Minimax M1 80K
Reason Deep. Context Vast
Scale Reasoning Efficiently
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.
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
import requestsresponse = requests.post("https://modelslab.com/api/v7/llm/chat/completions",json={"key": "YOUR_API_KEY","prompt": "","model_id": ""})print(response.json())
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Start generating with Minimax M1 80K on ModelsLab.