Benchmark
A standard set of test questions used to compare how well different AI models perform.
A benchmark is a fixed set of tasks or questions, with agreed ways of scoring the answers, that lets people compare AI models on equal terms. Think of it as an exam paper that every model sits under the same conditions. Common examples test general knowledge, mathematics, computer programming or the ability to follow instructions.
Benchmarks matter because they are the main way progress in AI is described to the public. When a company says a new model is better than the last one, it usually means the model scored higher on one or more benchmarks. Without them, claims would be hard to compare or check.
They also have limits. A model can do well on a test without being useful in daily work, and a test can stop being informative once models learn its style or its answers. Researchers therefore treat any single score with caution and prefer to look at many benchmarks together, alongside real-world use. Scores reported by the developers themselves deserve extra care, since they choose which tests to show.