Benchmarks & Their Limits

Benchmarks are standardised tests used to compare AI models - the equivalent of league tables for machine intelligence. They cover specific skills: MMLU tests general knowledge across dozens of subjects, HumanEval tests coding ability, GSM8K tests mathematical reasoning, and so on. Benchmarks have been invaluable for tracking progress: they provide common ground for comparison and create clear targets for improvement. But they have serious limitations. Models can be specifically optimised to perform well on popular benchmarks - a practice called "teaching to the test" - which inflates scores without necessarily improving real-world usefulness. Benchmark contamination is another problem: if test questions appeared in the model's training data, high scores don't demonstrate genuine capability. Many benchmarks also test narrow, well-defined skills that don't reflect the messy, ambiguous tasks AI is actually used for. A model might ace a benchmark on medical knowledge while being dangerously unreliable in actual clinical contexts. For business decision-makers, benchmark scores are useful for rough comparisons but should never be the sole basis for choosing a model. The question that matters is how the model performs on your tasks with your data - and the only reliable way to answer that is to test it yourself.