Performance of deepseek-R1 and ChatGPT-5.4 thinking in the medical laboratory professional title examination: accuracy, stability, and comparison with interns.

Objective: To systematically evaluate the accuracy, reproducibility, and performance of Deepseek-R1 and ChatGPT-5.4 Thinking across different question types and disciplines in the Medical Laboratory Junior Professional Title Examination, and to compare their performance with that of interns. Methods: Four examination papers comprising a total of 3,879 questions were independently administered to both models in three repeated sessions. Accuracy rates were recorded, and reproducibility was assessed. Performance was further compared across three question types and five disciplines. In addition, 46 final-year interns were recruited, and their accuracy rates were compared with those of the two models. Results: Neither model showed significant differences in accuracy across the three repeated sessions ( Conclusion: Both Deepseek-R1 and ChatGPT-5.4 Thinking demonstrated strong performance and reproducibility in the Medical Laboratory Junior Professional Title Examination. Deepseek-R1 showed superior overall accuracy and greater disciplinary consistency. Both models outperformed fourth-year interns, highlighting their potential as auxiliary tools for examination preparation, though the use of publicly available historical questions limits conclusions about genuine reasoning ability.