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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