objective This study aims to evaluate the performance of different large language models in the qualification examination for traditional Chinese medicine （TCM） practitioners. Methods Five large language models—ERNIE Bot 4.0， ChatGPT 4.0， Baichuan Big Model 3.0， Claude3-Sonnet， and ChatGLM 4.0—were tested for accuracy in answering questions from different disciplines within the TCM practitioner qualification exam question bank. Results ERNIE Bot 4.0 and Baichuan Big Model 3.0 achieved the highest overall accuracy rates across various TCM disciplines， with ERNIE Bot 4.0 exceeding an 80% accuracy rate. ChatGLM 4.0 showed the lowest overall accuracy. The models performed better in disciplines such as TCM Internal Medicine and Traditional Chinese Pharmacology， but their accuracy dropped significantly in Formula Science and TCM Classics， which require understanding ancient Chinese medical texts and advanced application skills. Differences in performance between the models were also observed. Conclusion The performance variations among the models indicate that factors such as the content and quality of training data， as well as the models' logical reasoning abilities， play a significant role in their effectiveness. As artificial intelligence continues to evolve， large language models are expected to become valuable teaching aids， potentially transforming education. Enhancing model training in specialized fields could improve their understanding and application of professional terminology， thereby better addressing educational needs and improving both teaching quality and learning efficiency.