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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
MiniMax M2.1, a SOTA model for real-world dev & agents.
| Date | Stars |
|---|---|
| 2026-07-31 | 543 |
| 2026-08-02 | 543 |
| 2026-08-06 | 543 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
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MiniMax Agent
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⚡️ API
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MCP
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MiniMax Website
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🤗 Hugging Face
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🐙 GitHub
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🤖️ ModelScope
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📄 License: Modified-MIT
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# Meet MiniMax-M2.1
Today, we are handing **MiniMax-M2.1** over to the open-source community. This release is more than just a parameter update; it is a significant step toward democratizing top-tier agentic capabilities.
M2.1 was built to shatter the stereotype that high-performance agents must remain behind closed doors. We have optimized the model specifically for robustness in coding, tool use, instruction following, and long-horizon planning. From automating multilingual software development to executing complex, multi-step office workflows, MiniMax-M2.1 empowers developers to build the next generation of autonomous applications—all while being fully transparent, controllable, and accessible.
We believe true intelligence should be within reach. M2.1 is our commitment to the future, and a powerful new tool in your hands.
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## How to Use
- The MiniMax-M2.1 API is now live on the MiniMax Open Platform: https://platform.minimax.io/docs/guides/text-generation
- Our product MiniMax Agent, built on MiniMax-M2.1, is now publicly available: https://agent.minimax.io/
- The MiniMax-M2.1 model weights are now open-source, allowing for local deployment and use: https://huggingface.co/MiniMaxAI/MiniMax-M2.1
## Benchmarks
MiniMax-M2.1 delivers a significant leap over M2 on core software engineering leaderboards. It shines particularly bright in multilingual scenarios, where it outperforms Claude Sonnet 4.5 and closely approaches Claude Opus 4.5.
| Benchmark | MiniMax-M2.1 | MiniMax-M2 | Claude Sonnet 4.5 | Claude Opus 4.5 | Gemini 3 Pro | GPT-5.2 (thinking) | DeepSeek V3.2 |
| ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- |
| SWE-bench Verified | 74.0 | 69.4 | 77.2 | 80.9 | 78.0 | 80.0 | 73.1 |
| Multi-SWE-bench | 49.4 | 36.2 | 44.3 | 50.0 | 42.7 | x | 37.4 |
| SWE-bench Multilingual | 72.5 | 56.5 | 68 | 77.5 | 65.0 | 72.0 | 70.2 |
| Terminal-bench 2.0 | 47.9 | 30.0 | 50.0 | 57.8 | 54.2 | 54.0 | 46.4 |
We also evaluated MiniMax-M2.1 on SWE-bench Verified across a variety of coding agent frameworks. The results highlight the model's exceptional framework generalization and robust stability.
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2f07015d8cc4c399, topic:large-language-models, topic:llm
matched fp:2f07015d8cc4c399, topic:agentic-ai