MiniMax-M3.1-Flash is a native multimodal mixture-of-experts (MoE) model developed by MiniMax for coding, agentic workflows, computer use, and cybersecurity, accepting text and image inputs and producing text output with a context window of up to 1M tokens. Building on MiniMax-M3, it has approximately 428B total parameters with 23B activated per token, and improves task performance and inference efficiency through MiniMax Sparse Attention (MSA) throughout the model, quantization-aware training (QAT) to NVFP4, and DSpark speculative decoding. The model supports five configurable reasoning effort levels (low, medium, high, xhigh, and max).
This model is for research and development only.
This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA MiniMax-M3.1-Flash Model Card
GOVERNING TERMS: The trial service is governed by the NVIDIA API Trial Terms of Service; and the use of this model is governed by the NVIDIA Software and Model Evaluation License. ADDITIONAL INFORMATION: The base model MiniMax M3.1 Flash is governed by the MINIMAX COMMUNITY LICENSE.
Global
Use Case: MiniMax-M3.1-Flash is designed for developers, researchers, and enterprises building agentic applications. Supported use cases include agentic coding and software engineering (repository generation, refactoring, and terminal tasks), computer use and desktop automation, tool-using workflow automation, cybersecurity tasks, long-context reasoning over up to 1M tokens, and multimodal understanding of images, charts, and documents.
Build.NVIDIA.com: 10/22/2026 via link
Huggingface: 10/22/2026 via link
References:
Architecture Type: Transformer
Network Architecture: Mixture-of-Experts
Total Parameters: 428B
Active Parameters: 23B
Base Model: MiniMax-M3
Input Types: Text, Image
Input Formats: String, Red, Green, Blue (RGB)
Input Parameters: One-Dimensional (1D), Two-Dimensional (2D)
Other Input Properties: Supports a context window of up to 1,000,000 tokens; usable context length and throughput depend on GPU memory and serving configuration. Reasoning effort is set via reasoning_effort in the chat template (low, medium, high, xhigh, or max).
Input Context Length (ISL): 1,000,000
Output Types: Text
Output Format: String
Output Parameters: One-Dimensional (1D)
Other Output Properties: Generates reasoning and final-answer text. Includes a DSpark speculative-decoding draft head for use with compatible serving implementations.
Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
Runtime Engines:
vLLM
SGLang
Supported Hardware Microarchitecture Compatibility:
Preferred Operating Systems: Linux
The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
MiniMax-M3.1-Flash v1.0 (October 2026)
Data Modality: Undisclosed
Training Data Collection: Undisclosed
Training Labeling: Undisclosed
Training Properties: Undisclosed
Testing Data Collection: Undisclosed
Testing Labeling: Undisclosed
Testing Properties: Undisclosed
Evaluation Benchmark Score: MiniMax-M3.1-Flash achieves strong results across CoWork, computer use, coding, cybersecurity, and general and multimodal benchmarks, including 76.2 on BankerToolBench, 73.7% on DeepSWE v1.1, 61.7 on OSWorld 2.0, 29.6 on Agents' Last Exam, and 82.2 on CyberGym. Results are for the official NVFP4 weights; third-party quantizations may differ.
| 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 |
Bold scores indicate the highest reported result in each row. A dash indicates an unavailable or inapplicable result, not zero. Results are for the official NVFP4 weights; third-party quantizations may differ.
Evaluation Data Collection: Undisclosed
Evaluation Labeling: Undisclosed
Evaluation Properties: Evaluation Methodology Notes:
Acceleration Engine: vLLM
Test Hardware: GB200, GB300
MiniMax-M3.1-Flash retains MiniMax-M3's core mixture-of-experts architecture, with three main changes:
temperature=1.0, top_p=0.95reasoning_effort in the chat template: low, medium, high, xhigh, or max.NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
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