AI benchmarks are curated test sets with ground-truth answers used to measure and compare model capabilities. Famous ones: MMLU (multitask language understanding), HumanEval (code), GSM8K (math word problems), GPQA (graduate-level science), and SWE-bench (software engineering).
Benchmark numbers are useful for initial model selection but have well-known limitations. Models increasingly overfit to benchmarks through training data contamination — a model can score well on a test it was trained on without actually understanding the underlying skill. The "vibe shift" between benchmark performance and production performance is real and common.
The practical advice: treat benchmarks as a filter (eliminate obviously weak models) and run your own task-specific evals as the final decision. A model that scores 90% on MMLU but 60% on your actual use case is worse than a model that scores 85% on MMLU and 80% on your use case.
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