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xiaomi/mimo-v2.5

xiaomi/mimo-v2.5

1,048,576 context · $0.40/M input tokens · $2.00/M output tokens

MiMo-V2.5 is a native omnimodal model by Xiaomi. It delivers Pro-level agentic performance at roughly half the inference cost, while surpassing MiMo-V2-Omni in multimodal perception across image and video understanding tasks. Its 1M context window supports complete documents, extended conversations, and complex task contexts in a single pass, making it ideal for integration with agent frameworks where strong reasoning, rich perception, and cost efficiency all matter.

Prezzi

Pay-per-use

Nessun costo iniziale, paga solo per ciò che usi

Input
256K $0.40 / M Tokens
> 256K $0.80 / M Tokens
Output
256K $2.00 / M Tokens
> 256K $4.00 / M Tokens
Cache Read
256K $0.08 / M Tokens
> 256K $0.16 / M Tokens

Prova il modello

xiaomi/mimo-v2.5
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Utilizzo API

Usa i seguenti esempi di codice per integrare la nostra API:

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://llm.wavespeed.ai/v1"
)

response = client.chat.completions.create(
    model="xiaomi/mimo-v2.5",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(response.choices[0].message.content)

Introduzione al modello

Xiaomi: MiMo-V2.5

MiMo-V2.5 is a native omnimodal model by Xiaomi. It delivers Pro-level agentic performance at roughly half the inference cost, while surpassing MiMo-V2-Omni in multimodal perception across image and video understanding...

This model is imported from OpenRouter metadata and exposed through the WaveSpeed AI OpenAI-compatible API for chat completions and compatible application workflows.


Why It Looks Great

  • text+image+audio+video->text architecture for Text, Audio, Image, Video to Text workloads
  • 1048576 context window for long prompts, document analysis, and multi-turn workflows
  • Competitive pricing at $0.4/$2 per million tokens
  • Vision input support for image understanding and multimodal tasks
  • Function calling and tool-use support for agentic application workflows
  • Structured output support for JSON responses and schema-constrained generation
  • Reasoning controls available through supported OpenRouter parameters

Key Features

  • Context Window: 1048576 tokens
  • Max Input: 917504 tokens
  • Max Output: 131072 tokens
  • Vision: Supported
  • Function Calling: Supported
  • Structured Outputs: Supported
  • Image Generation: Not listed
  • Audio Input: Supported
  • Supported Parameters: frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, response_format, stop, temperature, tool_choice, tools, top_p

Specifications

SpecificationValue
Providerxiaomi
Model TypeChat Completions model
Architecturetext+image+audio+video->text
Context Window1048576 tokens
Max Input917504 tokens
Max Output131072 tokens
InputText, Audio, Image, Video
OutputText
VisionSupported
Function CallingSupported
Structured OutputsSupported
OpenRouter CreatedApril 22, 2026

Pricing

Token TypeCost
Input$0.4 per million tokens
Output$2 per million tokens
Cached Input$0.08 per million tokens

Note: Pricing is generated from OpenRouter model metadata. If multiple upstream providers expose different endpoint prices, review and adjust the price before publishing.


How to Use

  1. Write your prompt - describe the task, provide context, and specify the desired output format.
  2. Submit - the model processes your request and returns the response.

API Integration

Base URL: https://llm.wavespeed.ai/v1 API Endpoint: chat/completions Model ID: xiaomi/mimo-v2.5


API Usage

Python SDK

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://llm.wavespeed.ai/v1"
)

response = client.chat.completions.create(
    model="xiaomi/mimo-v2.5",
    messages=[{"role": "user", "content": "Hello!"}]
)

print(response.choices[0].message.content)

cURL

curl https://llm.wavespeed.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "xiaomi/mimo-v2.5",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Info

Providerxiaomi
Tipollm

Funzionalità supportate

Input
TestoImmagineAudio
Output
Testo
Contesto1,048,576
Output massimo131,072
Vision✓ Supportato
Function Calling✓ Supportato

Guida all'accesso API

Base URLhttps://llm.wavespeed.ai/v1
API Endpointchat/completions
ID modelloxiaomi/mimo-v2.5

Mimo V2.5 API

xiaomi/mimo-v2.5

MiMo-V2.5 is a native omnimodal model by Xiaomi. It delivers Pro-level agentic performance at roughly half the inference cost, while surpassing MiMo-V2-Omni in multimodal perception across image and video understanding tasks. Its 1M context window supports complete documents, extended conversations, and complex task contexts in a single pass, making it ideal for integration with agent frameworks where strong reasoning, rich perception, and cost efficiency all matter.

Input

$0.4 /M

Output

$2 /M

Contesto

1049K

Output max

131K

Vision

Supportato

Uso strumenti

Supportato

Prova Mimo V2.5 su WaveSpeedAI

Accedi a Mimo V2.5 tramite la nostra API unificata — compatibile con OpenAI, senza cold start, prezzi trasparenti.

Domande frequenti su Mimo V2.5

Quanto costa Mimo V2.5 via API?+

Prezzi su WaveSpeedAI: $0.40 per milione di token in input e $2.00 per milione di token in output. Prompt caching e batch processing sono fatturati separatamente e riducono il costo effettivo su carichi lunghi e ripetitivi.

Qual è la context window di Mimo V2.5?+

Mimo V2.5 supporta fino a 1049K token di contesto e fino a 131K token di output per richiesta.

Mimo V2.5 è compatibile con OpenAI?+

Sì. WaveSpeedAI espone Mimo V2.5 tramite un endpoint compatibile con OpenAI all'indirizzo https://llm.wavespeed.ai/v1. Punta l'SDK ufficiale di OpenAI a questa base URL con la tua API key WaveSpeedAI — senza altre modifiche al codice.

Come si inizia con Mimo V2.5?+

Accedi a WaveSpeedAI, crea una API key in Access Keys, poi invia una richiesta a https://llm.wavespeed.ai/v1/chat/completions con il model id mostrato sopra. I nuovi account ricevono crediti gratuiti per testare Mimo V2.5.

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