Seedream 5.0 Pro è LIVE | Prova nel Generatore di immagini →
X
xiaomi/mimo-v2.5-pro

xiaomi/mimo-v2.5-pro

Data di rilascio: 2026-04-23

1,048,576 context · $1.00/M input tokens · $3.00/M output tokens

MiMo-V2.5-Pro is Xiaomi’s flagship open model for advanced agentic workflows, complex software engineering, and long-horizon task execution. Built on a sparse Mixture-of-Experts architecture with 1.02T total parameters and 42B active parameters, it supports a 1M-token context window and is optimized for autonomous coding agents, large codebase reasoning, tool-use workflows, and multi-step problem solving. It delivers strong performance on agentic and software engineering benchmarks such as ClawEval, GDPVal, and SWE-bench Pro, with an emphasis on token-efficient long-context execution.

Prezzi

Pay-per-use

Nessun costo iniziale, paga solo per ciò che usi

Input
256K $1.00 / M Tokens
> 256K $2.00 / M Tokens
Output
256K $3.00 / M Tokens
> 256K $6.00 / M Tokens
Cache Read
256K $0.20 / M Tokens
> 256K $0.40 / M Tokens

Prova il modello

xiaomi/mimo-v2.5-pro
Online
X
Ciao! Sono un assistente IA utile. Come posso aiutarti?
Pronto a usare questo modello in un coding agent locale?Setup agent

Utilizzo API

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

import OpenAI from 'openai';

if (!process.env.WAVESPEED_API_KEY) throw new Error('Set WAVESPEED_API_KEY');
const client = new OpenAI({
  apiKey: process.env.WAVESPEED_API_KEY,
  baseURL: 'https://llm.wavespeed.ai/v1',
  timeout: 120_000,
  maxRetries: 2,
});

try {
  const response = await client.chat.completions.create({
    model: 'xiaomi/mimo-v2.5-pro',
    messages: [{ role: 'user', content: 'Hello!' }],
  });
  console.log(response.choices[0]?.message?.content ?? '');
} catch (error) {
  console.error('LLM request failed:', error);
  process.exitCode = 1;
}

Introduzione al modello

Xiaomi: MiMo-V2.5-Pro

MiMo-V2.5-Pro is Xiaomi’s flagship open model for advanced agentic workflows, complex software engineering, and long-horizon task execution. Built on a sparse Mixture-of-Experts architecture with 1.02T total parameters and 42B active parameters, it is optimized for autonomous coding agents, large codebase reasoning, tool use, and multi-step problem solving.


Why It Looks Great

  • Flagship Xiaomi MiMo model for complex agentic and software engineering workloads
  • Sparse Mixture-of-Experts architecture with 1.02T total parameters and 42B active parameters
  • 1M-token context window for long prompts, large codebases, documents, and multi-turn workflows
  • Strong fit for autonomous coding agents, long-horizon task execution, and tool-heavy workflows
  • Competitive performance on benchmarks such as ClawEval, GDPVal, and SWE-bench Pro
  • Designed for token-efficient agent trajectories and extended multi-step execution
  • Function calling and tool-use support for agentic application workflows
  • Structured output support for JSON responses and schema-constrained generation
  • Reasoning controls for tuning latency, quality, and cost per request

Key Features

  • Architecture: Sparse Mixture-of-Experts
  • Total Parameters: 1.02T
  • Active Parameters: 42B
  • Context Window: 1,048,576 tokens
  • Max Input: 1,032,192 tokens
  • Max Output: 16,384 tokens
  • Input: Text
  • Output: Text
  • Vision: Not listed
  • Function Calling: Supported
  • Structured Outputs: Supported
  • Thinking Mode: Supported
  • Image Generation: Not listed
  • Audio Input: Not listed
  • Supported Parameters: frequency_penalty, include_reasoning, logit_bias, max_tokens, min_p, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p

Specifications

SpecificationValue
Providerxiaomi
Model TypeChat Completions model
ArchitectureSparse Mixture-of-Experts
Parameters1.02T total / 42B active
Context Window1,048,576 tokens
Max Input1,032,192 tokens
Max Output16,384 tokens
InputText
OutputText
VisionNot listed
Function CallingSupported
Structured OutputsSupported
Primary Use CasesAgentic coding, complex software engineering, long-horizon tasks, tool use

Pricing

Token TypeCost
Input$1.00 per million tokens
Output$3.00 per million tokens
Cached Input$0.20 per million tokens

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-pro


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-pro",
    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-pro",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Notes

  • Model: xiaomi/mimo-v2.5-pro
  • Provider: xiaomi
  • Best suited for autonomous coding, complex software engineering, long-horizon reasoning, tool-heavy agent workflows, and large-context text tasks

Info

Providerxiaomi
Tipollm

Funzionalità supportate

Input
Testo
Output
Testo
Contesto1,048,576
Output massimo16,384
Vision-
Function Calling✓ Supportato

Guida all'accesso API

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

Mimo V2.5 Pro API

xiaomi/mimo-v2.5-pro

MiMo-V2.5-Pro is Xiaomi’s flagship open model for advanced agentic workflows, complex software engineering, and long-horizon task execution. Built on a sparse Mixture-of-Experts architecture with 1.02T total parameters and 42B active parameters, it supports a 1M-token context window and is optimized for autonomous coding agents, large codebase reasoning, tool-use workflows, and multi-step problem solving. It delivers strong performance on agentic and software engineering benchmarks such as ClawEval, GDPVal, and SWE-bench Pro, with an emphasis on token-efficient long-context execution.

Input

$1 /M

Output

$3 /M

Contesto

1049K

Output max

16K

Uso strumenti

Supportato

Prova Mimo V2.5 Pro su WaveSpeedAI

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

Domande frequenti su Mimo V2.5 Pro

Quanto costa Mimo V2.5 Pro via API?+

Prezzi su WaveSpeedAI: $1.00 per milione di token in input e $3.00 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 Pro?+

Mimo V2.5 Pro supporta fino a 1049K token di contesto e fino a 16K token di output per richiesta.

Mimo V2.5 Pro è compatibile con OpenAI?+

WaveSpeedAI espone Mimo V2.5 Pro su https://llm.wavespeed.ai/v1 tramite l’interfaccia Chat Completions compatibile con OpenAI. Per la maggior parte dei client OpenAI SDK basta cambiare base URL e API key; i campi opzionali dipendono dal modello.

Come si inizia con Mimo V2.5 Pro?+

Accedi a WaveSpeedAI, crea una API key in Access Keys e invia una richiesta a https://llm.wavespeed.ai/v1/chat/completions con il model id mostrato sopra. Consulta il catalogo attuale per disponibilità, funzionalità e prezzi.

API LLM correlate

MiMo-V2.5-Pro | Xiaomi LLM API Pricing & Performance | WaveSpeedAI