MiniMax M2.7 Model Leaked Online
💡Leak reveals potential new MiniMax model—grab previews before official drop (r/LocalLLaMA)
⚡ 30-Second TL;DR
What Changed
Leaked on DesignArena platform
Why It Matters
This leak could preview upcoming MiniMax capabilities, exciting local LLM enthusiasts. Early access might spur community fine-tunes before official release.
What To Do Next
Check DesignArena for MiniMax M2.7 previews and monitor r/LocalLLaMA for downloads.
Key Points
- •Leaked on DesignArena platform
- •Website documentation removed shortly after leak
- •Preview images shared on Reddit r/LocalLLaMA
- •Submitted by u/External_Mood4719
🧠 Deep Insight
Background and context from public sources — not the original article. 8 sources cited.
🔑 Enhanced Key Takeaways
- •MiniMax M2 is an open-source MoE model with 230B total parameters and 10B active parameters at inference, optimized for coding and agentic workflows.[1][3][6]
- •It excels in elite coding, debugging multi-file repositories, agentic toolchains, and handwritten OCR, outperforming many models in community tests.[3]
- •M2 powers MiniMax Agent with Lightning Mode for fast tasks and Pro Mode for complex workflows like research and development, currently offered free.[2]
📊 Competitor Analysis▸ Show
| Feature | MiniMax M2 | Claude Sonnet 4.5 |
|---|---|---|
| Active Parameters | 10B (230B total MoE) | Not specified[1] |
| Inference Speed | ~100 t/s (or 48.2 t/s measured)[1][5] | ~50 t/s (half of M2)[1] |
| Pricing | $0.255/M input, $1/M output[6] | Not directly compared[1] |
| Benchmarks | Strong on SWE-Bench, Multi-SWE-Bench, Terminal-Bench, GAIA[6] | Competitive in programming, tool use[2] |
🛠️ Technical Deep Dive
- •Mixture-of-Experts (MoE) architecture: 230 billion total parameters, 10 billion active per inference for efficiency.[1][3][4][6]
- •Context length: 200k-205k tokens; max output: 128k tokens including chain-of-thought.[4][5][6]
- •Inference speed: ~100 tokens/second claimed, 48.2 t/s measured; supports vLLM/SGLang deployment on consumer hardware.[1][5][8]
- •Capabilities: Polyglot code mastery, function calling, advanced reasoning, multimodal agent support (text/video/audio/image).[1][2][4]
- •Deployment: Runs on 96G x4 GPUs (400K KV cache), up to 144G x8 GPUs (3M tokens).[8]
🔮 Future ImplicationsAI analysis grounded in cited sources
⏳ Timeline
📎 Sources (8)
Factual claims are grounded in the sources below. Forward-looking analysis is AI-generated interpretation.
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Original source: Reddit r/LocalLLaMA ↗
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