large language
MiniMax M2.5
MiniMax M2.5MiniMax M2.5 delivers strong performance for coding and agentic tasks. The model is built with agentic task completion speed in mind.
Model details
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.
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MiniMax M2.5 BenchmarksInput
1# You can use this model with any of the OpenAI clients in any language!
2# Simply change the API Key to get started
3
4from openai import OpenAI
5
6client = OpenAI(
7 api_key="YOUR_API_KEY",
8 base_url="https://inference.baseten.co/v1"
9)
10
11response = client.chat.completions.create(
12 model="MiniMaxAI/MiniMax-M2.5",
13 messages=[
14 {
15 "role": "user",
16 "content": "Implement Hello World in Python"
17 }
18 ],
19 stream=True,
20 stream_options={
21 "include_usage": True,
22 "continuous_usage_stats": True
23 },
24 top_p=1,
25 max_tokens=1000,
26 temperature=1,
27 presence_penalty=0,
28 frequency_penalty=0
29)
30
31for chunk in response:
32 if chunk.choices and chunk.choices[0].delta.content is not None:
33 print(chunk.choices[0].delta.content, end="", flush=True)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}
