What are benchmarks like MMLU, HumanEval, and GSM8K, and how should you interpret them?

These are standardised test sets for comparing base models. MMLU covers broad multiple-choice knowledge across many subjects; HumanEval measures code generation by running the code against unit tests; GSM8K tests grade-school math word problems (reasoning). They're useful for rough capability comparison, but interpret them cautiously: benchmark contamination (test questions leaking into training data) inflates scores, a high average hides weakness on your specific domain, and these academic tasks rarely match your production workload. A model topping MMLU may still be worse for your RAG chatbot. Use public benchmarks to shortlist candidates, then run your own task-specific evaluation on data that reflects real usage — that's what actually predicts how the model performs for you.