Chat Completions API

The chat completions endpoint mirrors OpenAI’s v1/chat/completions contract. Use it to stream or fetch MiniMax-M2 responses with minimal code changes.

Endpoint: POST https://minimax-m2.com/api/v1/chat/completions
Auth: Authorization: Bearer <api-key>
Models: MiniMax-M2.1 (default, multi-language & native apps), MiniMax-M2 (general coding & agents) → See model comparison to choose the right one for your workflow

💡 Model Selection: Examples below use MiniMax-M2.1 as the default for enhanced multi-language and native mobile capabilities. To use MiniMax-M2 for faster Python/JavaScript agent workflows, change the model field:

{ "model": "MiniMax-M2", ... }

Both models share the same API parameters and pricing. See available models for detailed comparison.

Request Example (JSON)

POST /api/v1/chat/completions HTTP/1.1
Host: minimax-m2.com
Authorization: Bearer sk-live-...
Content-Type: application/json
 
{
  "model": "MiniMax-M2.1",
  "messages": [
    { "role": "system", "content": "You are a precise financial analyst." },
    { "role": "user", "content": "Summarize Q4 revenue trends for APAC." }
  ],
  "stream": false,
  "reasoning_split": true
}

cURL

curl https://minimax-m2.com/api/v1/chat/completions \
  -H "content-type: application/json" \
  -H "authorization: Bearer $MINIMAX_API_KEY" \
  -d '{
    "model": "MiniMax-M2.1",
    "messages": [
      { "role": "system", "content": "You are a precise financial analyst." },
      { "role": "user", "content": "Summarize Q4 revenue trends for APAC." }
    ],
    "reasoning_split": true
  }'

Node.js (TypeScript)

import fetch from 'node-fetch';
 
const response = await fetch('https://minimax-m2.com/api/v1/chat/completions', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    Authorization: `Bearer ${process.env.MINIMAX_API_KEY}`,
  },
  body: JSON.stringify({
    model: 'MiniMax-M2.1',
    messages: [
      { role: 'system', content: 'You are a precise financial analyst.' },
      { role: 'user', content: 'Summarize Q4 revenue trends for APAC.' },
    ],
    reasoning_split: true,
  }),
});
 
const data = await response.json();
console.log(data.choices[0].message?.content);

Python

import requests
 
headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json",
}
 
payload = {
    "model": "MiniMax-M2.1",
    "messages": [
        {"role": "system", "content": "You are a precise financial analyst."},
        {"role": "user", "content": "Summarize Q4 revenue trends for APAC."}
    ],
    "reasoning_split": True
}
 
resp = requests.post("https://minimax-m2.com/api/v1/chat/completions", json=payload, headers=headers)
resp.raise_for_status()
print(resp.json()["choices"][0]["message"]["reasoning_details"][0]['text'])
print(resp.json()["choices"][0]["message"]["content"])

Python (OpenAI SDK)

from openai import OpenAI
 
client = OpenAI(
    base_url="https://minimax-m2.com/api/v1/",
    api_key="MINIMAX_API_KEY",
)
 
response = client.chat.completions.create(
    model="MiniMax-M2.1",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hi, how are you?"},
    ],
    extra_body={"reasoning_split": True},
)
 
print(f"Thinking:\\n{response.choices[0].message.reasoning_details[0]['text']}\\n")
print(f"Text:\\n{response.choices[0].message.content}\\n")

Python with MiniMax-M2 (Fast Agent Workflows)

import requests
 
headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json",
}
 
payload = {
    "model": "MiniMax-M2",  # Fast inference for Python/JavaScript agents
    "messages": [
        {"role": "system", "content": "You are a DevOps automation expert."},
        {"role": "user", "content": "Write a Python script to monitor disk usage and send Slack alerts"}
    ],
    "reasoning_split": True,
    "temperature": 0.7
}
 
resp = requests.post("https://minimax-m2.com/api/v1/chat/completions", json=payload, headers=headers)
resp.raise_for_status()
print("Thinking:", resp.json()["choices"][0]["message"]["reasoning_details"][0]['text'])
print("\nCode:", resp.json()["choices"][0]["message"]["content"])

Why M2 here? Optimized for Python agent workflows with fast inference speed (~100 tokens/s).

Streaming Responses

Set stream: true to receive Server-Sent Events (SSE). The data format matches OpenAI’s, enabling drop-in use of existing clients.

data: {"id":"chatcmpl-...","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":"你好"}}],"model":"minimax-m2.1"}
data: {"id":"chatcmpl-...","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":"你好"}}],"model":"minimax-m2.1"}
 
...
data: [DONE]

Usage Metrics

Responses include token usage in the OpenAI schema (usage.prompt_tokens, usage.completion_tokens). These values feed billing and are visible in the dashboard usage explorer.

Chat Completions API

The chat completions endpoint mirrors OpenAI’s v1/chat/completions contract. Use it to stream or fetch MiniMax-M2 responses with minimal code changes.

Endpoint: POST https://minimax-m2.com/api/v1/chat/completions
Auth: Authorization: Bearer <api-key>
Models: MiniMax-M2.1 (default, multi-language & native apps), MiniMax-M2 (general coding & agents) → See model comparison to choose the right one for your workflow

{ "model": "MiniMax-M2", ... }

Both models share the same API parameters and pricing. See available models for detailed comparison.

Request Example (JSON)

POST /api/v1/chat/completions HTTP/1.1
Host: minimax-m2.com
Authorization: Bearer sk-live-...
Content-Type: application/json
 
{
  "model": "MiniMax-M2.1",
  "messages": [
    { "role": "system", "content": "You are a precise financial analyst." },
    { "role": "user", "content": "Summarize Q4 revenue trends for APAC." }
  ],
  "stream": false,
  "reasoning_split": true
}

cURL

curl https://minimax-m2.com/api/v1/chat/completions \
  -H "content-type: application/json" \
  -H "authorization: Bearer $MINIMAX_API_KEY" \
  -d '{
    "model": "MiniMax-M2.1",
    "messages": [
      { "role": "system", "content": "You are a precise financial analyst." },
      { "role": "user", "content": "Summarize Q4 revenue trends for APAC." }
    ],
    "reasoning_split": true
  }'

Node.js (TypeScript)

import fetch from 'node-fetch';
 
const response = await fetch('https://minimax-m2.com/api/v1/chat/completions', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    Authorization: `Bearer ${process.env.MINIMAX_API_KEY}`,
  },
  body: JSON.stringify({
    model: 'MiniMax-M2.1',
    messages: [
      { role: 'system', content: 'You are a precise financial analyst.' },
      { role: 'user', content: 'Summarize Q4 revenue trends for APAC.' },
    ],
    reasoning_split: true,
  }),
});
 
const data = await response.json();
console.log(data.choices[0].message?.content);

Python

import requests
 
headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json",
}
 
payload = {
    "model": "MiniMax-M2.1",
    "messages": [
        {"role": "system", "content": "You are a precise financial analyst."},
        {"role": "user", "content": "Summarize Q4 revenue trends for APAC."}
    ],
    "reasoning_split": True
}
 
resp = requests.post("https://minimax-m2.com/api/v1/chat/completions", json=payload, headers=headers)
resp.raise_for_status()
print(resp.json()["choices"][0]["message"]["reasoning_details"][0]['text'])
print(resp.json()["choices"][0]["message"]["content"])

Python (OpenAI SDK)

from openai import OpenAI
 
client = OpenAI(
    base_url="https://minimax-m2.com/api/v1/",
    api_key="MINIMAX_API_KEY",
)
 
response = client.chat.completions.create(
    model="MiniMax-M2.1",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hi, how are you?"},
    ],
    extra_body={"reasoning_split": True},
)
 
print(f"Thinking:\\n{response.choices[0].message.reasoning_details[0]['text']}\\n")
print(f"Text:\\n{response.choices[0].message.content}\\n")

Python with MiniMax-M2 (Fast Agent Workflows)

import requests
 
headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json",
}
 
payload = {
    "model": "MiniMax-M2",  # Fast inference for Python/JavaScript agents
    "messages": [
        {"role": "system", "content": "You are a DevOps automation expert."},
        {"role": "user", "content": "Write a Python script to monitor disk usage and send Slack alerts"}
    ],
    "reasoning_split": True,
    "temperature": 0.7
}
 
resp = requests.post("https://minimax-m2.com/api/v1/chat/completions", json=payload, headers=headers)
resp.raise_for_status()
print("Thinking:", resp.json()["choices"][0]["message"]["reasoning_details"][0]['text'])
print("\nCode:", resp.json()["choices"][0]["message"]["content"])

Why M2 here? Optimized for Python agent workflows with fast inference speed (~100 tokens/s).

Streaming Responses

Set stream: true to receive Server-Sent Events (SSE). The data format matches OpenAI’s, enabling drop-in use of existing clients.

data: {"id":"chatcmpl-...","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":"你好"}}],"model":"minimax-m2.1"}
data: {"id":"chatcmpl-...","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":"你好"}}],"model":"minimax-m2.1"}
 
...
data: [DONE]

Usage Metrics

Responses include token usage in the OpenAI schema (usage.prompt_tokens, usage.completion_tokens). These values feed billing and are visible in the dashboard usage explorer.

Chat Completions API

Chat Completions API

Request Example (JSON)

cURL

Node.js (TypeScript)

Python

Python (OpenAI SDK)

Python with MiniMax-M2 (Fast Agent Workflows)

Streaming Responses

Usage Metrics

Table of Contents

Chat Completions API

Chat Completions API

Request Example (JSON)

cURL

Node.js (TypeScript)

Python

Python (OpenAI SDK)

Python with MiniMax-M2 (Fast Agent Workflows)

Streaming Responses

Usage Metrics

Table of Contents