A benchmark is a standardized test used to compare models. Benchmarks are useful for a first-pass ranking, but they are public, saturate over time, and rarely match your workload. Treat them as a starting filter, then evaluate candidates on your own dataset.
Why it matters
Benchmarks give a fast, shared starting point for comparing models, but they are public and saturate, so they rarely predict performance on your specific workload.
How it works
A benchmark is a fixed test set with a defined scoring method. Use it to shortlist candidates, then evaluate the shortlist on your own dataset, because a model can lead a public benchmark and still underperform on your domain.