Is the OpenAI batch queue predictable?

We measure it: gpt-5.6-sol runs a spread of 1.1x (p90 57s over p50 51s), the tightest distribution we measure across every model on the site. A tight spread means the median is safe to book a deadline against - which is a real part of whether the batch API is worth it, not just the 50% price cut.

Is the OpenAI batch API worth the wait?

For most workloads, yes. It is 50% cheaper, and half of gpt-5-nano jobs finished within 50s in our measurements - well inside the 24 hours window OpenAI promises. The only question is your deadline, which is why batchwatch answers against a max_wait you supply rather than guessing one.

How much does the OpenAI batch API save?

50% of the synchronous price, on both input and output tokens. That is OpenAI's own published batch rate, not a batchwatch estimate.

Is the OpenAI Batch API worth it?

The answer

OpenAI's batch endpoint costs 50% of the synchronous rate - their own published price, on every input and output token. That is a real, immediate saving on any workload that does not need an answer this second.

And the wait is shorter than the "within 24 hours" ceiling suggests: we measured a median of 50s for gpt-5-nano, with nine in ten jobs finishing within 29 min. For most pipelines that is an easy trade - half the cost for a wait you can plan around.

There is a second half to "worth it" that price alone misses: how predictable the queue is. gpt-5.6-sol's spread - its p90 over its p50 - is 1.1x, the tightest distribution we measure across every model on the site. The slow tail sits close to the median, so the number you measure once is close to the number you get again, and sizing a deadline against the tail costs almost nothing. That consistency is itself part of the value.

Every model we have measured

Every OpenAI model we can stand behind a percentile for - price is the same 50% cut on all of them; the wait is what differs. Most predictable first - spread is p90 over p50.

Model	Median (p50)	p90	Spread	n
OpenAI gpt-5.6-sol	51s	57s	1.1x	1728
OpenAI gpt-5.6-luna	61s	4 min	3.7x	3332
OpenAI gpt-5-nano	50s	29 min	34.2x	3891

Each row is measured over the last 30 days, and links to that model's own page with its full distribution and confidence grade. A model we have not yet measured enough of to stand behind a percentile is left out entirely - never filled in with an estimate.

The full numbers, per model

Questions this answers

Is the OpenAI batch queue predictable?: We measure it: gpt-5.6-sol runs a spread of 1.1x (p90 57s over p50 51s), the tightest distribution we measure across every model on the site. A tight spread means the median is safe to book a deadline against - which is a real part of whether the batch API is worth it, not just the 50% price cut.
Is the OpenAI batch API worth the wait?: For most workloads, yes. It is 50% cheaper, and half of gpt-5-nano jobs finished within 50s in our measurements - well inside the 24 hours window OpenAI promises. The only question is your deadline, which is why batchwatch answers against a max_wait you supply rather than guessing one.
How much does the OpenAI batch API save?: 50% of the synchronous price, on both input and output tokens. That is OpenAI's own published batch rate, not a batchwatch estimate.

See it live

Every number here is measured, and it is all public.

The live dashboard shows every model with its distribution, n and confidence grade. /v1/coverage is the same data as JSON, no key required. Send five measurements in seven days and the delay is gone: you read every figure live, and the decision endpoints open (/v1/should-i-batch, /v1/estimate-batchtime).