MiniMax: MiniMax M2.7
Self-Evolves. Outperforms Giants
Build Agents That Improve
Self-Evolving Core
Autonomously Optimizes Itself
Runs 100+ scaffold iterations, handles 30-50% of RL workflows without humans.
Agentic Power
Masters Complex Harnesses
Builds agent teams, dynamic tools, 97% skill compliance on 40+ complex tasks.
Efficient Inference
10B Params, Tier-1 Scores
Activates 10B parameters for 56% SWE-Pro, 100 TPS, $0.30/M input via MiniMax: MiniMax M2.7 API.
Examples
See what MiniMax: MiniMax M2.7 can create
Copy any prompt below and try it yourself in the playground.
Code Scaffold
“Design an agent harness in Python using OpenClaw framework to optimize reinforcement learning experiments. Include memory updates, skill building for 40 complex tasks over 2000 tokens each, and self-evaluation loops for 100 iterations.”
Workflow Debug
“Analyze this failing ML pipeline code, identify root causes, propose fixes, and generate an improved version with agentic multi-step reasoning for production deployment.”
Financial Model
“Build Excel-compatible financial model for revenue forecasting using historical data. Include sensitivity analysis, Monte Carlo simulations, and export to spreadsheet format.”
Document Pipeline
“Generate full technical report on AI agent benchmarks in Word format. Cover SWE-Pro 56%, Terminal Bench, include charts, executive summary, and self-optimization recommendations.”
For Developers
A few lines of code.
Agents Evolve. Code Ships.
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())
Ready to create?
Start generating with MiniMax: MiniMax M2.7 on ModelsLab.