Step-3.7-Flash is a StepFun vision-language model built on Step 3.5 Flash with additional vision capability for native multimodal, agentic, and coding-related use cases.
Step 3.7 Flash
Description
Step 3.7 Flash is a StepFun vision-language model built on Step 3.5 Flash with additional vision capability for native multimodal, agentic, and coding-related use cases. The model is intended to process text and image inputs and produce text outputs, with emphasis on image understanding, fast throughput, and tool-use workflows.
This model is ready for commercial/non-commercial use.
Third-Party Community Consideration:
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 Step 3.7 Flash Model Card.
License and Terms of Use:
GOVERNING TERMS: The NIM container is governed by the NVIDIA Software License Agreement and the Product-Specific Terms for NVIDIA AI Products; and the use of the model is governed by the NVIDIA Open Model License Agreement.
Additional Information: Apache-2.0.
Deployment Geography:
Global
Use Case:
Use Case: Developers and enterprises can use this model to build multimodal assistants, document and image understanding workflows, long-context analysis systems, visual-language conversational applications, and agentic workflows that combine perception, search, and reasoning.
Release Date:
NGC 05/28/2026 via Step 3.7 Flash model link
Reference(s):
References:
Model Architecture:
Architecture Type: Transformer Network Architecture: Multimodal Mixture-of-Experts vision-language architecture with a 196B-parameter language backbone and a 1.8B-parameter vision encoder. The model has 198B total parameters, activates approximately 11B parameters per token, supports a 256K context window, and supports text and image inputs. Total Parameters: 198B Active Parameters: Approximately 11B Base Model: Step 3.5 Flash
Input:
Input Types: Text, Image Input Formats: Text: String; Image: Red, Green, Blue (RGB) Input Parameters: One Dimensional (1D), Two Dimensional (2D) Other Input Properties: Text prompts, chat messages, image inputs, and interleaved image-and-text inputs. The model supports context length up to 256K tokens.
Output:
Output Types: Text Output Format: String Output Parameters: One Dimensional (1D) Other Output Properties: Generated natural language, structured text, and image-grounded answers.
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.
Software Integration:
Runtime Engines:
- vLLM
Supported Hardware:
- NVIDIA B200
- NVIDIA H100
- NVIDIA H20
- NVIDIA H200
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.
Model Version(s)
Step 3.7 Flash 1.0
Training, Testing, and Evaluation Datasets
Training Dataset
Data Modality: Text and image Text Training Data Size: Undisclosed Image Training Data Size: Undisclosed Data Collection Method by dataset: Undisclosed Labeling Method by dataset: Undisclosed Properties: Undisclosed
Testing Dataset
Data Collection Method by dataset: Undisclosed Labeling Method by dataset: Undisclosed Properties: Undisclosed
Evaluation Dataset
Data Collection Method by dataset: [Hybrid: Automated, Human] Labeling Method by dataset: [Hybrid: Automated, Human] Properties: Evaluation benchmarks include SimpleVQA (Search), V* (Python), ClawEval-1.1, Toolathlon, HLE w. Tool, SWE-PRO, Terminal-Bench v2.1, and GPDVal.
Inference
Engine: vLLM Test Hardware:
- NVIDIA B200
- NVIDIA H100
- NVIDIA H20
- NVIDIA H200
Ethical Considerations
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 developer team to ensure these software components meet requirements for the relevant industry and use case and address unforeseen product misuse.
Please make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included.
Users are responsible for model inputs and outputs. Users are responsible for ensuring safe integration of this model, including implementing guardrails as well as other safety mechanisms, prior to deployment.
Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.
Get Help
Getting started with the NIM
Deploying and integrating the NIM is straightforward thanks to our industry standard APIs. Visit the NIM Container page for release documentation, deployment guides and more.
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