Field noteOct 23, 20261 min read1 source

The Methodology of NerfBench

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BridgeBench has described the methodology behind NerfBench, its benchmark for tracking whether a served model continues to perform as it did at the start of measurement.

What changed: BridgeBench details NerfBench’s protocol: 50 automatically graded tasks, 150 attempts per session, a frozen baseline, a weighted Power score, and repeat-session thresholds for reporting changes.

The suite contains 50 programming and reasoning tasks across eight categories: 33 code tasks and 17 exact-answer tasks.

A full session runs every task three times, producing 150 independently graded attempts. Code is extracted from responses and run in a separate Node process against tests; answer tasks are normalized and compared with an expected result. The model receives no tools or repository, and no model is used as a grader. Unanswered requests are retried up to five times and leave a session incomplete rather than counting as failures.

Each model’s first complete session is frozen as its 100% reference. NerfBench’s “Power” score combines correctness (60%), output tokens (20%), and cost (20%), relative to that reference. Results between 90% and 110% are considered normal; an out-of-band result requires two consecutive comparable sessions before publication. The source does not disclose the exact prompts, hidden tests, expected answers, or reference implementations, so its results cannot be independently reproduced from the published material alone.

Evidence / sources

  1. 1bridgebench.ai/blog/the-methodology-of-nerfbench
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