The Dunning-Kruger Effect in Large Language Models: An Empirical Study of Confidence Calibration

A new study reveals that large language models exhibit patterns similar to the Dunning-Kruger effect, where poorly performing AI models show severe overconfidence in their abilities. The research tested four major models across 24,000 trials, finding that Kimi K2 displayed the worst calibration with 72.6% overconfidence despite only 23.3% accuracy, while Claude Haiku 4.5 achieved the best performance with proper confidence calibration.