Multiple Large Language Models’ Performance on the Chinese Medical Licensing Examination: Quantitative Comparative Study
Bibliographic record
Abstract
Background: ChatGPT excels in natural language tasks, but its performance in the Chinese National Medical Licensing Examination (NMLE) and Chinese medical education remains underexplored. Meanwhile, Chinese corpus-based large language models (LLMs) such as ERNIE Bot, Tongyi Qianwen, Doubao, and DeepSeek have emerged, yet their effectiveness in the NMLE awaits systematic evaluation. Objective: This study aimed to quantitatively compare the performance of 6 LLMs (GPT-3.5, GPT-4, ERNIE Bot, Tongyi Qianwen, Doubao, and DeepSeek) in answering NMLE questions from 2018 to 2024 and analyze their feasibility as supplementary tools in Chinese medical education. Methods: We selected questions from the 4 content units of the NMLE's General Written test (2018-2024), preprocessed image- and table-based content into standardized text, and input the questions into each model. We evaluated the accuracy, comprehensiveness, and logical coherence of the responses, with quantitative comparison centered on scores and accuracy rates against the official answer keys (passing score: 360/600). Results: GPT-4 outperformed GPT-3.5 across all units, achieving average accuracies of 66.57% (SD 3.21%; unit 1), 69.05% (SD 2.87%; unit 2), 71.71% (SD 2.53%; unit 3), and 80.67% (SD 2.19%; unit 4), with consistent scores above the passing threshold. Among the Chinese models, DeepSeek demonstrated the highest overall performance, with an average score of 454.8 (SD 17.3) and average accuracies of 73.2% (unit 1, SD 2.89%) and 71.5% (unit 3, SD 2.64%), as well as average accuracies of 70.3% (unit 2, SD 3.02%) and 78.2% (unit 4, SD 2.47%). ERNIE Bot (mean score 442.3, SD 19.6; unit 1 accuracy =70.8%, SD 3.01%; unit 2 accuracy =68.7%, SD 3.15%; unit 3 accuracy =69.1%, SD 2.93%; unit 4 accuracy =68.3%, SD 2.76%), Tongyi Qianwen (mean score 426.5, SD 21.4; unit 1 accuracy =67.4%, SD 3.22%; unit 2 accuracy =65.9%, SD 3.31%; unit 3 accuracy =66.2%, SD 3.08%; unit 4 accuracy =67.2%, SD 2.89%), and Doubao (mean score 413.7, SD 23.1; unit 1 accuracy =65.2%, SD 3.45%; unit 2 accuracy =63.8%, SD 3.52%; unit 3 accuracy =64.1%, SD 3.27%; unit 4 accuracy =62.8%, SD 3.11%) all exceeded the passing score. DeepSeek's overall average accuracy (75.8%, SD 2.73%) was significantly higher than those of the other Chinese models (χ²₁=11.4, P=.001 vs ERNIE Bot; χ²₁=28.7, P<.001 vs Tongyi Qianwen; χ²₁=45.3, P<.001 vs Doubao). GPT-4's overall average accuracy (77.0%, SD 2.58%) was slightly higher than that of DeepSeek but not statistically significant (χ²₁=2.2, P=.14), while both outperformed GPT-3.5 (overall accuracy =68.5%, SD 3.67%; χ²₁=89.8, P<.001 for GPT-4 vs GPT-3.5; χ²₁=76.3, P<.001 for DeepSeek vs GPT-3.5). Conclusions: GPT-4 and Chinese-developed LLMs such as DeepSeek show potential as supplementary tools in Chinese medical education given their solid performance on the NMLE. However, further optimization is required for complex reasoning, multimodal processing, and dynamic knowledge updates, with human medical expertise remaining central to clinical practice and education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".