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No. The OpenAI Batch API groups requests for asynchronous processing, but it does not turn them into unrestricted operations. Each line still has to meet the requirements of its endpoint, and the batch is subject to separate queue, size, request-count, and completion-window limits.
What a batch does—and does not—change
A Batch API input is a JSONL file containing separate requests, one per line. Each request has its own endpoint and request body, and each needs a unique custom_id so you can match its result to the original input. Batch changes how the requests are submitted and processed; it does not make them a single request exempt from endpoint requirements.
OpenAI’s Batch API guide lists the supported endpoints and describes request-level requirements. Check that the endpoint and model are available for your use, and that every request body uses the parameters supported by that endpoint. For example, the guide notes that moderation requests reject stream=true.
Which limits still apply?
Batch has its own operational limits, distinct from the standard limits used for synchronous requests. That distinction is not unlimited capacity: your work must fit both the rules for each request and the constraints on the batch system.
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- Per-request requirements: Each line must use a supported endpoint and a compatible request body.
- Batch constraints: OpenAI documents maximum request counts and file sizes per batch, as well as a limit on batch creation.
- Queued-token capacity: Batch queue limits are based on input tokens queued for a model. Pending jobs count against the available queue until they complete. The available limit depends on the account and model; check the live value in Platform Settings.
- Account and billing limits: A separate batch queue does not remove account usage or billing constraints.
The rate-limit guide explains the distinction between standard limits and batch queue limits. Because queue capacity is account- and model-dependent, do not assume a limit from another account or model applies to yours.
How batch processing differs from synchronous calls
| Aspect | Batch API | Synchronous API calls |
|---|---|---|
| Timing | Asynchronous processing, with a documented 24-hour completion window. | Returns a response to each call synchronously. |
| Capacity | Uses batch queue limits, including queued input-token capacity for each model. | Uses standard request and token limits. |
| Request handling | Requires separate JSONL lines, supported endpoint formats, and unique custom_id values. |
Each call is sent and handled individually. |
| Completion risk | A batch can finish only some requests before its window expires; unfinished requests are cancelled. | There is no batch completion window for a group of calls. |
| Pricing | Check current pricing for the endpoint and model before submitting. | Check current pricing for the endpoint and model before calling. |
What happens when a request fails or a batch expires?
A batch is not guaranteed to succeed as a whole. Inspect its output and error files to identify which individual requests completed and which did not. A failed line needs its own diagnosis; a batch-level status alone may not explain the cause.
OpenAI documents a 24-hour completion window. If a batch expires, unfinished requests are cancelled. Responses for requests that did finish remain available, and completed work is charged. Plan for partial completion rather than treating submission as an all-or-nothing operation.
When an error appears related to limits, inspect its details before deciding what to do. A rate-limit error may call for pacing or a retry; a billing or usage-limit error may instead require credits or an account-limit change. Those issues can look similar at first glance, but retrying will not resolve an account usage limit.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to submit a batch without avoidable failures
- Confirm endpoint and model support. Check the current Batch API guide and the endpoint’s requirements before building the input file.
- Validate every JSONL line. Make sure each line has a supported endpoint, a compatible request body, and a unique
custom_id. Check endpoint-specific restrictions, including whether a parameter such asstream=trueis allowed. - Check live capacity. In Platform Settings, verify the queued-token limit for the model you plan to use. Account for pending jobs, since they continue to consume queue capacity until completion.
- Submit within the documented batch limits. Check current maximums for request count, file size, and batch creation rather than relying on an old or account-specific value.
- Monitor status and inspect both result files. Review output and error files to find completed, failed, or still-unfinished requests, then handle each case according to its error details and the batch’s remaining completion window.
Batch can be useful when asynchronous results fit the job, but it is not a way to bypass endpoint rules, request validation, queue capacity, or account limits. For operational details, consult OpenAI’s Batch API guide, rate-limit guide, Batch API reference, and Batch API FAQ.
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