AI benchmarks are standardized test sets used to compare model capabilities: math, coding, reasoning, knowledge, agentic tasks. They drive headlines and leaderb
Only as a shortlist. Benchmarks are standardized test sets for capabilities like math, coding, and reasoning, but they leak: models increasingly train on benchmark-adjacent data, inflating scores. A public rank predicts your task's quality far more weakly than evaluating on your own data.
The professional stance is: benchmarks shortlist candidate models; your own evals decide. Use leaderboards to narrow the field, then run an afternoon of evaluation on your actual data and use cases before committing to a model.