large language

MiniMax M2

A frontier open source model built for coding and agentic workflows

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Model details

Developed by
MiniMax
Model family
MiniMax
Use case
large language
Version
M2
Size
230B
License
MIT

Example usage

MiniMax M2 is a 230B-parameter MoE model with 10B active parameters. It scores competitively across benchmarks for coding and agentic tool use.

MiniMax M2 achieves highly competitive benchmark scores

Input

1from openai import OpenAI
2import os
3
4model_url = "" # Copy in from API pane in Baseten model dashboard
5
6client = OpenAI(
7    api_key=os.environ['BASETEN_API_KEY'],
8    base_url=model_url
9)
10
11# Chat completion
12response_chat = client.chat.completions.create(
13    model="",
14    messages=[
15        {"role": "user", "content": "Write FizzBuzz."}
16    ],
17    temperature=0.6,
18    max_tokens=100,
19)
20print(response_chat)

JSON output

1{
2    "id": "143",
3    "choices": [
4        {
5            "finish_reason": "stop",
6            "index": 0,
7            "logprobs": null,
8            "message": {
9                "content": "[Model output here]",
10                "role": "assistant",
11                "audio": null,
12                "function_call": null,
13                "tool_calls": null
14            }
15        }
16    ],
17    "created": 1741224586,
18    "model": "",
19    "object": "chat.completion",
20    "service_tier": null,
21    "system_fingerprint": null,
22    "usage": {
23        "completion_tokens": 145,
24        "prompt_tokens": 38,
25        "total_tokens": 183,
26        "completion_tokens_details": null,
27        "prompt_tokens_details": null
28    }
29}