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Documentation Index

Fetch the complete documentation index at: https://platform.minimax.io/docs/llms.txt

Use this file to discover all available pages before exploring further.

To meet developers’ needs for the OpenAI API ecosystem, our API now supports the OpenAI API format. With simple configuration, you can integrate MiniMax capabilities into the OpenAI API ecosystem.

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Quick Start

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1. Install OpenAI SDK

pip install openai

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2. Configure Environment Variables

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

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3. Call API

Python
from openai import OpenAI

client = OpenAI()

response = client.chat.completions.create(
    model="MiniMax-M2.7",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hi, how are you?"},
    ],
    # Set reasoning_split=True to separate thinking content into reasoning_details field
    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")

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4. Important Note

In multi-turn function call conversations, the complete model response (i.e., the assistant message) must be append to the conversation history to maintain the continuity of the reasoning chain.
  • Append the full response_message object (including the tool_calls field) to the message history
    • For native OpenAI API with MiniMax-M2.7 MiniMax-M2.7-highspeed MiniMax-M2.5 MiniMax-M2.5-highspeed MiniMax-M2.1 MiniMax-M2.1-highspeed MiniMax-M2 models, the content field will contain <think> tag content, which must be preserved completely
    • In the Interleaved Thinking compatible format, by enabling the additional parameter (reasoning_split=True), the model’s thinking content is provided separately via the reasoning_details field, which must also be preserved completely

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Supported Models

When using the OpenAI SDK, the following MiniMax models are supported:
Model NameContext WindowDescription
MiniMax-M2.7204,800Beginning the journey of recursive self-improvement (output speed approximately 60 tps)
MiniMax-M2.7-highspeed204,800M2.7 Highspeed: Same performance, faster and more agile (output speed approximately 100 tps)
MiniMax-M2.5204,800Peak Performance. Ultimate Value. Master the Complex (output speed approximately 60 tps)
MiniMax-M2.5-highspeed204,800M2.5 highspeed: Same performance, faster and more agile (output speed approximately 100 tps)
MiniMax-M2.1204,800Powerful Multi-Language Programming Capabilities with Comprehensively Enhanced Programming Experience (output speed approximately 60 tps)
MiniMax-M2.1-highspeed204,800Faster and More Agile (output speed approximately 100 tps)
MiniMax-M2204,800Agentic capabilities, Advanced reasoning
For details on how tps (Tokens Per Second) is calculated, please refer to FAQ > About APIs.
For more model information, please refer to the standard MiniMax API documentation.

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Examples

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Streaming Response

Python
from openai import OpenAI

client = OpenAI()

print("Starting stream response...\n")
print("=" * 60)
print("Thinking Process:")
print("=" * 60)

stream = client.chat.completions.create(
    model="MiniMax-M2.7",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hi, how are you?"},
    ],
    # Set reasoning_split=True to separate thinking content into reasoning_details field
    extra_body={"reasoning_split": True},
    stream=True,
)

reasoning_buffer = ""
text_buffer = ""

for chunk in stream:
    if (
        hasattr(chunk.choices[0].delta, "reasoning_details")
        and chunk.choices[0].delta.reasoning_details
    ):
        for detail in chunk.choices[0].delta.reasoning_details:
            if "text" in detail:
                reasoning_text = detail["text"]
                new_reasoning = reasoning_text[len(reasoning_buffer) :]
                if new_reasoning:
                    print(new_reasoning, end="", flush=True)
                    reasoning_buffer = reasoning_text

    if chunk.choices[0].delta.content:
        content_text = chunk.choices[0].delta.content
        new_text = content_text[len(text_buffer) :] if text_buffer else content_text
        if new_text:
            print(new_text, end="", flush=True)
            text_buffer = content_text

print("\n" + "=" * 60)
print("Response Content:")
print("=" * 60)
print(f"{text_buffer}\n")

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Tool Use & Interleaved Thinking

Learn how to use M2.1 Tool Use and Interleaved Thinking capabilities with OpenAI SDK, please refer to the following documentation.

M2.1 Tool Use & Interleaved Thinking

Learn how to leverage MiniMax-M2.1 tool calling and interleaved thinking capabilities to enhance performance in complex tasks.

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Important Notes

  1. The temperature parameter range is (0.0, 1.0], recommended value: 1.0, values outside this range will return an error
  2. Some OpenAI parameters (such as presence_penalty, frequency_penalty, logit_bias, etc.) will be ignored
  3. Image and audio type inputs are not currently supported
  4. The n parameter only supports value 1
  5. The deprecated function_call is not supported, please use the tools parameter