AI Model Benchmark Comparator

MMLU (Massive Multitask Language Understanding) tests knowledge across 57 subjects — STEM, humanities, law, medicine, and more. It's the most widely cited general-knowledge benchmark. A score of 90%+ is roughly 'better than most human experts across all domains'. It's measured as % of correct multiple-choice answers.

HumanEval is OpenAI's Python coding benchmark — 164 hand-written programming problems. The model generates a function implementation given a docstring, and it's graded by running unit tests. It measures practical coding ability better than perplexity or next-token prediction. Score = % of problems where all tests pass (pass@1).

GSM8K (Grade School Math 8K) contains 8,500 grade school math word problems requiring 2–8 step reasoning chains. It tests arithmetic reasoning and the ability to follow multi-step logical chains. Modern frontier models now score 90%+, while earlier GPT-3-class models scored under 10%.

TruthfulQA specifically tests whether models give truthful answers to questions where humans tend to give misconceptions (e.g., 'Does eating before swimming cause cramps?'). Models trained on human data inherit human misconceptions. Scores in the 60–80% range are typical for frontier models — there is fundamental tension between fluency and factual accuracy.

Partially. Benchmark results vary significantly based on: (1) evaluation methodology — 5-shot vs 0-shot vs chain-of-thought prompting, (2) whether models were fine-tuned on benchmark-adjacent data, (3) sampling settings (temperature, top-p). Scores from different papers may not be directly comparable. Always check whether results use the same evaluation setup.

For raw coding (HumanEval), Claude 3.5 Sonnet and GPT-4o are both excellent (90%+ range). For agentic coding with long context (large codebases), Claude 3.5 Sonnet's 200K context window is a significant advantage. For cost-sensitive bulk code generation, GPT-4o mini or Gemini Flash offer strong performance at a fraction of the price.