MiniMaxAI/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

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

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.

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