MiniMaxAI/MiniMax-M3.1-Flash

类型 image-text-to-text · 框架 transformers · 下载 11,075 · 收藏 422

♥ 422来源：HuggingFace Models收录于 2026年10月28日

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中文 MiniMax-M3.1-Flash 是原生多模态 MoE 模型，面向编程、Agent 工作流、computer use 与安全场景，支持文本、图像、视频输入，上下文窗口达 100 万 token。它解决长上下文与多模态任务下的推理效率问题：全模型采用稀疏注意力，配合量化感知训练和 DSpark 投机解码。总参数约 428B，每 token 激活 23B，输出为文本。适合需要长文档、视频理解与 Agent 编排的开发者。

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pipelinetag: image-text-to-text license: other licensename: minimax-community licenselink: LICENSE libraryname: transformers tags: - multimodal - moe - agent - coding - video ---

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

<div style="overflow-x:auto;margin:16px 0"> <table style="width:100%;border-collapse:collapse;font-size:14px"> <thead> <tr> <th style="padding:10px 12px;text-align:left;vertical-align:middle;border-bottom:1px solid #e5e7eb;font-weight:600;border-bottom:2px solid #f25f5c;background:#f7f7f8;">Benchmark</th> <th style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;font-weight:600;border-bottom:2px solid #f25f5c;background:rgba(242,95,92,0.08);">MiniMax M3.1 Flash</th> <th style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;font-weight:600;border-bottom:2px solid #f25f5c;background:#f7f7f8;">DeepSeek V4.1 Flash-1017</th> <th style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;font-weight:600;border-bottom:2px solid #f25f5c;background:#f7f7f8;">DeepSeek V4.1 Flash</th> <th style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;font-weight:600;border-bottom:2px solid #f25f5c;background:#f7f7f8;">Kimi K3</th> <th style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;font-weight:600;border-bottom:2px solid #f25f5c;background:#f7f7f8;">GLM-5.3</th> <th style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;font-weight:600;border-bottom:2px solid #f25f5c;background:#f7f7f8;">GPT-5.6 Sol</th> <th style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;font-weight:600;border-bottom:2px solid #f25f5c;background:#f7f7f8;">Opus 5</th> </tr> </thead> <tbody> <tr> <td style="padding:10px 12px;text-align:left;vertical-align:middle;border-bottom:1px solid #e5e7eb;"><div>Automation-Bench v1.0.6</div></td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;background:rgba(242,95,92,0.08);"><strong>62.8</strong></td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">60.5</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">54.8</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">46.7</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">48.8</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">45.8</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">50.3</td> </tr> <tr> <td style="padding:10px 12px;text-align:left;vertical-align:middle;border-bottom:1px solid #e5e7eb;">BankerToolBench</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;background:rgba(242,95,92,0.08);"><strong>76.2</strong></td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">—</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">73.7</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">69.1</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">74.5</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">74.6</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;"><strong>76.2</strong></td> </tr> <tr> <td style="padding:10px 12px;text-align:left;vertical-align:middle;border-bottom:1px solid #e5e7eb;">GDP.pdf AA</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;background:rgba(242,95,92,0.08);">20.8</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">—</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">12.8</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">22.0</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">11.2</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;"><strong>27.2</strong></td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">21.6</td> </tr> <tr> <td style="padding:10px 12px;text-align:left;vertical-align:middle;border-bottom:1px solid #e5e7eb;">Terminal Bench 4.0</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;background:rgba(242,95,92,0.08);">38.1</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">42.3</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">31.2</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">12.6</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">37.9</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">39.9</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;"><strong>51.8</strong></td> </tr> <tr> <td style="padding:10px 12px;text-align:left;vertical-align:middle;border-bottom:1px solid #e5e7eb;">LOCA-Bench 256K</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;background:rgba(242,95,92,0.08);"><strong>60.3</strong></td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">—</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">—</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">49.7</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">50.3</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">60.0</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">53.7</td> </tr> <tr> <td style="padding:10px 12px;text-align:left;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;"><div>Agents’ Last Exam</div></td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;background:rgba(242,95,92,0.08);">29.6</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;">—</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;">—</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;">28.3</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;">—</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;">30.6</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;"><strong>32.2</strong></td> </tr> <tr> <td style="padding:10px 12px;text-align:left;vertical-align:middle;border-bottom:1px solid #e5e7eb;">OSWorld 2.0</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;background:rgba(242,95,92,0.08);">61.7</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">—</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">54.3</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">58.3</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">—</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">65.7</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;"><strong>70.6</strong></td> </tr> <tr> <td style="padding:10px 12px;text-align:left;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;"><div>PostTrain Bench v1.1</div></td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;background:rgba(242,95,92,0.08);">39.3</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;">—</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;">30.5</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;">36.6</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;">37.5</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;">36.2</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;"><strong>40.4</strong></td> </tr> <tr> <td style="padding:10px 12px;text-align:left;vertical-align:middle;border-bottom:1px solid #e5e7eb;">DeepSWE v1.1</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;background:rgba(242,95,92,0.08);">73.7</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;"><strong>77.0</strong></td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">74.2</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">67.5</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">66.9</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">73.0</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">74.0</td> </tr> <tr> <td style="padding:10px 12px;text-align:left;vertical-align:middle;border-bottom:1px solid #e5e7eb;">NL2Repo-Bench</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;background:rgba(242,95,92,0.08);">61.2</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">68.2</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">65.4</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">58.0</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">58.0</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">56.8</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;"><strong>75.3</strong></td> </tr> <tr> <td style="padding:10px 12px;text-align:left;vertical-align:middle;border-bottom:1px solid #e5e7eb;">SWE Refactor Bench</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;background:rgba(242,95,92,0.08);">23.0</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">—</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">11.8</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">19.5</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">10.4</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">28.5</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;"><strong>31.0</strong></td> </tr> <tr> <td style="padding:10px 12px;text-align:left;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;"><div>CyberGym</div></td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;background:rgba(242,95,92,0.08);">82.2</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;">—</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;">—</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;">80.0</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;"><strong>84.5</strong></td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;">83.6</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;">—</td> </tr> <tr> <td style="padding:10px 12px;text-align:left;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;"><div>BabyVision (with tools)</div></td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;background:rgba(242,95,92,0.08);">91.0</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;">92.5</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;">89.6</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;">85.7</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;">—</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;">88.9</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;border-top:2px solid #f1d6d3;"><strong>94.1</strong></td> </tr> <tr> <td style="padding:10px 12px;text-align:left;vertical-align:middle;border-bottom:1px solid #e5e7eb;">HLE (without tools)</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;background:rgba(242,95,92,0.08);">43.4</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">42.6</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">39.2</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">43.5</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">42.3</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">49.5</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;"><strong>55.6</strong></td> </tr> <tr> <td style="padding:10px 12px;text-align:left;vertical-align:middle;border-bottom:1px solid #e5e7eb;">CharXiv RQ</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;background:rgba(242,95,92,0.08);">82.5</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">—</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">69.0</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">84.8</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">—</td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;"><strong>85.2</strong></td> <td style="padding:10px 12px;text-align:center;vertical-align:middle;border-bottom:1px solid #e5e7eb;">83.7</td> </tr> </tbody> </table> </div>

<details> <summary><strong>Evaluation Notes</strong></summary>

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.

</details>

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

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:

Additional integrations in progress: Transformers, KTransformers, unsloth, and ATOM.

Multi-Chip Deployment

FlagOS — see Multi-Chip Support and Downloads.

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.