Evaluating the performance of DeepSeek-V3, Doubao, ERNIE 4.5 Turbo and iFLYTEK Spark in the Chinese national nursing licensing examination: a cross-sectional comparative study.

Background: While large language models (LLMs) have been widely adopted in nursing education and several studies have evaluated their performance on the Chinese National Nursing Licensing Examination (NNLE), systematic comparisons focusing on the latest generation of Chinese LLMs such as DeepSeek-V3, Doubao, ERNIE 4.5 Turbo, and iFLYTEK Spark remain limited. Furthermore, no study has specifically examined the multimodal capabilities of these models using image-based nursing questions. Objective: This study aimed to compare the performance of DeepSeek-V3, Doubao, ERNIE 4.5 Turbo, and iFLYTEK Spark LLMs on the NNLE and evaluate their potential for nursing education. Methods: This cross-sectional study assessed DeepSeek-V3, Doubao, ERNIE 4.5 Turbo, and iFLYTEK Spark using questions from the 2025 NNLE, including both authentic text-based items ( Results: On text-based NNLE items, all four models achieved accuracy rates above 90% (DeepSeek-V3: 92.9, 95% CI [89.0, 95.6%]; Doubao: 94.6, 95% CI [91.0, 96.9%]; ERNIE 4.5 Turbo: 93.3, 95% CI [89.5, 95.9%]; iFLYTEK Spark: 92.1, 95% CI [88.0, 95%]), meeting the NNLE passing requirement. No significant differences in overall accuracy were observed among models (all pairwise comparisons Conclusion: This study demonstrates that DeepSeek-V3, Doubao, ERNIE 4.5 Turbo, and iFLYTEK Spark perform well on Chinese nursing examinations questions in text format, indicating their potential as auxiliary resources for nursing education and exam preparation. However, their performance on image-based items is substantially lower, and the findings do not support claims of clinical applicability. Further research is needed to assess their utility in real-world clinical reasoning or patient care.