Benchmarks

Benchmarks are the primary way the AI community evaluates and compares language models. Each benchmark consists of a curated set of questions or tasks with known correct answers, allowing objective scoring. Popular benchmarks include MMLU (massive multitask language understanding), HumanEval (code generation), GSM8K (grade-school math), HellaSwag (commonsense reasoning), and ARC (science questions).

Different benchmarks test different capabilities. MMLU covers 57 academic subjects from elementary to professional level. HumanEval asks models to write Python functions that pass unit tests. MT-Bench evaluates multi-turn conversation quality using GPT-4 as a judge. Chatbot Arena uses human preferences from real conversations to create an Elo rating. Each benchmark reveals a different facet of model capability, and no single benchmark tells the whole story.

Benchmark scores should be interpreted carefully. A model scoring 90% on MMLU may still fail at basic reasoning tasks not covered by the test. Benchmark contamination — where a model has seen test questions during training — can inflate scores. Some benchmarks become saturated as models improve, losing their ability to differentiate between top performers. Researchers continuously develop new benchmarks (like GPQA for PhD-level questions or SWE-bench for real-world software engineering) to keep pace with model capabilities.

When comparing models on GPTCrunch, look at benchmark scores as one data point among many. Consider which benchmarks align with your use case: if you need a coding assistant, HumanEval and SWE-bench matter more than MMLU. If you need general knowledge, MMLU and ARC are more relevant. The best model for you is the one that performs well on the tasks you actually care about, not necessarily the one with the highest average score.