LLM Performance Benchmarking Guide

Public LLM benchmarks (MMLU, HumanEval, HellaSwag, etc.) measure general capabilities on standardized academic tasks. They answer the question: is this model generally smart? They do not answer the question you actually need answered: will this model perform well on my specific use case, with my prompts, at my latency requirements, within my budget? A model that scores highest on MMLU may be the wrong choice for your product because it is too expensive, too slow, or not better than a cheaper model on your specific task.

Production benchmarking evaluates models in the context where they will actually be used. This means testing with your prompts (not standardized benchmarks), measuring latency at production-relevant percentiles (p95 and p99, not just average), calculating total cost including prompt tokens and caching effects (not just listed per-token prices), and evaluating quality using domain-specific criteria (not general knowledge tests). The result is a decision matrix that tells you which model provides the best value for each of your use cases.