MiniMax M3 (GMI Cloud)

Official GMI Cloud MiniMax M3 inference endpoint. 100% free for 14 days with 1M MSA context and OpenAI-compatible API.

Updated 8/25/20262 code examples

About the Model

Why Choose MiniMax M3?

MiniMax M3 is MiniMax's flagship coding and reasoning model engineered for long-horizon autonomous agents, full-repository refactoring, and complex logical tasks. On SWE-bench Pro, it scores 59.0%, closely approaching Kimi K3 and outperforming DeepSeek V4.

Core Strengths & Key Features

1 Million Token MSA Sparse Attention: Ingests hundreds of thousands of lines of codebase context with low latency and minimal compute overhead.
Top-Tier Agentic Coding: Native tool calling and multi-step self-correction, scoring 66.0% on Terminal-Bench 2.1.
Multimodal Troubleshooting: Understands screenshots, architecture diagrams, and UI recordings to generate immediate fixes.
100% Free for 14 Days: Available with zero fee on GMI Cloud during MiniMax Week (no credit card or deposit needed).

View all free providers for MiniMax M3

How to Access for Free (via GMI Cloud)

Free Offer Details

From August 24 to September 6, 2026, GMI Cloud and MiniMax host "MiniMax Week", offering completely free API calls to minimax-m3 without credit card verification or balance top-ups.

Quick IDE & Tool Integration

Compatible with the standard OpenAI API format. Set Base URL to https://api.gmi-serving.com/v1 in Cursor, Claude Code, Trae, or OpenCode.

📖 View Full Guide

Try it in your browser

Pick a model, paste your own free key, and run. Your key is sent once to call the provider and never stored on our servers.

Code Examples

curl

curl https://api.gmi-serving.com/v1/chat/completions \
  -H "Authorization: Bearer $GMI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "minimax-m3",
    "messages": [
      {"role": "user", "content": "请分析基于分布式 Raft 协议的选主逻辑并给出优化方案。"}
    ],
    "temperature": 0.2
  }'

python

from openai import OpenAI
import os

client = OpenAI(
    base_url="https://api.gmi-serving.com/v1",
    api_key=os.environ.get("GMI_API_KEY"),
)

response = client.chat.completions.create(
    model="minimax-m3",
    messages=[
        {"role": "system", "content": "You are an expert full-stack engineer."},
        {"role": "user", "content": "请分析工程架构瓶颈并生成重构后的 TypeScript 代码。"}
    ],
    temperature=0.3,
    stream=True,
)

for chunk in response:
    content = chunk.choices[0].delta.content
    if content:
        print(content, end="", flush=True)

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