MiniMax M2.7
minimax/minimax-m2.7
MiniMax-M2.5 is a SOTA large language model designed for real-world productivity. Trained in a diverse range of complex real-world digital working environments, M2.5 builds upon the coding expertise of M2.1 to extend into general office work, reaching fluency in generating and operating Word, Excel, and Powerpoint files, context switching between diverse software environments, and working across different agent and human teams. Scoring 80.2% on SWE-Bench Verified, 51.3% on Multi-SWE-Bench, and 76.3% on BrowseComp, M2.5 is also more token efficient than previous generations, having been trained to optimize its actions and output through planning.
Price
| Input | $0.3per million tokens |
| Cached reads | $0.03per million tokens |
| Output | $1.2per million tokens |
Use the following code example to integrate our API:
1from openai import OpenAI
2
3client = OpenAI(
4 api_key="<Your API Key>",
5 base_url="https://api.highwayapi.ai/openai"
6)
7
8response = client.chat.completions.create(
9 model="minimax/minimax-m2.7",
10 messages=[
11 {"role": "system", "content": "You are a helpful assistant."},
12 {"role": "user", "content": "Hello, how are you?"}
13 ],
14 max_tokens=131100,
15 temperature=0.7
16)
17
18print(response.choices[0].message.content)Information
Provider
Quantization
fp8
Supported Features
Context Length
204.8K
Maximum Output
131.1K
Function Calling
Supported
Structured Outputs
Supported
Reasoning
Supported
serverless
Supported
Input Capabilities
text
Output Capabilities
text