Benchmarks score models on fixed datasets so different systems can be compared on equal footing. A widely cited example is MMLU (Hendrycks et al., 2021), which tests knowledge and reasoning across 57 subjects from mathematics to law.
Benchmarks are essential but imperfect: results can be inflated by training-data contamination, and a high score on a fixed test does not guarantee good behaviour on your specific, real-world prompts — part of why MultipleChat lets you compare models directly on your own questions.