Human baseline
How people score on the same test as an AI model, provided for comparison.
A human baseline is the score that people achieve on the same test given to an AI model. It gives a reference point, so that a result like “the model scored 80%” can be read alongside how well people do. Without it, a number on its own says little.
Baselines are collected by asking a group of people to take the test, often paid volunteers or online workers, and sometimes experts. The score is then quoted as an average or as a best result. Claims that AI has “matched” or “beaten” humans on a test usually rest on such a comparison.
They should be read with care. Which humans were asked makes a big difference: an average member of the public, or a specialist, and how much time and help they were given. The test conditions for the person and the model may also differ. Some baselines come from very few people, and some tests are so flawed that even experts disagree about the right answer.
Beating a human baseline on one test does not mean a system matches people generally. It shows performance on that task only.
One test shows how much the choice of people matters. On GPQA Diamond, a set of hard science questions, experts answering in their own field score about 81%. Skilled people from other fields score about 22%, even with the web to hand and around half an hour per question.