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MiniMax M2.5

MiniMax/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.

large language

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

MiniMax M2.5 Benchmarks
MiniMax M2.5 Benchmarks

Input

python
# You can use this model with any of the OpenAI clients in any language!
# Simply change the API Key to get started

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://inference.baseten.co/v1"
)

response = client.chat.completions.create(
    model="MiniMaxAI/MiniMax-M2.5",
    messages=[
        {
            "role": "user",
            "content": "Implement Hello World in Python"
        }
    ],
    stream=True,
    stream_options={
        "include_usage": True,
        "continuous_usage_stats": True
    },
    top_p=1,
    max_tokens=1000,
    temperature=1,
    presence_penalty=0,
    frequency_penalty=0
)

for chunk in response:
    if chunk.choices and chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="", flush=True)

Output

json
{
  "id": "143",
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "logprobs": null,
      "message": {
        "content": "[Model output here]",
        "role": "assistant",
        "audio": null,
        "function_call": null,
        "tool_calls": null
      }
    }
  ],
  "created": 1741224586,
  "model": "",
  "object": "chat.completion",
  "service_tier": null,
  "system_fingerprint": null,
  "usage": {
    "completion_tokens": 145,
    "prompt_tokens": 38,
    "total_tokens": 183,
    "completion_tokens_details": null,
    "prompt_tokens_details": null
  }
}

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