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Available now on ModelsLab · Language Model

Minimax M1 40KMillion-token reasoning. Lean compute.

Try Minimax M1 40KAPI Documentation

Efficient reasoning at scale

Lightning-Fast Processing

1M token context window

Process entire documents and complex multi-step tasks without losing context or coherence.

Computational Efficiency

30% lower compute cost

Hybrid-attention architecture activates only relevant model components per task.

Production-Ready

Tool use and agents

Integrate external APIs, calculators, and search for autonomous multi-step workflows.

Examples

See what Minimax M1 40K can create

Copy any prompt below and try it yourself in the playground.

Code Review

“Review this Python microservice for performance bottlenecks, security vulnerabilities, and architectural improvements. Provide specific line-by-line recommendations with refactored code examples.”

Document Analysis

“Extract key findings, methodology, and conclusions from this 200-page research paper. Summarize in structured format with cross-references to supporting sections.”

API Integration

“Design a workflow that fetches real-time weather data, calculates optimal travel routes, and books accommodations based on user preferences. Include error handling.”

Math Problem Solving

“Solve this competition-grade algorithm problem step-by-step. Explain time complexity, space complexity, and provide optimized implementations in multiple languages.”

For Developers

A few lines of code.
Million tokens. Three lines.

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

  • Serverless: scales to zero, scales to millions
  • Pay per token, no minimums
  • Python and JavaScript SDKs, plus REST API
API Documentation 
import requests


response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())

FAQ

Common questions about Minimax M1 40K

Read the docs

M1 40K combines a 1M token context window with hybrid-attention architecture that requires only 30% of the compute of comparable models. Its Lightning Attention mechanism and Mixture-of-Experts design make it uniquely efficient for long-context reasoning and tool-use tasks.

The model processes up to 1 million input tokens while maintaining coherence across entire conversations. It excels at finding specific details within massive datasets and connecting ideas across multiple documents without context loss.

Yes. M1 40K scored 55.6% on SWE-bench validation, significantly outperforming other open-weight models. It handles complex coding tasks, debugging, and architectural design at production grade.

Both models support 1M token input, but M1 80K has an 80,000 token reasoning output budget versus M1 40K's 40,000. M1 80K consistently outperforms across benchmarks for deep reasoning tasks.

Yes. M1 40K is built for AI agents with robust tool-use capabilities. It can understand and execute external tools like APIs, calculators, and web search while following specific rules for multi-step tasks.

M1 40K delivers reasoning capabilities rivaling GPT-4 at significantly lower computational cost due to its efficient architecture. It matches Gemini 2.5 Pro's 1M context window while requiring substantially less infrastructure investment.

Ready to create?

Start generating with Minimax M1 40K on ModelsLab.

Try Minimax M1 40KAPI Documentation