Examples include MMLU for broad knowledge, coding benchmarks that run generated programs against tests, and human-preference arenas. Benchmarks make progress measurable, but they saturate as models improve and can leak into training data, inflating scores.
For choosing a model for real work, results on your own tasks matter more than leaderboard positions. See the MMLU paper explained.