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MiniMax: MiniMax-01Scale Contexts Lightning Fast

Try MiniMax: MiniMax-01API Documentation

Unlock Massive Context Power

Hybrid Attention

Lightning Attention Core

Combines lightning attention with softmax every 7 layers for linear efficiency on 4M tokens.

MoE Architecture

456B Parameters Efficiently

Activates 45.9B parameters per token across 32 experts in 80-layer structure.

Long Context

4 Million Tokens

Handles inference up to 4M tokens, 20-32x longer than leading models like GPT-4o.

Examples

See what MiniMax: MiniMax-01 can create

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

Code Refactor

“Analyze this 500k token codebase for Python refactoring. Identify inefficiencies in async functions, suggest optimizations using type hints and context managers, output refactored modules with explanations.”

Document Summary

“Summarize this 2M token technical report on AI scaling laws. Extract key findings on parameter efficiency, context limits, and benchmark comparisons to GPT-4o, structure as bullet points with metrics.”

Reasoning Chain

“Solve this multi-step math problem using chain-of-thought over 1M token context of theorems and proofs. Compute integral of exp(-x^2) from -inf to inf, verify with historical derivations.”

Agent Planning

“Plan a software project roadmap from this 3M token spec document. Break into phases, assign tasks with dependencies, estimate timelines using historical data in context.”

For Developers

A few lines of code.
4M tokens. One call.

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: MiniMax-01

Read the docs

MiniMax: MiniMax-01 is a 456B parameter LLM with 45.9B activated per token. It uses hybrid lightning attention for 4M token context. Access via ModelsLab LLM endpoint.

Trained to 1M tokens, infers up to 4M with minimal degradation. Lightning attention achieves near-linear complexity. Outperforms models like Claude-3.5-Sonnet on long inputs.

Yes, available on Hugging Face and GitHub. Includes MiniMax-Text-01 and MiniMax-VL-01. Matches GPT-4o benchmarks with longer context.

Hybrid MoE with 32 experts, 64 attention heads. 80 layers balance depth and speed. Alternative to traditional Transformers for efficiency.

Comparable to top models on benchmarks. 20-32x longer context than Gemini 1.5 Pro. Optimized for code, reasoning, multimodal tasks.

Ideal for long-document analysis, codebases, agentic workflows. Integrate minimax minimax 01 api via ModelsLab for scalable inference.

Ready to create?

Start generating with MiniMax: MiniMax-01 on ModelsLab.

Try MiniMax: MiniMax-01API Documentation