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.
Quick Start
1. Install OpenAI SDK
For international users, use https://api.minimax.io/v1; for users in China, use https://api.minimaxi.com/v1
export OPENAI_BASE_URL=https://api.minimax.io/v1
export OPENAI_API_KEY=${YOUR_API_KEY}
3. Call API
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="MiniMax-M2",
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")
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 model, 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
Supported Models
When using the OpenAI SDK, the following MiniMax models are supported:
| Model Name | Description |
|---|
| MiniMax-M2 | Agentic capabilities, Advanced reasoning |
| MiniMax-M2-Stable | High concurrency and commercial use |
For more model information, please refer to the standard MiniMax API
documentation.
Examples
Streaming Response
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",
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")
Important Notes
-
The
temperature parameter range is (0.0, 1.0], recommended value: 1.0, values outside this range will return an error
-
Some OpenAI parameters (such as
presence_penalty, frequency_penalty, logit_bias, etc.) will be ignored
-
Image and audio type inputs are not currently supported
-
The
n parameter only supports value 1
-
The deprecated
function_call is not supported, please use the tools parameter
Recommended Reading
