Struct TokenCountsBody 

pub struct TokenCountsBody {
    pub conversation: Option<ConversationParam>,
    pub input: Option<InputParam>,
    pub instructions: Option<String>,
    pub model: Option<String>,
    pub parallel_tool_calls: Option<bool>,
    pub previous_response_id: Option<String>,
    pub reasoning: Option<Reasoning>,
    pub text: Option<ResponseTextParam>,
    pub tool_choice: Option<ToolChoiceParam>,
    pub tools: Option<Vec<Tool>>,
    pub truncation: Option<Truncation>,
}

Fields

conversation: Option<ConversationParam>

The conversation that this response belongs to. Items from this conversation are prepended to input_items for this response request. Input items and output items from this response are automatically added to this conversation after this response completes.

input: Option<InputParam>

Text, image, or file inputs to the model, used to generate a response

instructions: Option<String>

A system (or developer) message inserted into the model’s context.

When used 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.

model: Option<String>

Model 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.

parallel_tool_calls: Option<bool>

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.

reasoning: Option<Reasoning>

gpt-5 and o-series models only Configuration options for reasoning models.

text: Option<ResponseTextParam>

Configuration options for a text response from the model. Can be plain text or structured JSON data. Learn more:

• Text inputs and outputs
• Structured Outputs

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.

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.

Trait Implementations

impl Clone for TokenCountsBody

fn clone(&self) -> TokenCountsBody

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for TokenCountsBody

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for TokenCountsBody

fn default() -> TokenCountsBody

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for TokenCountsBody

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl PartialEq for TokenCountsBody

fn eq(&self, other: &TokenCountsBody) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Serialize for TokenCountsBody

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for TokenCountsBody