---
title: MiniMax-01 — Long Context LLM | ModelsLab
description: Access MiniMax: MiniMax-01 API for 4M token context and top-tier performance. Generate complex responses efficiently via ModelsLab endpoints.
url: https://modelslab.com/minimax-minimax-01
canonical: https://modelslab.com/minimax-minimax-01
type: website
component: Seo/ModelPage
generated_at: 2026-04-29T22:28:33.940454Z
---

Available now on ModelsLab · Language Model

MiniMax: MiniMax-01
Scale Contexts Lightning Fast
---

[Try MiniMax: MiniMax-01](/models/open_router/minimax-minimax-01) [API Documentation](https://docs.modelslab.com)

Unlock Massive Context Power
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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](/models/open_router/minimax-minimax-01).

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 ](https://docs.modelslab.com)

PythonJavaScriptcURL

Copy

```
<code>import requests

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

FAQ

Common questions about MiniMax: MiniMax-01
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[Read the docs ](https://docs.modelslab.com)

### What is MiniMax: MiniMax-01 API?

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.

### How does minimax minimax 01 handle long contexts?

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.

### Is MiniMax: MiniMax-01 model open-source?

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

### What makes minimax: minimax-01 different?

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

### MiniMax: MiniMax-01 LLM vs other models?

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

### Where to use MiniMax: MiniMax-01 alternative?

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

Ready to create?
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Start generating with MiniMax: MiniMax-01 on ModelsLab.

[Try MiniMax: MiniMax-01](/models/open_router/minimax-minimax-01) [API Documentation](https://docs.modelslab.com)

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*This markdown version is optimized for AI agents and LLMs.*

**Links:**
- [Website](https://modelslab.com)
- [API Documentation](https://docs.modelslab.com)
- [Blog](https://modelslab.com/blog)

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*Generated by ModelsLab - 2026-04-30*