MODEL PROFILE

MiniMax-M3.1-Flash-GGUF

unsloth · MiniMax-M3.1-Flash-GGUF

**MiniMax-M3.1-Flash** is a native multimodal mixture-of-experts model for coding, agentic workflows, computer use, and cybersecurity. It supports text, image, and video inputs with a context window of up to **1M tokens**. Building on MiniMax-M3, M3.1-Flash improves task performance and inference efficiency with sparse attention throughout the model, quantization-aware training, and DSpark speculative decoding. **Highlights:** - **Agentic performance:** A generational leap in agentic tasks, coding, and cybersecurity — 76.2 on BankerToolBench, 73.7% on DeepSWE v1.1, 61.7 on OSWorld V2, 29.6 on Agents' Last Exam, and 82.2 on CyberGym. - **Efficient by design:** Sparse attention throughout, quantization-aware training, and DSpark speculative decoding improve inference efficiency.

Visual QAmultimodallicense:other
Context windowNot disclosed
Input → OutputText、Image → Text
Model weightsNot verified
Profile updated2026/11/2 19:15:40
OVERVIEW

Model overview

**MiniMax-M3.1-Flash** is a native multimodal mixture-of-experts model for coding, agentic workflows, computer use, and cybersecurity. It supports text, image, and video inputs with a context window of up to **1M tokens**. Building on MiniMax-M3, M3.1-Flash improves task performance and inference efficiency with sparse attention throughout the model, quantization-aware training, and DSpark speculative decoding. **Highlights:** - **Agentic performance:** A generational leap in agentic tasks, coding, and cybersecurity — 76.2 on BankerToolBench, 73.7% on DeepSWE v1.1, 61.7 on OSWorld V2, 29.6 on Agents' Last Exam, and 82.2 on CyberGym. - **Efficient by design:** Sparse attention throughout, quantization-aware training, and DSpark speculative decoding improve inference efficiency.

Key features

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Limitations & considerations

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Frequently asked questions

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Sources & traceability

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