A Prompt Optimizer Learned How Much Evidence It Could Afford

Automatic prompt optimizers often assume hundreds or thousands of model calls, a poor fit for paid or rate-limited systems. BudgetAPO begins with a short noise probe, sizes each evaluation slice to the task, and compares candidates on a fixed paired slice. Across seven benchmarks and five subject models, the authors report that it ranked first for every subject under their tests. At a 250-call budget it returned the unchanged seed prompt in 13% of runs, versus 86% for GEPA; on GPT-OSS-20B, GEPA needed 4.5 times as many calls to match BudgetAPO's 100-call score. The result turns budget allocation into part of the optimizer rather than an external limit it discovers too late.