MiniMax 发布 M3.1-Flash 多模态 MoE 模型

简体中文 MiniMax MiniMax Agent API MiniMax Website ModelScope MiniMax AI WeChat Discord Hugging Face GitHub arXiv Paper 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. Model at a Glance - Architecture: Native multimodal mixture-of-experts (MoE) - Parameters: Approximately 428B total; 23B activated per token - Inputs / output: Text, image, and video inputs; text output - Context window: Up to 1,000,000 tokens - Attention: MiniMax Sparse Attention (MSA) throughout the model - Speculative decoding: DSpark draft head included Benchmarks Benchmark MiniMax M3.1 Flash DeepSeek V4.1 Flash-1017 DeepSeek V4.1 Flash Kimi K3 GLM-5.3 GPT-5.6 Sol Opus 5 Automation-Bench v1.0.6 62.8 60.5 54.8 46.7 48.8 45.8 50.3 BankerToolBench 76.2 — 73.7 69.1 74.5 74.6 76.2 GDP.pdf AA 20.8 — 12.8 22.0 11.2 27.2 21.6 Terminal Bench 4.0 38.1 42.3 31.2 12.6 37.9 39.9 51.8 LOCA-Bench 256K 60.3 — — 49.7 50.3 60.0 53.7 Agents’ Last Exam 29.6 — — 28.3 — 30.6 32.2 OSWorld 2.0 61.7 — 54.3 58.3 — 65.7 70.6 PostTrain Bench v1.1 39.3 — 30.5 36.6 37.5 36.2 40.4 DeepSWE v1.1 73.7 77.0 74.2 67.5 66.9 73.0 74.0 NL2Repo-Bench 61.2 68.2 65.4 58.0 58.0 56.8 75.3 SWE Refactor Bench 23.0 — 11.8 19.5 10.4 28.5 31.0 CyberGym 82.2 — — 80.0 84.5 83.6 — BabyVision (with tools) 91.0 92.5 89.6 85.7 — 88.9 94.1 HLE (without tools) 43.4 42.6 39.2 43.5 42.3 49.5 55.6 CharXiv RQ 82.5 — 69.0 84.8 — 85.2 83.7 Evaluation Notes - Automation-Bench v1.0.6: 600 publicly released tasks. - BankerToolBench: OpenCode 1.17.18; MiniMax-M2.7 judge. - GDP.pdf AA: Taken directly from Artificial Analysis. - Terminal Bench 4.0: Codex 0.149.1. - LOCA-Bench 256K: Official implementation. - Agents’ Last Exam: Official Overall setting; full-set results from the official ALE leaderboard only. Blank = no official result (CLI-only excluded), not zero. - OSWorld 2.0: 0808 Full release; highest reported per model at release, except DeepSeek V4.1 (in-house reproduction). - PostTrain Bench v1.1: Claude Code 2.1.251, 512K context; otherwise official setup. - DeepSWE v1.1: mini-SWE-agent 2.4.5, 512K context. - NL2Repo-Bench: Claude Code 2.1.233, 512K context; reward hacking = 0; two inconsistent tasks corrected (scores use the corrected set). - SWE Refactor Bench: Claude Code 2.1.220, 200K context; otherwise official setup. - CyberGym: Claude Code 2.1.263, 1M context. - BabyVision (with tools): Claude Code 2.1.285; min image side 1,500 px. - HLE (without tools): Text-only subset (2,158 questions). - CharXiv RQ: GPT-5.5 judge. Results are for the official weights; third-party quantizations may differ. What’s New in M3.1-Flash M3.1-Flash retains M3’s core mixture-of-experts architecture, with three main changes: - Sparse attention throughout: the first three full-attention layers are replaced with sparse attention. - Quantization-aware training: the routed MoE experts and the attention paths are quantized during training, reducing memory footprint and improving serving efficiency. - DSpark speculative decoding: DSpark replaces the earlier EAGLE-like MTP approach. The release includes the draft head for use with compatible serving implementations. How to Use - MiniMax Agent - MiniMax API Local Deployment Download the model: hf download MiniMaxAI/MiniMax-M3.1-Flash --local-dir MiniMax-M3.1-Flash We recommend the following inference frameworks to serve the model: - SGLang — see SGLang cookbook. - vLLM — see vLLM recipe. - TokenSpeed — see TokenSpeed recipe. Additional integrations in progress: Transformers, KTransformers, unsloth, and ATOM. Inference Settings Recommended sampling settings: temperature=1.0 and top_p=0.95. For reasoning, set reasoning_effort in the chat template: low, medium, high, xhigh, or max. The model supports a context window of up to 1M tokens. Usable context length and throughput depend on GPU memory and the serving configuration. Contact Us Contact us at model@minimax.io.