Best-of-N LLM sampling cuts latency but costs nearly 3x more tokens than retries

The Best-of-N strategy fires multiple LLM attempts simultaneously and keeps the best result, trading higher guaranteed token costs for lower and more predictable response latency. Unlike sequential retries, which only charge for additional attempts when earlier ones fail, Best-of-N always pays for all N attempts regardless of outcome. Simulations run over 20,000 trials show that at a 30% failure rate, Best-of-N with four attempts consumes roughly 2.9 times the tokens of sequential retrying while achieving the same success rate. The approach is most justified when response latency carries a real cost — such as user-facing applications — and when parallel attempts fail independently of one another. Teams that skip measuring attempt independence risk paying a steep token premium without gaining the reliability benefits the strategy promises.