pub struct Response {Show 29 fields
pub background: Option<bool>,
pub billing: Option<Billing>,
pub conversation: Option<Conversation>,
pub created_at: u64,
pub error: Option<ErrorObject>,
pub id: String,
pub incomplete_details: Option<IncompleteDetails>,
pub instructions: Option<Instructions>,
pub max_output_tokens: Option<u32>,
pub metadata: Option<HashMap<String, String>>,
pub model: String,
pub object: String,
pub output: Vec<OutputItem>,
pub parallel_tool_calls: Option<bool>,
pub previous_response_id: Option<String>,
pub prompt: Option<Prompt>,
pub prompt_cache_key: Option<String>,
pub reasoning: Option<Reasoning>,
pub safety_identifier: Option<String>,
pub service_tier: Option<ServiceTier>,
pub status: Status,
pub temperature: Option<f32>,
pub text: Option<ResponseTextParam>,
pub tool_choice: Option<ToolChoiceParam>,
pub tools: Option<Vec<Tool>>,
pub top_logprobs: Option<u8>,
pub top_p: Option<f32>,
pub truncation: Option<Truncation>,
pub usage: Option<ResponseUsage>,
}Expand description
The complete response returned by the Responses API.
Fields§
§background: Option<bool>Whether to run the model response in the background. Learn more.
billing: Option<Billing>Billing information for the response.
conversation: Option<Conversation>The conversation that this response belongs to. Input items and output items from this response are automatically added to this conversation.
created_at: u64Unix timestamp (in seconds) when this Response was created.
error: Option<ErrorObject>An error object returned when the model fails to generate a Response.
id: StringUnique identifier for this response.
incomplete_details: Option<IncompleteDetails>Details about why the response is incomplete, if any.
instructions: Option<Instructions>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.
max_output_tokens: Option<u32>An upper bound for the number of tokens that can be generated for a response, including visible output tokens and reasoning tokens.
metadata: Option<HashMap<String, String>>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: StringModel ID used to generate the response, like gpt-4o or o3. OpenAI offers a wide range of models with different capabilities, performance characteristics, and price points. Refer to the model guide to browse and compare available models.
object: StringThe object type of this resource - always set to response.
output: Vec<OutputItem>An array of content items generated by the model.
- The length and order of items in the output array is dependent on the model’s response.
- Rather than accessing the first item in the output array and assuming it’s an assistant
message with the content generated by the model, you might consider using
the
output_textproperty where supported in SDKs.
parallel_tool_calls: Option<bool>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.
previous_response_id: Option<String>The unique ID of the previous response to the model. Use this to create multi-turn conversations.
Learn more about conversation state.
Cannot be used in conjunction with conversation.
prompt: Option<Prompt>Reference to a prompt template and its variables. Learn more.
prompt_cache_key: Option<String>Used by OpenAI to cache responses for similar requests to optimize your cache hit rates. Replaces
the user field. Learn more.
reasoning: Option<Reasoning>gpt-5 and o-series models only Configuration options for reasoning models.
safety_identifier: Option<String>A stable identifier used to help detect users of your application that may be violating OpenAI’s usage policies.
The IDs should be a string that uniquely identifies each user. We recommend hashing their username or email address, in order to avoid sending us any identifying information. Learn more.
service_tier: Option<ServiceTier>Specifies the processing type used for serving the request.
- If set to ‘auto’, then the request will be processed with the service tier configured in the Project settings. Unless otherwise configured, the Project will use ‘default’.
- If set to ‘default’, then the request will be processed with the standard pricing and performance for the selected model.
- If set to ‘flex’ or ‘priority’, then the request will be processed with the corresponding service tier.
- When not set, the default behavior is ‘auto’.
When the service_tier parameter is set, the response body will include the service_tier value based on the processing mode actually used to serve the request. This response value may be different from the value set in the parameter.
status: StatusThe status of the response generation.
One of completed, failed, in_progress, cancelled, queued, or incomplete.
temperature: Option<f32>What sampling temperature was used, between 0 and 2. Higher values like 0.8 make outputs more random, lower values like 0.2 make output more focused and deterministic.
We generally recommend altering this or top_p but not both.
text: Option<ResponseTextParam>Configuration options for a text response from the model. Can be plain text or structured JSON data. Learn more:
tool_choice: Option<ToolChoiceParam>How the model should select which tool (or tools) to use when generating
a response. See the tools parameter to see how to specify which tools
the model can call.
tools: Option<Vec<Tool>>An array of tools the model may call while generating a response. You
can specify which tool to use by setting the tool_choice parameter.
We support the following categories of tools:
- Built-in tools: Tools that are provided by OpenAI that extend the model’s capabilities, like web search or file search. Learn more about built-in tools.
- MCP Tools: Integrations with third-party systems via custom MCP servers or predefined connectors such as Google Drive and SharePoint. Learn more about MCP Tools.
- Function calls (custom tools): Functions that are defined by you, enabling the model to call your own code with strongly typed arguments and outputs. Learn more about function calling. You can also use custom tools to call your own code.
top_logprobs: Option<u8>An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability.
top_p: Option<f32>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.
truncation: Option<Truncation>The truncation strategy to use for the model response.
auto: If the input to this Response exceeds the model’s context window size, the model will truncate the response to fit the context window by dropping items from the beginning of the conversation.disabled(default): If the input size will exceed the context window size for a model, the request will fail with a 400 error.
usage: Option<ResponseUsage>Represents token usage details including input tokens, output tokens, a breakdown of output tokens, and the total tokens used.