The answer
We have measured this. Across the 1 Google model we have enough completions for, the median wait ranges from 2 min (gemini-3.7-flash) to 2 min (gemini-3.7-flash). Nine in ten gemini-3.7-flash jobs finished within 3 min. Every figure carries n and a confidence grade on its own page.
Google promises only "within 24 hours" - a ceiling, not a typical wait. The measured medians above are what jobs actually get, and they are why batching is worth the wait for most workloads.
How predictable is it?
1.3x spread - p90 over p50
How much longer the slow tail runs than the typical job. Nearer 1× is more predictable.
Half of gemini-3.7-flash jobs finished within 2 min, and nine in ten within 3 min - a spread of 1.3x. The tail sits close to the middle, so what you measure once is close to what you get again, and sizing a deadline against the tail costs almost nothing. It is the tightest distribution we measure across every model on the site - the most predictable batch queue we have found.
Spread is p90 divided by p50, both measured here over the last 30 days. No provider publishes it; it is the number that tells you whether a median is safe to plan on.
The full numbers, per model
Questions this answers
- How long does the Google batch API actually take?
- Half of gemini-3.7-flash jobs finished within 2 min and nine in ten within 3 min, across 2414 measured completions in the last 30 days.
- Is that faster than Google's 24-hour window?
- Far faster for the typical job. Google guarantees completion within 24 hours; the measured medians above are minutes, not hours. The 24-hour window is the contract, not the experience.
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).