Faculties of Health Science2026-08-122026-08-1220251932-6203AS1319https://doi.org/10.1371/journal.pone.0317423https://doi.org/10.1371/journal.pone.0317423This study evaluates the performance of the latest large language models (LLMs) in answering dental multiple-choice questions, including both text-based and image-based formats. A total of 1,490 questions from United States National Board Dental Examination review books were used to assess models such as ChatGPT-4, Gemini, Copilot, Claude, Mistral, and Llama. Statistical analyses revealed significant differences in accuracy across models. Copilot, Claude, and ChatGPT achieved the highest performance, especially on text-based questions, while performance varied more on image-based questions. The findings indicate that multimodal capabilities improve performance and highlight the growing potential of LLMs in dental education and assessment.10 pagesengCC BY 4.0Large Language ModelsDentistryMultiple Choice QuestionsArtificial IntelligenceComparative StudyArticle