lysandre/MiniMax-M3.1-Flash
image-text-to-text model by lysandre
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From the publisher
Model card & documentation
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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: 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…
Read the full model card ↗ · Preview checked 2026-10-27T20:43:46.869Z
Inside the original model card — Document outline
- Model at a Glance
- Benchmarks
- What’s New in M3.1-Flash
- How to Use
- Local Deployment
- Multi-Chip Deployment
- Inference Settings
- Contact Us
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Model overview
lysandre/MiniMax-M3.1-Flash is a image-text-to-text model repository published by lysandre on Hugging Face. The source reports the transformers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- image-text-to-text
- Library
- transformers
- Recent downloads (30 days)
- 0
- Cumulative likes
- 0
- Hugging Face trending score
- 0
- Reported safetensors parameters
- 220,490,854,016
- Architecture
- MiniMaxM3SparseForConditionalGeneration
- License
- other
- Access
- Not gated by Hugging Face
- Created
- 2026-10-23T06:20:53.000Z
- Last modified
- 2026-10-23T06:26:31.000Z
Compatibility and lineage
Language coverage was not reported.
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-10-23T06:46:00.441Z. Daily imports are snapshots, not real-time monitoring.
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