RATIONALE AND OBJECTIVES: This study aims to evaluate the performance of generative pre-trained transformer (GPT)-4o in the complete official European Board of Radiology (EBR) exam, designed to assess radiology knowledge, skills, and competence. MATERIALS AND METHODS: Questions based on text, image, or video and in the format of multiple choice, free-text reporting, or image annotation were uploaded into GPT-4o using standardized prompting. The results were compared to the average scores of radiologists taking the exam in real time. RESULTS: In Part 1 (multiple response questions and short cases), GPT-4o outperformed both the radiologists' average scores and the maximum pass score (70.2% vs. 58.4% and 60%, respectively). In Part 2 (clinically oriented reasoning evaluation), the performance of GPT-4o was below both the radiologists' average scores and the minimum pass score (52.9% vs. 66.1% and 55%, respectively). The accuracy on questions involving ultrasound images was higher compared to other imaging modalities (accuracy rate, 87.5-100%). For video-based questions, the performance was 50.6%. The model achieved the highest accuracy on most likely diagnosis questions but showed lower accuracy in free-text reporting and direct anatomical assessment in images (100% vs. 31% and 28.6%, respectively). CONCLUSION: The abilities of GPT-4o in the official EBR exam are particularly noteworthy. This study demonstrates the potential of large language models to assist radiologists in assessing and managing cases from diagnosis to treatment or follow-up recommendations, even with zero-shot prompting.