stepfun

Access 2 stepfun models through the OpenRouter unified API including Step 3.7 Flash and Step 3.5 Flash. Compare pricing, context windows, benchmarks, and capabilities between different stepfun models.

stepfun tokens processed on OpenRouter

StepFun: Step 3.7 FlashStep 3.7 Flash
7.27B tokens
Step 3.7 Flash is StepFun's latest high-efficiency multimodal Mixture-of-Experts model. It pairs a 196B-parameter language backbone with a vision encoder for native image and video understanding, activating roughly 11B parameters per token. The model supports a 256K context window and exposes selectable reasoning levels (high/medium/low), letting callers trade off speed, cost, and depth of reasoning. Designed for coding, agentic workflows, structured outputs, and long-context productivity tasks.
by stepfunMay 28, 2026256K context$0.20/M input tokens$1.15/M output tokens

stepfun

Access 2 stepfun models through the OpenRouter unified API including Step 3.7 Flash and Step 3.5 Flash. Compare pricing, context windows, benchmarks, and capabilities between different stepfun models.

stepfun tokens processed on OpenRouter

StepFun: Step 3.7 FlashStep 3.7 Flash
7.27B tokens
Step 3.7 Flash is StepFun's latest high-efficiency multimodal Mixture-of-Experts model. It pairs a 196B-parameter language backbone with a vision encoder for native image and video understanding, activating roughly 11B parameters per token. The model supports a 256K context window and exposes selectable reasoning levels (high/medium/low), letting callers trade off speed, cost, and depth of reasoning. Designed for coding, agentic workflows, structured outputs, and long-context productivity tasks.
by stepfunMay 28, 2026256K context$0.20/M input tokens$1.15/M output tokens

StepFun: Step 3.5 FlashStep 3.5 Flash

158B tokens

Step 3.5 Flash is StepFun's most capable open-source foundation model. Built on a sparse Mixture of Experts (MoE) architecture, it selectively activates only 11B of its 196B parameters per token. It is a reasoning model that is incredibly speed efficient even at long contexts.

by stepfunJan 29, 2026262K context$0.09/M input tokens$0.30/M output tokens