large language
MiniMax M2.1
MiniMax M2.1An update to MiniMax M2 with improved coding and reasoning performance
Model details
Example usage
MiniMax M2.1 is a 230B-parameter MoE model with 10B active parameters. It scores competitively across benchmarks for coding and agentic tool use.
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MiniMax M2.1 shows improved performance across coding benchmarksInput
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}
