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API Reference

Batches

Create large batches of API requests to run asynchronously.

Create batch
batches.create(BatchCreateParams**kwargs) -> Batch
POST/batches
Retrieve batch
batches.retrieve(strbatch_id) -> Batch
GET/batches/{batch_id}
Cancel batch
batches.cancel(strbatch_id) -> Batch
POST/batches/{batch_id}/cancel
List batches
batches.list(BatchListParams**kwargs) -> SyncCursorPage[Batch]
GET/batches
ModelsExpand Collapse
class Batch: …
id: str
completion_window: str

The time frame within which the batch should be processed.

created_at: int

The Unix timestamp (in seconds) for when the batch was created.

endpoint: str

The OpenAI API endpoint used by the batch.

input_file_id: str

The ID of the input file for the batch.

object: Literal["batch"]

The object type, which is always batch.

status: Literal["validating", "failed", "in_progress", 5 more]

The current status of the batch.

One of the following:
"validating"
"failed"
"in_progress"
"finalizing"
"completed"
"expired"
"cancelling"
"cancelled"
cancelled_at: Optional[int]

The Unix timestamp (in seconds) for when the batch was cancelled.

cancelling_at: Optional[int]

The Unix timestamp (in seconds) for when the batch started cancelling.

completed_at: Optional[int]

The Unix timestamp (in seconds) for when the batch was completed.

error_file_id: Optional[str]

The ID of the file containing the outputs of requests with errors.

errors: Optional[Errors]
data: Optional[List[BatchError]]
code: Optional[str]

An error code identifying the error type.

line: Optional[int]

The line number of the input file where the error occurred, if applicable.

message: Optional[str]

A human-readable message providing more details about the error.

param: Optional[str]

The name of the parameter that caused the error, if applicable.

object: Optional[str]

The object type, which is always list.

expired_at: Optional[int]

The Unix timestamp (in seconds) for when the batch expired.

expires_at: Optional[int]

The Unix timestamp (in seconds) for when the batch will expire.

failed_at: Optional[int]

The Unix timestamp (in seconds) for when the batch failed.

finalizing_at: Optional[int]

The Unix timestamp (in seconds) for when the batch started finalizing.

in_progress_at: Optional[int]

The Unix timestamp (in seconds) for when the batch started processing.

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[str]

Model ID used to process the batch, like gpt-5-2025-08-07. 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.

output_file_id: Optional[str]

The ID of the file containing the outputs of successfully executed requests.

request_counts: Optional[BatchRequestCounts]

The request counts for different statuses within the batch.

usage: Optional[BatchUsage]

Represents token usage details including input tokens, output tokens, a breakdown of output tokens, and the total tokens used. Only populated on batches created after September 7, 2025.

class BatchError: …
code: Optional[str]

An error code identifying the error type.

line: Optional[int]

The line number of the input file where the error occurred, if applicable.

message: Optional[str]

A human-readable message providing more details about the error.

param: Optional[str]

The name of the parameter that caused the error, if applicable.

class BatchRequestCounts: …

The request counts for different statuses within the batch.

completed: int

Number of requests that have been completed successfully.

failed: int

Number of requests that have failed.

total: int

Total number of requests in the batch.

class BatchUsage: …

Represents token usage details including input tokens, output tokens, a breakdown of output tokens, and the total tokens used. Only populated on batches created after September 7, 2025.

input_tokens: int

The number of input tokens.

input_tokens_details: InputTokensDetails

A detailed breakdown of the input tokens.

cached_tokens: int

The number of tokens that were retrieved from the cache. More on prompt caching.

output_tokens: int

The number of output tokens.

output_tokens_details: OutputTokensDetails

A detailed breakdown of the output tokens.

reasoning_tokens: int

The number of reasoning tokens.

total_tokens: int

The total number of tokens used.