v1
latestOpenAPI 3.0.02026-07-2380631.2 KBRequest body
Whether to run the model response in the background.
A system (or developer) message inserted into the model's context.
When using along with previous_response_id, the instructions from a previous response will not be carried over to the next response. This makes it simple to swap out system (or developer) messages in new responses.
Specify additional output data to include in the model response. Currently supported values are:
- code_interpreter_call.outputs: Includes the outputs of python code execution in code interpreter tool call items.
- computer_call_output.output.image_url: Include image urls from the computer call output.
- file_search_call.results: Include the search results of the file search tool call.
- message.output_text.logprobs: Include logprobs with assistant messages.
- reasoning.encrypted_content: Includes an encrypted version of reasoning tokens in reasoning item outputs. This enables reasoning items to be used in multi-turn conversations when using the Responses API statelessly (like when the store parameter is set to false, or when an organization is enrolled in the zero data retention program).
An upper bound for the number of tokens that can be generated for a response, including visible output tokens and reasoning tokens.
The unique ID of the previous response to the model. Use this to create multi-turn conversations.
Whether to store the generated model response for later retrieval via API.
If set to true, the model response data will be streamed to the client as it is generated using server-sent events.
The truncation strategy to use for the model response.
- auto: If the context of this response and previous ones exceeds the model's context window size, the model will truncate the response to fit the context window by dropping input items in the middle of the conversation.
- disabled (default): If a model response will exceed the context window size for a model, the request will fail with a 400 error.
Whether to allow the model to run tool calls in parallel.
What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. We generally recommend altering this or top_p but not both.
An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or temperature but not both.
Response
Whether to run the model response in the background.
Unix timestamp (in seconds) of when this Response was created.
Unique identifier for this Response.
An upper bound for the number of tokens that can be generated for a response, including visible output tokens and reasoning tokens.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
Model ID used to generate the response.
The object type of this resource - always set to response.
SDK-only convenience property that contains the aggregated text output from all output_text items in the output array, if any are present. Supported in the Python and JavaScript SDKs.
Whether to allow the model to run tool calls in parallel.
The unique ID of the previous response to the model. Use this to create multi-turn conversations.
Specifies the processing type used for serving the request.
The status of the response generation.
What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. We generally recommend altering this or top_p but not both.
An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.
We generally recommend altering this or temperature but not both.
The truncation strategy to use for the model response.
- auto: If the context of this response and previous ones exceeds the model's context window size, the model will truncate the response to fit the context window by dropping input items in the middle of the conversation.
- disabled (default): If a model response will exceed the context window size for a model, the request will fail with a 400 error.