MiniMax M2.7
MiniMax-M2.7 is a next-generation large language model built for autonomous real-world productivity and continuous improvement. Through advanced agentic capabilities and multi-agent collaboration, it can plan, execute, evaluate, and refine complex tasks in dynamic environments while actively contributing to its own evolution. Optimized for production-grade workflows, M2.7 excels at live debugging, root-cause analysis, financial modeling, and end-to-end document creation across Word, Excel, and PowerPoint. It achieves 56.2% on SWE-Pro, 57.0% on Terminal Bench 2, and a 1495 ELO rating on GDPval-AA, establishing a new benchmark for multi-agent systems operating in real-world digital workflows.
MiniMax M2.7
MiniMax-M2.7 is a next-generation large language model built for autonomous real-world productivity and continuous improvement. Through advanced agentic capabilities and multi-agent collaboration, it can plan, execute, evaluate, and refine complex tasks in dynamic environments while actively contributing to its own evolution.
Optimized for production-grade workflows, M2.7 excels at live debugging, root-cause analysis, financial modeling, and end-to-end document creation across Word, Excel, and PowerPoint. It achieves 56.2% on SWE-Pro, 57.0% on Terminal Bench 2, and a 1495 ELO rating on GDPval-AA, establishing a new benchmark for multi-agent systems operating in real-world digital workflows.
Base URL
https://api.icreat.ai/llm/openai/v1Authentication
All API requests must be authenticated with an API Key. You can obtain an API Key from the console.
export ICREAT_API_KEY="your-api-key-here"HTTP Request Headers
import os
API_KEY = os.environ.get("ICREAT_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": "Bearer " + API_KEY,
}Protect your API Key
Never expose your API Key in client-side code or public repositories. Use environment variables or a backend proxy.
Code Examples
This model is invoked via the OpenAI-compatible Chat Completions API and supports both streaming and non-streaming modes. With stream: false (default), the server returns the full JSON response at once. With stream: true, partial deltas are pushed as Server-Sent Events (SSE).
Input Schema
The following parameters are accepted in the request body.
Total: 6 Required: 2 Optional: 4
The model ID for the completion. Must be the iCreat model_code (MiniMax-M2.7).
Example: "MiniMax-M2.7"
Conversation messages.
Maximum number of tokens to generate.
Sampling temperature, 0–2.
If true, stream via Server-Sent Events.
Extended thinking configuration (if supported).
Output Schema
OpenAI-compatible Chat Completions response.
Total: 6
Unique completion identifier.
Object type, always chat.completion.
Unix timestamp.
Model ID used.
List of completion choices.
Token usage statistics.
LLM-friendly prompt
Below is an LLM-friendly Markdown prompt you can copy into Cursor, ChatGPT, or other AI assistants to help them understand this model's API integration, call flow, and key parameters.
# MiniMax-M2.7
> MiniMax-M2.7 is a next-generation large language model built for autonomous real-world productivity and continuous improvement.
## Overview
Call via iCreat OpenAI-compatible Chat Completions API; supports streaming and non-streaming and extended thinking.
## API Info
- **Base URL**: `https://api.icreat.ai/llm/openai/v1`
- **Endpoint (POST)**: `/chat/completions`
- **Model ID**: `MiniMax-M2.7`
- **Auth**: `Authorization: Bearer ${ICREAT_API_KEY}`
## Call Flow
Single POST; `stream: false` returns full JSON, `stream: true` streams via SSE.
### Input
- `model` (required): iCreat model_code `MiniMax-M2.7`
- `messages` (required): conversation messages
- Common optional: `max_tokens`, `temperature`, `stream`, `thinking`
### Output
- Read reply from `choices[0].message.content`
## Notes
- `model` must be the iCreat model_code
- Other fields follow the OpenAI Chat Completions protocol