StepFun: Step 3.5 Flash – API Quickstart

Sample code and API for Step 3.5 Flash

OpenRouter normalizes requests and responses across providers for you.

Get your API key

Create an API key from your OpenRouter dashboard and set it as an environment variable:

Make your first request

Use stepfun/step-3.5-flash with the OpenRouter API:

OpenRouter supports reasoning-enabled models that can show their step-by-step thinking process. Use the reasoning parameter in your request to enable reasoning, and access the reasoning_details array in the response to see the model's internal reasoning before the final answer. When continuing a conversation, preserve the complete reasoning_details when passing messages back to the model so it can continue reasoning from where it left off. Learn more about reasoning tokens.

In the examples below, the OpenRouter-specific headers are optional. Setting them allows your app to appear on the OpenRouter leaderboards.

Using third-party SDKs

For information about using third-party SDKs and frameworks with OpenRouter, please see our frameworks documentation.

Enable streaming

Add "stream": true to your request body to receive responses as server-sent events:

Endpoint

POSThttps://openrouter.ai/api/v1/chat/completions

AuthorizationBearer $OPENROUTER_API_KEY

Content-Typeapplication/json

HTTP-Refereroptional — your site URL, for rankings

X-Titleoptional — your site name, for rankings

Modelstepfun/step-3.5-flash

Parameters

Name	Type	Default	Description
`reasoning`	map	—	Controls reasoning behavior for models that support thinking tokens, including whether reasoning is enabled, the reasoning effort, maximum reasoning tokens, and whether reasoning is excluded from the response.
`include_reasoning`	boolean	—	Deprecated alias for reasoning.exclude.
`max_tokens`	integer	—	This sets the upper limit for the number of tokens the model can generate in response.
`temperature`	float	`1`	This setting influences the variety in the model's responses.
`top_p`	float	`1`	This setting limits the model's choices to a percentage of likely tokens: only the top tokens whose probabilities add up to P.
`stop`	array	—	Stop generation immediately if the model encounter any token specified in the stop array.
`frequency_penalty`	float	`0`	This setting aims to control the repetition of tokens based on how often they appear in the input.
`presence_penalty`	float	`0`	Adjusts how often the model repeats specific tokens already used in the input.
`repetition_penalty`	float	`1`	Helps to reduce the repetition of tokens from the input.
`top_k`	integer	`0`	This limits the model's choice of tokens at each step, making it choose from a smaller set.
`seed`	integer	—	If specified, the inferencing will sample deterministically, such that repeated requests with the same seed and parameters should return the same result.
`min_p`	float	`0`	Represents the minimum probability for a token to be considered, relative to the probability of the most likely token.
`response_format`	map	—	Forces the model to produce specific output format.
`logit_bias`	map	—	Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100.
`tools`	array	—	Tool calling parameter, following OpenAI's tool calling request shape.
`tool_choice`	string or object	—	Controls which (if any) tool is called by the model.