---
title: Minimax M1 80K — Reasoning LLM | ModelsLab
description: Access Minimax M1 80K API for 1M token context and 80K reasoning output. Generate complex solutions via lightning attention MoE model. Try now.
url: https://modelslab.com/minimax-m1-80k
canonical: https://modelslab.com/minimax-m1-80k
type: website
component: Seo/ModelPage
generated_at: 2026-04-15T02:02:47.606495Z
---

Available now on ModelsLab · Language Model

Minimax M1 80K
Reason Deep. Context Vast
---

[Try Minimax M1 80K](/models/together_ai/MiniMaxAI-MiniMax-M1-80k) [API Documentation](https://docs.modelslab.com)

Scale Reasoning Efficiently
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Hybrid MoE

### 456B Parameters Active 46B

Activates 45.9B parameters per token in Mixture-of-Experts for efficient complex reasoning.

Lightning Attention

### 1M Token Context

Processes 1 million input tokens with 80K output using hybrid attention for long documents.

Test-Time Scaling

### 80K Thinking Budget

Extends compute for superior SWE-bench (56%) and AIME 2024 (86%) performance.

Examples

See what Minimax M1 80K can create
---

Copy any prompt below and try it yourself in the [playground](/models/together_ai/MiniMaxAI-MiniMax-M1-80k).

Code Debug

“Analyze this 50K token Python codebase with bugs in the async handler. Step through logic, identify issues in dependency injection and error handling, then output fixed code with explanations.”

Document Summary

“Summarize key insights from this 800K token technical report on quantum computing advancements, highlighting breakthroughs in error correction and scalability challenges.”

Math Proof

“Prove the Riemann hypothesis implications for prime distribution using chain-of-thought reasoning over extended steps, citing relevant theorems and counterexamples.”

Agent Workflow

“Design multi-step agent plan to optimize supply chain logistics from this 200K token dataset, incorporating tool calls for inventory query and route optimization.”

For Developers

A few lines of code.
Reasoning chains. 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 M1 80K
---

[Read the docs ](https://docs.modelslab.com)

### What is Minimax M1 80K?

### How does Minimax M1 80K API work?

### What is Minimax M1 80K context length?

### Is Minimax M1 80K good for coding?

### Minimax M1 80K vs DeepSeek R1?

### Best Minimax M1 80K alternative?

Ready to create?
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Start generating with Minimax M1 80K on ModelsLab.

[Try Minimax M1 80K](/models/together_ai/MiniMaxAI-MiniMax-M1-80k) [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 - 2026-04-15*