Step-3.7-Flash

Step-3.7-Flash is Stepfun AI’s 198B-A13B Mixture-of-Experts vision-language model. It extends the Step-3.5-Flash language architecture with native vision support for image and video understanding, with an emphasis on agentic developer workflows and stable tool calling.

Positioning

Step-3.7-Flash is positioned as a multimodal foundation model for agents and agentic applications. The model targets high-throughput, low-latency inference so developer workflows can use image and video context without relying on text-only requirement descriptions.

Architecture

• Language backbone: derived from Step-3.5-Flash with 45 layers, 288 experts, 8 activated experts per token, and a 256k context length.
• Vision backbone: 1.8B-parameter ViT with 47 layers and 728x728 image inputs.
• Optimization target: trained on Hopper GPUs, with BF16 and FP8 support planned on Day 0 and NVFP4 listed as best effort.

Key Strengths

• Native multimodal input. Designed for image and video understanding on top of a large sparse language backbone.
• Agentic stability. Focused on tool-call stability for agent frameworks and bounded task execution.
• Developer workflow fit. Targets frontend generation from mockups, data-processing tasks, and screenshot-based debugging.
• Fast serving path. Intended for high throughput and fast inference in real-time developer loops.

Available Models

• Step-3.7-Flash — registered as Step3p7ForConditionalGeneration, with the checkpoint-facing alias Step3p6ForConditionalGeneration mapping to the same model class.

Example HF Models

Example Recipes

This documentation-only branch does not add a recipe YAML.

See the Step-3.7-Flash fine-tuning guide for the expected training setup and launch notes.

Agent Frameworks

Step-3.7-Flash continues support for agent integrations such as OpenClaw, HermesAgent, and KiloClaw.

Hugging Face Model Cards

• stepfun-ai/Step-3.7-Flash