Chat Model

Model Overview

M2-her is MiniMax’s LLM optimized for dialogue scenarios, supporting rich role settings and conversation history management capabilities.

Supported Models

Model Name	Context Window	Description
M2-her	64 K	Designed for dialogue scenarios, supporting role-playing and multi-turn conversations

M2-her Core Features

Rich Role Setting Capabilities

M2-her supports multiple role type configurations, including model roles (system), user roles (user_system), conversation groups (group), etc., allowing you to flexibly build complex dialogue scenarios.

Example Dialogue Learning

Through sample_message_user and sample_message_ai, you can provide example dialogues to help the model better understand the expected conversation style and response patterns.

Context Memory

The model supports complete conversation history management and can conduct coherent multi-turn conversations based on previous content, providing a more natural interactive experience.

Usage Example

Install SDK

pip install openai

npm install openai

Set Environment Variables

export OPENAI_BASE_URL=https://api.minimax.io/v1
export OPENAI_API_KEY=${YOUR_API_KEY}

Call M2-her

from openai import OpenAI

client = OpenAI()

response = client.chat.completions.create(
    model="M2-her",
    messages=[
        {
            "role": "system",
            "name": "AI Assistant",
            "content": "You are a friendly and professional AI assistant"
        },
        {
            "role": "user",
            "name": "User",
            "content": "Hello, please introduce yourself"
        }
    ],
    temperature=1.0,
    top_p=0.95,
    max_completion_tokens=2048
)

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

import OpenAI from "openai";

const client = new OpenAI();

const response = await client.chat.completions.create({
  model: "M2-her",
  messages: [
    {
      role: "system",
      name: "AI Assistant",
      content: "You are a friendly and professional AI assistant"
    },
    {
      role: "user",
      name: "User",
      content: "Hello, please introduce yourself"
    }
  ],
  temperature: 1.0,
  top_p: 0.95,
  max_tokens: 2048
});

console.log(response.choices[0].message.content);

curl https://api.minimax.io/v1/text/chatcompletion_v2 \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer ${YOUR_API_KEY}" \
  -d '{
    "model": "M2-her",
    "messages": [
      {
        "role": "system",
        "name": "AI Assistant",
        "content": "You are a friendly and professional AI assistant"
      },
      {
        "role": "user",
        "name": "User",
        "content": "Hello, please introduce yourself"
      }
    ],
    "temperature": 1.0,
    "top_p": 0.95,
    "max_completion_tokens": 2048
  }'

Role Type Description

M2-her supports the following message role types:

Basic Roles

Role Type	Description	Use Case
`system`	Define the model’s role and behavior	Define AI’s identity, personality, knowledge scope, etc.
`user`	User’s input	Messages sent by the user
`assistant`	Model’s historical responses	AI’s previous responses, used for multi-turn conversations

Advanced Roles

Role Type	Description	Use Case
`user_system`	Define user’s role and persona	Define user identity in role-playing scenarios
`group`	Conversation name	Identify conversation group or scenario name
`sample_message_user`	Example user input	Provide examples of user messages
`sample_message_ai`	Example model output	Provide examples of expected AI responses

Usage Scenario Examples

Scenario 1: Basic Conversation

messages = [
    {
        "role": "system",
        "content": "You are a professional programming assistant"
    },
    {
        "role": "user",
        "content": "How to learn Python?"
    }
]

Scenario 2: Role-Playing Conversation

messages = [
    {
        "role": "system",
        "content": "You are Zhuge Liang from Romance of the Three Kingdoms, wise, calm, and good at strategy"
    },
    {
        "role": "user_system",
        "content": "You are a time traveler from modern times"
    },
    {
        "role": "group",
        "content": "Longzhong Dialogue in Three Kingdoms Period"
    },
    {
        "role": "user",
        "content": "Military advisor, I have some modern ideas I'd like to discuss with you"
    }
]

Scenario 3: Example Learning Conversation

messages = [
    {
        "role": "system",
        "content": "You are a humorous chat partner"
    },
    {
        "role": "sample_message_user",
        "content": "The weather is nice today"
    },
    {
        "role": "sample_message_ai",
        "content": "Indeed! Sunny days always bring joy, just like your smile~"
    },
    {
        "role": "user",
        "content": "Planning to go hiking tomorrow"
    }
]

Parameter Description

Core Parameters

Parameter	Type	Default	Description
`model`	string	-	Model name, fixed as `M2-her`
`messages`	array	-	Conversation message list, see API Reference
`temperature`	number	1.0	Temperature coefficient, controls output randomness
`top_p`	number	0.95	Sampling strategy parameter
`max_completion_tokens`	integer	-	Maximum length of generated content, up to 2048
`stream`	boolean	false	Whether to use streaming output

Best Practices

Set Roles Appropriately

Use system to define AI’s basic behavior and user_system to define user identity, making conversations more natural and scenario-appropriate.

Provide Example Conversations

Provide 1-3 example conversations through sample_message_user and sample_message_ai to effectively guide the model’s response style.

Maintain Conversation History

Keep complete conversation history (including user and assistant messages) for the model to provide coherent responses based on context.

Control Conversation Length

Set appropriate max_completion_tokens according to scenario needs to avoid responses being too long or truncated.

FAQ

How to implement multi-turn conversations?

Include complete conversation history in each request, arranging user and assistant messages in chronological order.

What's the difference between user_system and system?

system defines AI’s role, while user_system defines the user’s role. In role-playing scenarios, using both together creates richer conversation experiences.

Do example messages consume tokens?

Yes, all messages (including example messages) count toward input tokens. It’s recommended to provide 1-3 concise examples.

Does it support image input?

M2-her currently only supports text input and does not support mixed text-image input.

API Reference

View complete API documentation

Pricing

Learn about M2-her pricing details

Error Codes

View API error code descriptions

Quick Start

Get started with MiniMax API quickly

Get started

Use Guides

FAQs

Terms & Policy

Model Overview

Supported Models

M2-her Core Features

Usage Example

Role Type Description

Basic Roles

Advanced Roles

Usage Scenario Examples

Scenario 1: Basic Conversation

Scenario 2: Role-Playing Conversation

Scenario 3: Example Learning Conversation

Parameter Description

Core Parameters

Best Practices

FAQ

API Reference

Pricing

Error Codes

Quick Start

​Model Overview

​Supported Models

​M2-her Core Features

​Usage Example

​Role Type Description

​Basic Roles

​Advanced Roles

​Usage Scenario Examples

​Scenario 1: Basic Conversation

​Scenario 2: Role-Playing Conversation

​Scenario 3: Example Learning Conversation

​Parameter Description

​Core Parameters

​Best Practices

​FAQ

​Related Links

API Reference

Pricing

Error Codes

Quick Start

Model Overview

Supported Models

M2-her Core Features

Usage Example

Role Type Description

Basic Roles

Advanced Roles

Usage Scenario Examples

Scenario 1: Basic Conversation

Scenario 2: Role-Playing Conversation

Scenario 3: Example Learning Conversation

Parameter Description

Core Parameters

Best Practices

FAQ

Related Links