Benchmarks are the smoke alarm of AI selection. Useful, but not smokeproof.
MMLU: breadth of knowledge. A 90% score means the model has read a lot. It doesn't mean it reasons well about your specific problem.
HumanEval: code correctness on small, isolated problems. Closer to your job, but still a unit test, not a codebase.
SWE-bench Verified: the one that actually matters. Real GitHub issues, real repos, real tests. If a model tops SWE-bench, it has learned to navigate a codebase like an engineer.
LMArena (Chatbot Arena): Elo ratings from real human comparisons. The most honest signal we have, though harder to game with contaminated training data.
MATH and GPQA: reasoning under pressure. DeepSeek R1 made these famous.
Our rule of thumb: pick benchmarks that resemble your task. If you're writing SQL queries, HumanEval + a task-specific suite beats MMLU every time. If you want the surprising truth, test on your own code before you commit.