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large language

MiniMax M2.5

MiniMax M2.5 delivers strong performance for coding and agentic tasks. The model is built with agentic task completion speed in mind.

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Model details

Developed by
MiniMax
Model family
MiniMax
Use case
large language
Version
M2.5
Size
230B
License
MIT

Example usage

MiniMax M2.5 is a 230B-parameter MoE model with 10B active parameters. It scores competitively across benchmarks for coding and agentic tool use.

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}