MiniMax M3.1 Flash

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

• 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.

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.

Contact Us

Contact us at model@minimax.io.