---
title: MiniMax M2.7 — Self-Evolving LLM | ModelsLab
description: Access MiniMax: MiniMax M2.7 API for self-evolving agents that optimize code and workflows. Try MiniMax: MiniMax M2.7 model now.
url: https://modelslab-frontend-v2-927501783998.us-east4.run.app/minimax-minimax-m27
canonical: https://modelslab-frontend-v2-927501783998.us-east4.run.app/minimax-minimax-m27
type: website
component: Seo/ModelPage
generated_at: 2026-05-13T10:35:55.083372Z
---

Available now on ModelsLab · Language Model

MiniMax: MiniMax M2.7
Self-Evolves. Outperforms Giants
---

[Try MiniMax: MiniMax M2.7](/models/open_router/minimax-minimax-m2.7) [API Documentation](https://docs.modelslab.com)

Build Agents That Improve
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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
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Copy any prompt below and try it yourself in the [playground](/models/open_router/minimax-minimax-m2.7).

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

[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 M2.7
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[Read the docs ](https://docs.modelslab.com)

### What is MiniMax: MiniMax M2.7?

MiniMax: MiniMax M2.7 is a 10B parameter LLM with self-evolution, running 100+ optimization cycles. It rivals larger models on SWE-Pro at 56%. Access via MiniMax: MiniMax M2.7 API.

### How does minimax minimax m2 7 self-improve?

It updates memory, builds skills, and refines harnesses autonomously. Handles 30-50% RL workflows and won 9 ML competition golds. Ties Gemini on MLE-Bench.

### What is MiniMax: MiniMax M2.7 API pricing?

Costs $0.30 per million input tokens. Delivers 46-100 TPS with 200k context. Suitable for agentic tasks via minimax: minimax m2.7 endpoints.

### Is MiniMax: MiniMax M2.7 good for coding?

Scores 56% on SWE-Pro, 57% Terminal Bench. Excels in AI coding tools, multi-turn dialogue. Use MiniMax: MiniMax M2.7 model for production pipelines.

### MiniMax: MiniMax M2.7 alternative to Claude?

Matches Opus-level benchmarks at 50x lower cost, 3x speed. Self-evolving agents make it ideal alternative. Try MiniMax: MiniMax M2.7 LLM for efficiency.

### What context does minimax minimax m2 7 api support?

Supports 205k token context window for long conversations. Handles complex agent harnesses and document generation. Integrates via standard LLM endpoints.

Ready to create?
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Start generating with MiniMax: MiniMax M2.7 on ModelsLab.

[Try MiniMax: MiniMax M2.7](/models/open_router/minimax-minimax-m2.7) [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 - 2026-05-13*