AI Benchmarks Explained

AI benchmarks are standardized tests used to measure and compare the capabilities of language models.

**Common Benchmarks:** - **MMLU**: Tests knowledge across 57 academic subjects - **HumanEval**: Measures code generation ability - **GPQA Diamond**: Tests scientific reasoning at PhD level - **MATH**: Mathematical problem-solving - **Arena Elo**: Human preference voting (blind A/B tests)

**Composite Indices:** - **Artificial Analysis Intelligence Index**: Combines 10 evaluations including coding, reasoning, knowledge - **LiveBench**: Contamination-free benchmark refreshed every 6 months - **BenchLM BenchAlign**: 8 weighted categories across 27 benchmarks

**How to Interpret Scores:** - Higher is generally better, but context matters - A model good at coding may be weak at creative writing - Benchmarks can be "gamed" (trained on test data) - Real-world performance may differ from benchmark scores

**Red Flags:** - Models claiming 100% on any benchmark (likely trained on test data) - Only showing favorable benchmarks (cherry-picking) - Comparing across different benchmark versions - Not disclosing benchmark methodology