Standard benchmarks help teams compare model accuracy, reasoning and robustness. Datasets like MMLU and MT-Bench cover diverse subjects from closed book question answering to structured tool use. No single benchmark captures every capability, so practitioners combine automated metrics with human review for a holistic view.
When selecting models, focus on tasks that mirror your production workload. Leaderboards provide a snapshot of raw skill, but fine-tuned metrics and domain specific tests give a clearer picture of real world performance.