Read the question behind the score
A benchmark measures performance on a particular collection of tasks under particular conditions. That can be informative without being a complete description of a model. Before comparing scores, understand what the test actually asks the system to do.
Look for the missing context
Prompt format, tool access, sampling settings, and the number of attempts can all affect a result. A score achieved with extensive retries may not describe the experience of a person asking one question in an application.
Averages also hide distributions. Two models can reach similar totals while failing on very different examples. If your work depends on a narrow subject or a particular output format, those differences matter.
Build a small reality check
Collect a modest set of representative tasks from your own work. Remove sensitive information, define what a good result looks like, and compare outputs consistently. Keep examples that expose failure instead of selecting only the most impressive responses.
The takeaway
Use public evaluations to choose candidates, then test the candidates against your actual needs. Benchmarks are a map. Your workload is the terrain.
