Evaluating Large‐Scale and Lightweight Large Language Models for Traditional Chinese Medicine Exam Questions: A Comparative Study

This study demonstrates that although large-scale LLMs exhibit strong knowledge recall in TCM, their suboptimal performance on multiple-choice questions and substantial computational costs may limit their practical applicability in clinical settings. The robust performance of Qwen3-1.7B indicates that effective model optimization and domain-specific training may offer greater advantages than simply increasing model size. While the current evaluation is based on examination-style tasks and does not involve real-world clinical decision-making, our findings provide insights to support the deployment of optimized models in resource-constrained healthcare environments.