimo-v2.5.
import requests

r = requests.post(
    "https://api.aimlapi.com/v1/chat/completions",
    headers={"Authorization": "Bearer " + AIMLAPI_KEY},
    json={
      "model": "xiaomi/mimo-v2.5",
      "messages": [
        {
          "role": "user",
          "content": "Hello!"
        }
      ]
    },
)
print(r.json())
const r = await fetch("https://api.aimlapi.com/v1/chat/completions", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    "model": "xiaomi/mimo-v2.5",
    "messages": [
      {
        "role": "user",
        "content": "Hello!"
      }
    ]
  }),
});
console.log(await r.json());
curl -X POST https://api.aimlapi.com/v1/chat/completions \
  -H "Authorization: Bearer $AIMLAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"xiaomi/mimo-v2.5","messages":[{"role":"user","content":"Hello!"}]}'

OpenAI-compatible — swap the base URL and it works with your existing SDK.

MiMo-V2.5 API Pricing

TypePrice
Input
$0.14812 / 1M tokens
Output
$0.29624 / 1M tokens
Cached input
$0.0029624 / 1M tokens

MiMo-V2.5 Benchmarks

BenchmarkScoreWhat it measuresSourceRetrieved
Intelligence
22.3
Composite score across standardised reasoning, knowledge and problem-solving evaluations, measured independently by Artificial AnalysisSourceSeptember 12, 2026
Coding
56.8
Composite score across standardised coding evaluations, measured independently by Artificial AnalysisSourceSeptember 12, 2026

MiMo-V2.5 vs other models

ModelInputOutputContextBest for
MiMo-V2.5
This page
$0.14812 / 1M tokens
$0.29624 / 1M tokens
1M tokens
Reasoning + agents
$5 / 1M tokens
$30 / 1M tokens
1M tokens
Reasoning + agents
$2 / 1M tokens
$10 / 1M tokens
1M tokens
Balanced coding + agents
$3 / 1M tokens
$15 / 1M tokens
1M tokens
Long-context, multimodal & agentic workflows
$1.5 / 1M tokens
$9 / 1M tokens
1M tokens
Reasoning + agents
$0.14812 / 1M tokens
$0.29624 / 1M tokens
1M tokens
Agentic workflows and structured output
$0.46023 / 1M tokens
$0.92046 / 1M tokens
1M tokens
Agentic workflows and structured output
$4.6023 / 1M tokens
$9.2046 / 1M tokens
1M tokens
Agentic workflows and structured output

Frequently asked questions

MiMo-V2.5 has a 1,000,000 tokens context window and can return up to 128,000 tokens.

MiMo-V2.5 takes image, text as input and returns text.

Use xiaomi/mimo-v2.5 as the model id. Requests go to https://api.aimlapi.com/v1/chat/completions.

MiMo-V2.5 became available on April 23, 2026.

MiMo-V2.5 is priced at input $0.192556 / 1M tokens, output $0.385112 / 1M tokens, cached input $0.0038511 / 1M tokens.

Yes, MiMo-V2.5 can stream responses as they are generated.

Yes, MiMo-V2.5 accepts image input alongside text.

MiMo-V2.5 was built by Xiaomi.

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