Cancel eval run

POST/evals/{eval_id}/runs/{run_id}

Cancel an ongoing evaluation run.

Path ParametersExpand Collapse

eval_id: string

run_id: string

ReturnsExpand Collapse

id: string

Unique identifier for the evaluation run.

created_at: number

Unix timestamp (in seconds) when the evaluation run was created.

data_source: CreateEvalJSONLRunDataSource { source, type } or CreateEvalCompletionsRunDataSource { source, type, input_messages, 2 more } or object { source, type, input_messages, 2 more }

Information about the run's data source.

Accepts one of the following:

CreateEvalJSONLRunDataSource = object { source, type }

A JsonlRunDataSource object with that specifies a JSONL file that matches the eval

source: object { content, type } or object { id, type }

Determines what populates the item namespace in the data source.

Accepts one of the following:

EvalJSONLFileContentSource = object { content, type }

content: array of object { item, sample }

The content of the jsonl file.

item: map[unknown]

sample: optional map[unknown]

type: "file_content"

The type of jsonl source. Always file_content.

EvalJSONLFileIDSource = object { id, type }

id: string

The identifier of the file.

type: "file_id"

The type of jsonl source. Always file_id.

type: "jsonl"

The type of data source. Always jsonl.

CreateEvalCompletionsRunDataSource = object { source, type, input_messages, 2 more }

A CompletionsRunDataSource object describing a model sampling configuration.

source: object { content, type } or object { id, type } or object { type, created_after, created_before, 3 more }

Determines what populates the item namespace in this run's data source.

Accepts one of the following:

EvalJSONLFileContentSource = object { content, type }

content: array of object { item, sample }

The content of the jsonl file.

item: map[unknown]

sample: optional map[unknown]

type: "file_content"

The type of jsonl source. Always file_content.

EvalJSONLFileIDSource = object { id, type }

id: string

The identifier of the file.

type: "file_id"

The type of jsonl source. Always file_id.

StoredCompletionsRunDataSource = object { type, created_after, created_before, 3 more }

A StoredCompletionsRunDataSource configuration describing a set of filters

type: "stored_completions"

The type of source. Always stored_completions.

created_after: optional number

An optional Unix timestamp to filter items created after this time.

created_before: optional number

An optional Unix timestamp to filter items created before this time.

limit: optional number

An optional maximum number of items to return.

metadata: optional Metadata

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: optional string

An optional model to filter by (e.g., 'gpt-4o').

type: "completions"

The type of run data source. Always completions.

input_messages: optional object { template, type } or object { item_reference, type }

Used when sampling from a model. Dictates the structure of the messages passed into the model. Can either be a reference to a prebuilt trajectory (ie, item.input_trajectory), or a template with variable references to the item namespace.

Accepts one of the following:

TemplateInputMessages = object { template, type }

template: array of EasyInputMessage { content, role, phase, type } or object { content, role, type }

A list of chat messages forming the prompt or context. May include variable references to the item namespace, ie {{item.name}}.

Accepts one of the following:

EasyInputMessage = object { content, role, phase, type }

A message input to the model with a role indicating instruction following hierarchy. Instructions given with the developer or system role take precedence over instructions given with the user role. Messages with the assistant role are presumed to have been generated by the model in previous interactions.

content: string or ResponseInputMessageContentList { , , }

Text, image, or audio input to the model, used to generate a response. Can also contain previous assistant responses.

Accepts one of the following:

TextInput = string

A text input to the model.

ResponseInputMessageContentList = array of ResponseInputContent

A list of one or many input items to the model, containing different content types.

Accepts one of the following:

ResponseInputText = object { text, type }

A text input to the model.

text: string

The text input to the model.

type: "input_text"

The type of the input item. Always input_text.

ResponseInputImage = object { detail, type, file_id, image_url }

An image input to the model. Learn about image inputs.

detail: "low" or "high" or "auto"

The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

Accepts one of the following:

"low"

"high"

"auto"

type: "input_image"

The type of the input item. Always input_image.

file_id: optional string

The ID of the file to be sent to the model.

image_url: optional string

The URL of the image to be sent to the model. A fully qualified URL or base64 encoded image in a data URL.

ResponseInputFile = object { type, file_data, file_id, 2 more }

A file input to the model.

type: "input_file"

The type of the input item. Always input_file.

file_data: optional string

The content of the file to be sent to the model.

file_id: optional string

The ID of the file to be sent to the model.

file_url: optional string

The URL of the file to be sent to the model.

filename: optional string

The name of the file to be sent to the model.

role: "user" or "assistant" or "system" or "developer"

The role of the message input. One of user, assistant, system, or developer.

Accepts one of the following:

"user"

"assistant"

"system"

"developer"

phase: optional "commentary" or "final_answer"

Labels an assistant message as intermediate commentary (commentary) or the final answer (final_answer). For models like gpt-5.3-codex and beyond, when sending follow-up requests, preserve and resend phase on all assistant messages — dropping it can degrade performance. Not used for user messages.

Use commentary for an intermediate assistant message and final_answer for the final assistant message. For follow-up requests with models like gpt-5.3-codex and later, preserve and resend phase on all assistant messages. Omitting it can degrade performance. Not used for user messages.

Accepts one of the following:

"commentary"

"final_answer"

type: optional "message"

The type of the message input. Always message.

EvalMessageObject = object { content, role, type }

content: string or ResponseInputText { text, type } or object { text, type } or 3 more

Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

Accepts one of the following:

TextInput = string

A text input to the model.

ResponseInputText = object { text, type }

A text input to the model.

text: string

The text input to the model.

type: "input_text"

The type of the input item. Always input_text.

OutputText = object { text, type }

A text output from the model.

text: string

The text output from the model.

type: "output_text"

The type of the output text. Always output_text.

InputImage = object { image_url, type, detail }

An image input block used within EvalItem content arrays.

image_url: string

The URL of the image input.

type: "input_image"

The type of the image input. Always input_image.

detail: optional string

The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

ResponseInputAudio = object { input_audio, type }

An audio input to the model.

input_audio: object { data, format }

data: string

Base64-encoded audio data.

format: "mp3" or "wav"

The format of the audio data. Currently supported formats are mp3 and wav.

Accepts one of the following:

"mp3"

"wav"

type: "input_audio"

The type of the input item. Always input_audio.

GraderInputs = array of string or ResponseInputText { text, type } or object { text, type } or 2 more

A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

Accepts one of the following:

TextInput = string

A text input to the model.

ResponseInputText = object { text, type }

A text input to the model.

text: string

The text input to the model.

type: "input_text"

The type of the input item. Always input_text.

OutputText = object { text, type }

A text output from the model.

text: string

The text output from the model.

type: "output_text"

The type of the output text. Always output_text.

InputImage = object { image_url, type, detail }

An image input block used within EvalItem content arrays.

image_url: string

The URL of the image input.

type: "input_image"

The type of the image input. Always input_image.

detail: optional string

The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

ResponseInputAudio = object { input_audio, type }

An audio input to the model.

input_audio: object { data, format }

data: string

Base64-encoded audio data.

format: "mp3" or "wav"

The format of the audio data. Currently supported formats are mp3 and wav.

Accepts one of the following:

"mp3"

"wav"

type: "input_audio"

The type of the input item. Always input_audio.

role: "user" or "assistant" or "system" or "developer"

The role of the message input. One of user, assistant, system, or developer.

Accepts one of the following:

"user"

"assistant"

"system"

"developer"

type: optional "message"

The type of the message input. Always message.

type: "template"

The type of input messages. Always template.

ItemReferenceInputMessages = object { item_reference, type }

item_reference: string

A reference to a variable in the item namespace. Ie, "item.input_trajectory"

type: "item_reference"

The type of input messages. Always item_reference.

model: optional string

The name of the model to use for generating completions (e.g. "o3-mini").

sampling_params: optional object { max_completion_tokens, reasoning_effort, response_format, 4 more }

max_completion_tokens: optional number

The maximum number of tokens in the generated output.

reasoning_effort: optional ReasoningEffort

Constrains effort on reasoning for reasoning models. Currently supported values are none, minimal, low, medium, high, and xhigh. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response.

gpt-5.1 defaults to none, which does not perform reasoning. The supported reasoning values for gpt-5.1 are none, low, medium, and high. Tool calls are supported for all reasoning values in gpt-5.1.
All models before gpt-5.1 default to medium reasoning effort, and do not support none.
The gpt-5-pro model defaults to (and only supports) high reasoning effort.
xhigh is supported for all models after gpt-5.1-codex-max.

Accepts one of the following:

"none"

"minimal"

"low"

"medium"

"high"

"xhigh"

response_format: optional ResponseFormatText { type } or ResponseFormatJSONSchema { json_schema, type } or ResponseFormatJSONObject { type }

An object specifying the format that the model must output.

Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.

Setting to { "type": "json_object" } enables the older JSON mode, which ensures the message the model generates is valid JSON. Using json_schema is preferred for models that support it.

Accepts one of the following:

ResponseFormatText = object { type }

Default response format. Used to generate text responses.

type: "text"

The type of response format being defined. Always text.

ResponseFormatJSONSchema = object { json_schema, type }

JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.

json_schema: object { name, description, schema, strict }

Structured Outputs configuration options, including a JSON Schema.

The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.

description: optional string

A description of what the response format is for, used by the model to determine how to respond in the format.

schema: optional map[unknown]

The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.

strict: optional boolean

Whether to enable strict schema adherence when generating the output. If set to true, the model will always follow the exact schema defined in the schema field. Only a subset of JSON Schema is supported when strict is true. To learn more, read the Structured Outputs guide.

type: "json_schema"

The type of response format being defined. Always json_schema.

ResponseFormatJSONObject = object { type }

JSON object response format. An older method of generating JSON responses. Using json_schema is recommended for models that support it. Note that the model will not generate JSON without a system or user message instructing it to do so.

type: "json_object"

The type of response format being defined. Always json_object.

seed: optional number

A seed value to initialize the randomness, during sampling.

temperature: optional number

A higher temperature increases randomness in the outputs.

tools: optional array of ChatCompletionFunctionTool { function, type }

A list of tools the model may call. Currently, only functions are supported as a tool. Use this to provide a list of functions the model may generate JSON inputs for. A max of 128 functions are supported.

function: FunctionDefinition { name, description, parameters, strict }

The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.

description: optional string

A description of what the function does, used by the model to choose when and how to call the function.

parameters: optional FunctionParameters

The parameters the functions accepts, described as a JSON Schema object. See the guide for examples, and the JSON Schema reference for documentation about the format.

Omitting parameters defines a function with an empty parameter list.

strict: optional boolean

Whether to enable strict schema adherence when generating the function call. If set to true, the model will follow the exact schema defined in the parameters field. Only a subset of JSON Schema is supported when strict is true. Learn more about Structured Outputs in the function calling guide.

type: "function"

The type of the tool. Currently, only function is supported.

top_p: optional number

An alternative to temperature for nucleus sampling; 1.0 includes all tokens.

ResponsesRunDataSource = object { source, type, input_messages, 2 more }

A ResponsesRunDataSource object describing a model sampling configuration.

source: object { content, type } or object { id, type } or object { type, created_after, created_before, 8 more }

Determines what populates the item namespace in this run's data source.

Accepts one of the following:

EvalJSONLFileContentSource = object { content, type }

content: array of object { item, sample }

The content of the jsonl file.

item: map[unknown]

sample: optional map[unknown]

type: "file_content"

The type of jsonl source. Always file_content.

EvalJSONLFileIDSource = object { id, type }

id: string

The identifier of the file.

type: "file_id"

The type of jsonl source. Always file_id.

EvalResponsesSource = object { type, created_after, created_before, 8 more }

A EvalResponsesSource object describing a run data source configuration.

type: "responses"

The type of run data source. Always responses.

created_after: optional number

Only include items created after this timestamp (inclusive). This is a query parameter used to select responses.

minimum0

created_before: optional number

Only include items created before this timestamp (inclusive). This is a query parameter used to select responses.

minimum0

instructions_search: optional string

Optional string to search the 'instructions' field. This is a query parameter used to select responses.

metadata: optional unknown

Metadata filter for the responses. This is a query parameter used to select responses.

model: optional string

The name of the model to find responses for. This is a query parameter used to select responses.

reasoning_effort: optional ReasoningEffort

gpt-5.1 defaults to none, which does not perform reasoning. The supported reasoning values for gpt-5.1 are none, low, medium, and high. Tool calls are supported for all reasoning values in gpt-5.1.
All models before gpt-5.1 default to medium reasoning effort, and do not support none.
The gpt-5-pro model defaults to (and only supports) high reasoning effort.
xhigh is supported for all models after gpt-5.1-codex-max.

Accepts one of the following:

"none"

"minimal"

"low"

"medium"

"high"

"xhigh"

temperature: optional number

Sampling temperature. This is a query parameter used to select responses.

tools: optional array of string

List of tool names. This is a query parameter used to select responses.

top_p: optional number

Nucleus sampling parameter. This is a query parameter used to select responses.

users: optional array of string

List of user identifiers. This is a query parameter used to select responses.

type: "responses"

The type of run data source. Always responses.

input_messages: optional object { template, type } or object { item_reference, type }

Accepts one of the following:

InputMessagesTemplate = object { template, type }

A list of chat messages forming the prompt or context. May include variable references to the item namespace, ie {{item.name}}.

Accepts one of the following:

ChatMessage = object { content, role }

content: string

The content of the message.

role: string

The role of the message (e.g. "system", "assistant", "user").

EvalMessageObject = object { content, role, type }

content: string or ResponseInputText { text, type } or object { text, type } or 3 more

Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

Accepts one of the following:

TextInput = string

A text input to the model.

ResponseInputText = object { text, type }

A text input to the model.

text: string

The text input to the model.

type: "input_text"

The type of the input item. Always input_text.

OutputText = object { text, type }

A text output from the model.

text: string

The text output from the model.

type: "output_text"

The type of the output text. Always output_text.

InputImage = object { image_url, type, detail }

An image input block used within EvalItem content arrays.

image_url: string

The URL of the image input.

type: "input_image"

The type of the image input. Always input_image.

detail: optional string

The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

ResponseInputAudio = object { input_audio, type }

An audio input to the model.

input_audio: object { data, format }

data: string

Base64-encoded audio data.

format: "mp3" or "wav"

The format of the audio data. Currently supported formats are mp3 and wav.

Accepts one of the following:

"mp3"

"wav"

type: "input_audio"

The type of the input item. Always input_audio.

GraderInputs = array of string or ResponseInputText { text, type } or object { text, type } or 2 more

A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

Accepts one of the following:

TextInput = string

A text input to the model.

ResponseInputText = object { text, type }

A text input to the model.

text: string

The text input to the model.

type: "input_text"

The type of the input item. Always input_text.

OutputText = object { text, type }

A text output from the model.

text: string

The text output from the model.

type: "output_text"

The type of the output text. Always output_text.

InputImage = object { image_url, type, detail }

An image input block used within EvalItem content arrays.

image_url: string

The URL of the image input.

type: "input_image"

The type of the image input. Always input_image.

detail: optional string

The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

ResponseInputAudio = object { input_audio, type }

An audio input to the model.

input_audio: object { data, format }

data: string

Base64-encoded audio data.

format: "mp3" or "wav"

The format of the audio data. Currently supported formats are mp3 and wav.

Accepts one of the following:

"mp3"

"wav"

type: "input_audio"

The type of the input item. Always input_audio.

role: "user" or "assistant" or "system" or "developer"

The role of the message input. One of user, assistant, system, or developer.

Accepts one of the following:

"user"

"assistant"

"system"

"developer"

type: optional "message"

The type of the message input. Always message.

type: "template"

The type of input messages. Always template.

InputMessagesItemReference = object { item_reference, type }

item_reference: string

A reference to a variable in the item namespace. Ie, "item.name"

type: "item_reference"

The type of input messages. Always item_reference.

model: optional string

The name of the model to use for generating completions (e.g. "o3-mini").

sampling_params: optional object { max_completion_tokens, reasoning_effort, seed, 4 more }

max_completion_tokens: optional number

The maximum number of tokens in the generated output.

reasoning_effort: optional ReasoningEffort

gpt-5.1 defaults to none, which does not perform reasoning. The supported reasoning values for gpt-5.1 are none, low, medium, and high. Tool calls are supported for all reasoning values in gpt-5.1.
All models before gpt-5.1 default to medium reasoning effort, and do not support none.
The gpt-5-pro model defaults to (and only supports) high reasoning effort.
xhigh is supported for all models after gpt-5.1-codex-max.

Accepts one of the following:

"none"

"minimal"

"low"

"medium"

"high"

"xhigh"

seed: optional number

A seed value to initialize the randomness, during sampling.

temperature: optional number

A higher temperature increases randomness in the outputs.

text: optional object { format }

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

format: optional ResponseFormatTextConfig

An object specifying the format that the model must output.

Configuring { "type": "json_schema" } enables Structured Outputs, which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.

The default format is { "type": "text" } with no additional options.

Not recommended for gpt-4o and newer models:

Setting to { "type": "json_object" } enables the older JSON mode, which ensures the message the model generates is valid JSON. Using json_schema is preferred for models that support it.

Accepts one of the following:

ResponseFormatText = object { type }

Default response format. Used to generate text responses.

type: "text"

The type of response format being defined. Always text.

ResponseFormatTextJSONSchemaConfig = object { name, schema, type, 2 more }

JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.

The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.

schema: map[unknown]

The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.

type: "json_schema"

The type of response format being defined. Always json_schema.

description: optional string

A description of what the response format is for, used by the model to determine how to respond in the format.

strict: optional boolean

ResponseFormatJSONObject = object { type }

type: "json_object"

The type of response format being defined. Always json_object.

tools: optional array of 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.

The two categories of tools you can provide the model are:

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.
Function calls (custom tools): Functions that are defined by you, enabling the model to call your own code. Learn more about function calling.

Accepts one of the following:

FunctionTool = object { name, parameters, strict, 2 more }

Defines a function in your own code the model can choose to call. Learn more about function calling.

The name of the function to call.

parameters: map[unknown]

A JSON schema object describing the parameters of the function.

strict: boolean

Whether to enforce strict parameter validation. Default true.

type: "function"

The type of the function tool. Always function.

description: optional string

A description of the function. Used by the model to determine whether or not to call the function.

FileSearchTool = object { type, vector_store_ids, filters, 2 more }

A tool that searches for relevant content from uploaded files. Learn more about the file search tool.

type: "file_search"

The type of the file search tool. Always file_search.

vector_store_ids: array of string

The IDs of the vector stores to search.

filters: optional ComparisonFilter { key, type, value } or CompoundFilter { filters, type }

A filter to apply.

Accepts one of the following:

ComparisonFilter = object { key, type, value }

A filter used to compare a specified attribute key to a given value using a defined comparison operation.

key: string

The key to compare against the value.

type: "eq" or "ne" or "gt" or 3 more

Specifies the comparison operator: eq, ne, gt, gte, lt, lte, in, nin.

eq: equals
ne: not equal
gt: greater than
gte: greater than or equal
lt: less than
lte: less than or equal
in: in
nin: not in

Accepts one of the following:

"eq"

"ne"

"gt"

"gte"

"lt"

"lte"

value: string or number or boolean or array of string or number

The value to compare against the attribute key; supports string, number, or boolean types.

Accepts one of the following:

UnionMember0 = string

UnionMember1 = number

UnionMember2 = boolean

UnionMember3 = array of string or number

Accepts one of the following:

UnionMember0 = string

UnionMember1 = number

CompoundFilter = object { filters, type }

Combine multiple filters using and or or.

filters: array of ComparisonFilter { key, type, value } or unknown

Array of filters to combine. Items can be ComparisonFilter or CompoundFilter.

Accepts one of the following:

ComparisonFilter = object { key, type, value }

A filter used to compare a specified attribute key to a given value using a defined comparison operation.

key: string

The key to compare against the value.

type: "eq" or "ne" or "gt" or 3 more

Specifies the comparison operator: eq, ne, gt, gte, lt, lte, in, nin.

eq: equals
ne: not equal
gt: greater than
gte: greater than or equal
lt: less than
lte: less than or equal
in: in
nin: not in

Accepts one of the following:

"eq"

"ne"

"gt"

"gte"

"lt"

"lte"

value: string or number or boolean or array of string or number

The value to compare against the attribute key; supports string, number, or boolean types.

Accepts one of the following:

UnionMember0 = string

UnionMember1 = number

UnionMember2 = boolean

UnionMember3 = array of string or number

Accepts one of the following:

UnionMember0 = string

UnionMember1 = number

UnionMember1 = unknown

type: "and" or "or"

Type of operation: and or or.

Accepts one of the following:

"and"

"or"

max_num_results: optional number

The maximum number of results to return. This number should be between 1 and 50 inclusive.

ranking_options: optional object { hybrid_search, ranker, score_threshold }

Ranking options for search.

hybrid_search: optional object { embedding_weight, text_weight }

Weights that control how reciprocal rank fusion balances semantic embedding matches versus sparse keyword matches when hybrid search is enabled.

embedding_weight: number

The weight of the embedding in the reciprocal ranking fusion.

text_weight: number

The weight of the text in the reciprocal ranking fusion.

ranker: optional "auto" or "default-2024-11-15"

The ranker to use for the file search.

Accepts one of the following:

"auto"

"default-2024-11-15"

score_threshold: optional number

The score threshold for the file search, a number between 0 and 1. Numbers closer to 1 will attempt to return only the most relevant results, but may return fewer results.

ComputerTool = object { display_height, display_width, environment, type }

A tool that controls a virtual computer. Learn more about the computer tool.

display_height: number

The height of the computer display.

display_width: number

The width of the computer display.

environment: "windows" or "mac" or "linux" or 2 more

The type of computer environment to control.

Accepts one of the following:

"windows"

"mac"

"linux"

"ubuntu"

"browser"

type: "computer_use_preview"

The type of the computer use tool. Always computer_use_preview.

WebSearchTool = object { type, filters, search_context_size, user_location }

Search the Internet for sources related to the prompt. Learn more about the web search tool.

type: "web_search" or "web_search_2025_08_26"

The type of the web search tool. One of web_search or web_search_2025_08_26.

Accepts one of the following:

"web_search"

"web_search_2025_08_26"

filters: optional object { allowed_domains }

Filters for the search.

allowed_domains: optional array of string

Allowed domains for the search. If not provided, all domains are allowed. Subdomains of the provided domains are allowed as well.

Example: ["pubmed.ncbi.nlm.nih.gov"]

search_context_size: optional "low" or "medium" or "high"

High level guidance for the amount of context window space to use for the search. One of low, medium, or high. medium is the default.

Accepts one of the following:

"low"

"medium"

"high"

user_location: optional object { city, country, region, 2 more }

The approximate location of the user.

city: optional string

Free text input for the city of the user, e.g. San Francisco.

country: optional string

The two-letter ISO country code of the user, e.g. US.

region: optional string

Free text input for the region of the user, e.g. California.

timezone: optional string

The IANA timezone of the user, e.g. America/Los_Angeles.

type: optional "approximate"

The type of location approximation. Always approximate.

Mcp = object { server_label, type, allowed_tools, 6 more }

Give the model access to additional tools via remote Model Context Protocol (MCP) servers. Learn more about MCP.

server_label: string

A label for this MCP server, used to identify it in tool calls.

type: "mcp"

The type of the MCP tool. Always mcp.

allowed_tools: optional array of string or object { read_only, tool_names }

List of allowed tool names or a filter object.

Accepts one of the following:

McpAllowedTools = array of string

A string array of allowed tool names

McpToolFilter = object { read_only, tool_names }

A filter object to specify which tools are allowed.

read_only: optional boolean

Indicates whether or not a tool modifies data or is read-only. If an MCP server is annotated with readOnlyHint, it will match this filter.

tool_names: optional array of string

List of allowed tool names.

authorization: optional string

An OAuth access token that can be used with a remote MCP server, either with a custom MCP server URL or a service connector. Your application must handle the OAuth authorization flow and provide the token here.

connector_id: optional "connector_dropbox" or "connector_gmail" or "connector_googlecalendar" or 5 more

Identifier for service connectors, like those available in ChatGPT. One of server_url or connector_id must be provided. Learn more about service connectors here.

Currently supported connector_id values are:

Dropbox: connector_dropbox
Gmail: connector_gmail
Google Calendar: connector_googlecalendar
Google Drive: connector_googledrive
Microsoft Teams: connector_microsoftteams
Outlook Calendar: connector_outlookcalendar
Outlook Email: connector_outlookemail
SharePoint: connector_sharepoint

Accepts one of the following:

"connector_dropbox"

"connector_gmail"

"connector_googlecalendar"

"connector_googledrive"

"connector_microsoftteams"

"connector_outlookcalendar"

"connector_outlookemail"

"connector_sharepoint"

headers: optional map[string]

Optional HTTP headers to send to the MCP server. Use for authentication or other purposes.

require_approval: optional object { always, never } or "always" or "never"

Specify which of the MCP server's tools require approval.

Accepts one of the following:

McpToolApprovalFilter = object { always, never }

Specify which of the MCP server's tools require approval. Can be always, never, or a filter object associated with tools that require approval.

always: optional object { read_only, tool_names }

A filter object to specify which tools are allowed.

read_only: optional boolean

Indicates whether or not a tool modifies data or is read-only. If an MCP server is annotated with readOnlyHint, it will match this filter.

tool_names: optional array of string

List of allowed tool names.

never: optional object { read_only, tool_names }

A filter object to specify which tools are allowed.

read_only: optional boolean

Indicates whether or not a tool modifies data or is read-only. If an MCP server is annotated with readOnlyHint, it will match this filter.

tool_names: optional array of string

List of allowed tool names.

McpToolApprovalSetting = "always" or "never"

Specify a single approval policy for all tools. One of always or never. When set to always, all tools will require approval. When set to never, all tools will not require approval.

Accepts one of the following:

"always"

"never"

server_description: optional string

Optional description of the MCP server, used to provide more context.

server_url: optional string

The URL for the MCP server. One of server_url or connector_id must be provided.

CodeInterpreter = object { container, type }

A tool that runs Python code to help generate a response to a prompt.

container: string or object { type, file_ids, memory_limit, network_policy }

The code interpreter container. Can be a container ID or an object that specifies uploaded file IDs to make available to your code, along with an optional memory_limit setting.

Accepts one of the following:

UnionMember0 = string

The container ID.

CodeInterpreterToolAuto = object { type, file_ids, memory_limit, network_policy }

Configuration for a code interpreter container. Optionally specify the IDs of the files to run the code on.

type: "auto"

Always auto.

file_ids: optional array of string

An optional list of uploaded files to make available to your code.

memory_limit: optional "1g" or "4g" or "16g" or "64g"

The memory limit for the code interpreter container.

Accepts one of the following:

"1g"

"4g"

"16g"

"64g"

network_policy: optional ContainerNetworkPolicyDisabled { type } or ContainerNetworkPolicyAllowlist { allowed_domains, type, domain_secrets }

Network access policy for the container.

Accepts one of the following:

ContainerNetworkPolicyDisabled = object { type }

type: "disabled"

Disable outbound network access. Always disabled.

ContainerNetworkPolicyAllowlist = object { allowed_domains, type, domain_secrets }

allowed_domains: array of string

A list of allowed domains when type is allowlist.

type: "allowlist"

Allow outbound network access only to specified domains. Always allowlist.

domain_secrets: optional array of ContainerNetworkPolicyDomainSecret { domain, name, value }

Optional domain-scoped secrets for allowlisted domains.

domain: string

The domain associated with the secret.

minLength1

The name of the secret to inject for the domain.

minLength1

value: string

The secret value to inject for the domain.

maxLength10485760

minLength1

type: "code_interpreter"

The type of the code interpreter tool. Always code_interpreter.

ImageGeneration = object { type, action, background, 9 more }

A tool that generates images using the GPT image models.

type: "image_generation"

The type of the image generation tool. Always image_generation.

action: optional "generate" or "edit" or "auto"

Whether to generate a new image or edit an existing image. Default: auto.

Accepts one of the following:

"generate"

"edit"

"auto"

background: optional "transparent" or "opaque" or "auto"

Background type for the generated image. One of transparent, opaque, or auto. Default: auto.

Accepts one of the following:

"transparent"

"opaque"

"auto"

input_fidelity: optional "high" or "low"

Control how much effort the model will exert to match the style and features, especially facial features, of input images. This parameter is only supported for gpt-image-1 and gpt-image-1.5 and later models, unsupported for gpt-image-1-mini. Supports high and low. Defaults to low.

Accepts one of the following:

"high"

"low"

input_image_mask: optional object { file_id, image_url }

Optional mask for inpainting. Contains image_url (string, optional) and file_id (string, optional).

file_id: optional string

File ID for the mask image.

image_url: optional string

Base64-encoded mask image.

model: optional string or "gpt-image-1" or "gpt-image-1-mini" or "gpt-image-1.5"

The image generation model to use. Default: gpt-image-1.

Accepts one of the following:

UnionMember0 = string

UnionMember1 = "gpt-image-1" or "gpt-image-1-mini" or "gpt-image-1.5"

The image generation model to use. Default: gpt-image-1.

Accepts one of the following:

"gpt-image-1"

"gpt-image-1-mini"

"gpt-image-1.5"

moderation: optional "auto" or "low"

Moderation level for the generated image. Default: auto.

Accepts one of the following:

"auto"

"low"

output_compression: optional number

Compression level for the output image. Default: 100.

minimum0

maximum100

output_format: optional "png" or "webp" or "jpeg"

The output format of the generated image. One of png, webp, or jpeg. Default: png.

Accepts one of the following:

"png"

"webp"

"jpeg"

partial_images: optional number

Number of partial images to generate in streaming mode, from 0 (default value) to 3.

minimum0

maximum3

quality: optional "low" or "medium" or "high" or "auto"

The quality of the generated image. One of low, medium, high, or auto. Default: auto.

Accepts one of the following:

"low"

"medium"

"high"

"auto"

size: optional "1024x1024" or "1024x1536" or "1536x1024" or "auto"

The size of the generated image. One of 1024x1024, 1024x1536, 1536x1024, or auto. Default: auto.

Accepts one of the following:

"1024x1024"

"1024x1536"

"1536x1024"

"auto"

LocalShell = object { type }

A tool that allows the model to execute shell commands in a local environment.

type: "local_shell"

The type of the local shell tool. Always local_shell.

FunctionShellTool = object { type, environment }

A tool that allows the model to execute shell commands.

type: "shell"

The type of the shell tool. Always shell.

environment: optional ContainerAuto { type, file_ids, memory_limit, 2 more } or LocalEnvironment { type, skills } or ContainerReference { container_id, type }

Accepts one of the following:

ContainerAuto = object { type, file_ids, memory_limit, 2 more }

type: "container_auto"

Automatically creates a container for this request

file_ids: optional array of string

An optional list of uploaded files to make available to your code.

memory_limit: optional "1g" or "4g" or "16g" or "64g"

The memory limit for the container.

Accepts one of the following:

"1g"

"4g"

"16g"

"64g"

network_policy: optional ContainerNetworkPolicyDisabled { type } or ContainerNetworkPolicyAllowlist { allowed_domains, type, domain_secrets }

Network access policy for the container.

Accepts one of the following:

ContainerNetworkPolicyDisabled = object { type }

type: "disabled"

Disable outbound network access. Always disabled.

ContainerNetworkPolicyAllowlist = object { allowed_domains, type, domain_secrets }

allowed_domains: array of string

A list of allowed domains when type is allowlist.

type: "allowlist"

Allow outbound network access only to specified domains. Always allowlist.

domain_secrets: optional array of ContainerNetworkPolicyDomainSecret { domain, name, value }

Optional domain-scoped secrets for allowlisted domains.

domain: string

The domain associated with the secret.

minLength1

The name of the secret to inject for the domain.

minLength1

value: string

The secret value to inject for the domain.

maxLength10485760

minLength1

skills: optional array of SkillReference { skill_id, type, version } or InlineSkill { description, name, source, type }

An optional list of skills referenced by id or inline data.

Accepts one of the following:

SkillReference = object { skill_id, type, version }

skill_id: string

The ID of the referenced skill.

maxLength64

minLength1

type: "skill_reference"

References a skill created with the /v1/skills endpoint.

version: optional string

Optional skill version. Use a positive integer or 'latest'. Omit for default.

InlineSkill = object { description, name, source, type }

description: string

The description of the skill.

The name of the skill.

source: InlineSkillSource { data, media_type, type }

Inline skill payload

data: string

Base64-encoded skill zip bundle.

maxLength70254592

minLength1

media_type: "application/zip"

The media type of the inline skill payload. Must be application/zip.

type: "base64"

The type of the inline skill source. Must be base64.

type: "inline"

Defines an inline skill for this request.

LocalEnvironment = object { type, skills }

type: "local"

Use a local computer environment.

skills: optional array of LocalSkill { description, name, path }

An optional list of skills.

description: string

The description of the skill.

The name of the skill.

path: string

The path to the directory containing the skill.

ContainerReference = object { container_id, type }

container_id: string

The ID of the referenced container.

type: "container_reference"

References a container created with the /v1/containers endpoint

CustomTool = object { name, type, description, format }

A custom tool that processes input using a specified format. Learn more about custom tools

The name of the custom tool, used to identify it in tool calls.

type: "custom"

The type of the custom tool. Always custom.

description: optional string

Optional description of the custom tool, used to provide more context.

format: optional CustomToolInputFormat

The input format for the custom tool. Default is unconstrained text.

Accepts one of the following:

Text = object { type }

Unconstrained free-form text.

type: "text"

Unconstrained text format. Always text.

Grammar = object { definition, syntax, type }

A grammar defined by the user.

definition: string

The grammar definition.

syntax: "lark" or "regex"

The syntax of the grammar definition. One of lark or regex.

Accepts one of the following:

"lark"

"regex"

type: "grammar"

Grammar format. Always grammar.

WebSearchPreviewTool = object { type, search_context_size, user_location }

This tool searches the web for relevant results to use in a response. Learn more about the web search tool.

type: "web_search_preview" or "web_search_preview_2025_03_11"

The type of the web search tool. One of web_search_preview or web_search_preview_2025_03_11.

Accepts one of the following:

"web_search_preview"

"web_search_preview_2025_03_11"

search_context_size: optional "low" or "medium" or "high"

High level guidance for the amount of context window space to use for the search. One of low, medium, or high. medium is the default.

Accepts one of the following:

"low"

"medium"

"high"

user_location: optional object { type, city, country, 2 more }

The user's location.

type: "approximate"

The type of location approximation. Always approximate.

city: optional string

Free text input for the city of the user, e.g. San Francisco.

country: optional string

The two-letter ISO country code of the user, e.g. US.

region: optional string

Free text input for the region of the user, e.g. California.

timezone: optional string

The IANA timezone of the user, e.g. America/Los_Angeles.

ApplyPatchTool = object { type }

Allows the assistant to create, delete, or update files using unified diffs.

type: "apply_patch"

The type of the tool. Always apply_patch.

top_p: optional number

An alternative to temperature for nucleus sampling; 1.0 includes all tokens.

error: EvalAPIError { code, message }

An object representing an error response from the Eval API.

code: string

The error code.

message: string

The error message.

eval_id: string

The identifier of the associated evaluation.

metadata: Metadata

Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

model: string

The model that is evaluated, if applicable.

The name of the evaluation run.

object: "eval.run"

The type of the object. Always "eval.run".

per_model_usage: array of object { cached_tokens, completion_tokens, invocation_count, 3 more }

Usage statistics for each model during the evaluation run.

cached_tokens: number

The number of tokens retrieved from cache.

completion_tokens: number

The number of completion tokens generated.

invocation_count: number

The number of invocations.

model_name: string

The name of the model.

prompt_tokens: number

The number of prompt tokens used.

total_tokens: number

The total number of tokens used.

per_testing_criteria_results: array of object { failed, passed, testing_criteria }

Results per testing criteria applied during the evaluation run.

failed: number

Number of tests failed for this criteria.

passed: number

Number of tests passed for this criteria.

testing_criteria: string

A description of the testing criteria.

report_url: string

The URL to the rendered evaluation run report on the UI dashboard.

result_counts: object { errored, failed, passed, total }

Counters summarizing the outcomes of the evaluation run.

errored: number

Number of output items that resulted in an error.

failed: number

Number of output items that failed to pass the evaluation.

passed: number

Number of output items that passed the evaluation.

total: number

Total number of executed output items.

status: string

The status of the evaluation run.

Cancel eval run

curl https://api.openai.com/v1/evals/eval_67abd54d9b0081909a86353f6fb9317a/runs/evalrun_67abd54d60ec8190832b46859da808f7/cancel \
  -X POST \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: application/json"

{
  "object": "eval.run",
  "id": "evalrun_67abd54d60ec8190832b46859da808f7",
  "eval_id": "eval_67abd54d9b0081909a86353f6fb9317a",
  "report_url": "https://platform.openai.com/evaluations/eval_67abd54d9b0081909a86353f6fb9317a?run_id=evalrun_67abd54d60ec8190832b46859da808f7",
  "status": "canceled",
  "model": "gpt-4o-mini",
  "name": "gpt-4o-mini",
  "created_at": 1743092069,
  "result_counts": {
    "total": 0,
    "errored": 0,
    "failed": 0,
    "passed": 0
  },
  "per_model_usage": null,
  "per_testing_criteria_results": null,
  "data_source": {
    "type": "completions",
    "source": {
      "type": "file_content",
      "content": [
        {
          "item": {
            "input": "Tech Company Launches Advanced Artificial Intelligence Platform",
            "ground_truth": "Technology"
          }
        },
        {
          "item": {
            "input": "Central Bank Increases Interest Rates Amid Inflation Concerns",
            "ground_truth": "Markets"
          }
        },
        {
          "item": {
            "input": "International Summit Addresses Climate Change Strategies",
            "ground_truth": "World"
          }
        },
        {
          "item": {
            "input": "Major Retailer Reports Record-Breaking Holiday Sales",
            "ground_truth": "Business"
          }
        },
        {
          "item": {
            "input": "National Team Qualifies for World Championship Finals",
            "ground_truth": "Sports"
          }
        },
        {
          "item": {
            "input": "Stock Markets Rally After Positive Economic Data Released",
            "ground_truth": "Markets"
          }
        },
        {
          "item": {
            "input": "Global Manufacturer Announces Merger with Competitor",
            "ground_truth": "Business"
          }
        },
        {
          "item": {
            "input": "Breakthrough in Renewable Energy Technology Unveiled",
            "ground_truth": "Technology"
          }
        },
        {
          "item": {
            "input": "World Leaders Sign Historic Climate Agreement",
            "ground_truth": "World"
          }
        },
        {
          "item": {
            "input": "Professional Athlete Sets New Record in Championship Event",
            "ground_truth": "Sports"
          }
        },
        {
          "item": {
            "input": "Financial Institutions Adapt to New Regulatory Requirements",
            "ground_truth": "Business"
          }
        },
        {
          "item": {
            "input": "Tech Conference Showcases Advances in Artificial Intelligence",
            "ground_truth": "Technology"
          }
        },
        {
          "item": {
            "input": "Global Markets Respond to Oil Price Fluctuations",
            "ground_truth": "Markets"
          }
        },
        {
          "item": {
            "input": "International Cooperation Strengthened Through New Treaty",
            "ground_truth": "World"
          }
        },
        {
          "item": {
            "input": "Sports League Announces Revised Schedule for Upcoming Season",
            "ground_truth": "Sports"
          }
        }
      ]
    },
    "input_messages": {
      "type": "template",
      "template": [
        {
          "type": "message",
          "role": "developer",
          "content": {
            "type": "input_text",
            "text": "Categorize a given news headline into one of the following topics: Technology, Markets, World, Business, or Sports.\n\n# Steps\n\n1. Analyze the content of the news headline to understand its primary focus.\n2. Extract the subject matter, identifying any key indicators or keywords.\n3. Use the identified indicators to determine the most suitable category out of the five options: Technology, Markets, World, Business, or Sports.\n4. Ensure only one category is selected per headline.\n\n# Output Format\n\nRespond with the chosen category as a single word. For instance: \"Technology\", \"Markets\", \"World\", \"Business\", or \"Sports\".\n\n# Examples\n\n**Input**: \"Apple Unveils New iPhone Model, Featuring Advanced AI Features\"  \n**Output**: \"Technology\"\n\n**Input**: \"Global Stocks Mixed as Investors Await Central Bank Decisions\"  \n**Output**: \"Markets\"\n\n**Input**: \"War in Ukraine: Latest Updates on Negotiation Status\"  \n**Output**: \"World\"\n\n**Input**: \"Microsoft in Talks to Acquire Gaming Company for $2 Billion\"  \n**Output**: \"Business\"\n\n**Input**: \"Manchester United Secures Win in Premier League Football Match\"  \n**Output**: \"Sports\" \n\n# Notes\n\n- If the headline appears to fit into more than one category, choose the most dominant theme.\n- Keywords or phrases such as \"stocks\", \"company acquisition\", \"match\", or technological brands can be good indicators for classification.\n"
          }
        },
        {
          "type": "message",
          "role": "user",
          "content": {
            "type": "input_text",
            "text": "{{item.input}}"
          }
        }
      ]
    },
    "model": "gpt-4o-mini",
    "sampling_params": {
      "seed": 42,
      "temperature": 1.0,
      "top_p": 1.0,
      "max_completions_tokens": 2048
    }
  },
  "error": null,
  "metadata": {}
}

Returns Examples

{
  "object": "eval.run",
  "id": "evalrun_67abd54d60ec8190832b46859da808f7",
  "eval_id": "eval_67abd54d9b0081909a86353f6fb9317a",
  "report_url": "https://platform.openai.com/evaluations/eval_67abd54d9b0081909a86353f6fb9317a?run_id=evalrun_67abd54d60ec8190832b46859da808f7",
  "status": "canceled",
  "model": "gpt-4o-mini",
  "name": "gpt-4o-mini",
  "created_at": 1743092069,
  "result_counts": {
    "total": 0,
    "errored": 0,
    "failed": 0,
    "passed": 0
  },
  "per_model_usage": null,
  "per_testing_criteria_results": null,
  "data_source": {
    "type": "completions",
    "source": {
      "type": "file_content",
      "content": [
        {
          "item": {
            "input": "Tech Company Launches Advanced Artificial Intelligence Platform",
            "ground_truth": "Technology"
          }
        },
        {
          "item": {
            "input": "Central Bank Increases Interest Rates Amid Inflation Concerns",
            "ground_truth": "Markets"
          }
        },
        {
          "item": {
            "input": "International Summit Addresses Climate Change Strategies",
            "ground_truth": "World"
          }
        },
        {
          "item": {
            "input": "Major Retailer Reports Record-Breaking Holiday Sales",
            "ground_truth": "Business"
          }
        },
        {
          "item": {
            "input": "National Team Qualifies for World Championship Finals",
            "ground_truth": "Sports"
          }
        },
        {
          "item": {
            "input": "Stock Markets Rally After Positive Economic Data Released",
            "ground_truth": "Markets"
          }
        },
        {
          "item": {
            "input": "Global Manufacturer Announces Merger with Competitor",
            "ground_truth": "Business"
          }
        },
        {
          "item": {
            "input": "Breakthrough in Renewable Energy Technology Unveiled",
            "ground_truth": "Technology"
          }
        },
        {
          "item": {
            "input": "World Leaders Sign Historic Climate Agreement",
            "ground_truth": "World"
          }
        },
        {
          "item": {
            "input": "Professional Athlete Sets New Record in Championship Event",
            "ground_truth": "Sports"
          }
        },
        {
          "item": {
            "input": "Financial Institutions Adapt to New Regulatory Requirements",
            "ground_truth": "Business"
          }
        },
        {
          "item": {
            "input": "Tech Conference Showcases Advances in Artificial Intelligence",
            "ground_truth": "Technology"
          }
        },
        {
          "item": {
            "input": "Global Markets Respond to Oil Price Fluctuations",
            "ground_truth": "Markets"
          }
        },
        {
          "item": {
            "input": "International Cooperation Strengthened Through New Treaty",
            "ground_truth": "World"
          }
        },
        {
          "item": {
            "input": "Sports League Announces Revised Schedule for Upcoming Season",
            "ground_truth": "Sports"
          }
        }
      ]
    },
    "input_messages": {
      "type": "template",
      "template": [
        {
          "type": "message",
          "role": "developer",
          "content": {
            "type": "input_text",
            "text": "Categorize a given news headline into one of the following topics: Technology, Markets, World, Business, or Sports.\n\n# Steps\n\n1. Analyze the content of the news headline to understand its primary focus.\n2. Extract the subject matter, identifying any key indicators or keywords.\n3. Use the identified indicators to determine the most suitable category out of the five options: Technology, Markets, World, Business, or Sports.\n4. Ensure only one category is selected per headline.\n\n# Output Format\n\nRespond with the chosen category as a single word. For instance: \"Technology\", \"Markets\", \"World\", \"Business\", or \"Sports\".\n\n# Examples\n\n**Input**: \"Apple Unveils New iPhone Model, Featuring Advanced AI Features\"  \n**Output**: \"Technology\"\n\n**Input**: \"Global Stocks Mixed as Investors Await Central Bank Decisions\"  \n**Output**: \"Markets\"\n\n**Input**: \"War in Ukraine: Latest Updates on Negotiation Status\"  \n**Output**: \"World\"\n\n**Input**: \"Microsoft in Talks to Acquire Gaming Company for $2 Billion\"  \n**Output**: \"Business\"\n\n**Input**: \"Manchester United Secures Win in Premier League Football Match\"  \n**Output**: \"Sports\" \n\n# Notes\n\n- If the headline appears to fit into more than one category, choose the most dominant theme.\n- Keywords or phrases such as \"stocks\", \"company acquisition\", \"match\", or technological brands can be good indicators for classification.\n"
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