Evaluation of GPT-5.6 Models on the 2025 Chinese Licensed Pharmacist Qualification Examination, Including Image-Containing Questions
This study evaluates three GPT-5.6 models on the 2025 Chinese Licensed Pharmacist Qualification Examination, finding that all models passed every subject with GPT-5.6-Sol and GPT-5.6-Terra significantly outperforming GPT-5.6-Luna, while noting that performance on image-containing questions was generally lower and potentially influenced by the provision of original image inputs.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
In the rapidly evolving landscape of artificial intelligence, a new generation of computer programs has emerged that can understand and generate human language with remarkable fluency. These systems, known as large language models, are trained on vast amounts of text, allowing them to answer questions, write stories, and solve problems that once required human expertise. Recently, these tools have begun to see images as well as words, gaining the ability to interpret charts, diagrams, and photographs alongside text. This multimodal capability is crucial for real-world applications, where information is rarely presented in text alone. However, a critical question remains: can these sophisticated systems truly understand the complex visual and textual information required in high-stakes professional fields, such as pharmacy? The answer matters not just for technology enthusiasts, but for the future of medical education and professional certification, where the ability to correctly interpret a drug label, a chemical structure, or a medical image can have direct consequences for patient safety.
To investigate this, researchers at Shantou University and affiliated hospitals in China set out to test three specific versions of a powerful new artificial intelligence model, designated as GPT-5.6, against the 2025 Chinese Licensed Pharmacist Qualification Examination. This is a rigorous national test that pharmacists must pass to practice legally in China. The exam covers four distinct subjects, ranging from professional pharmaceutical knowledge to laws and regulations, and requires a score of at least 60 percent in each section to pass. The researchers were particularly interested in how the models handled questions that included images, a common feature in medical testing that many previous studies had ignored. They wanted to see if the models could pass the exam, if different versions of the same model family performed differently, and whether providing the actual image files to the computer improved its answers compared to simply telling the computer that an image existed.
The team constructed a dataset using a widely used study guide for the 2025 exam, which contained 400 questions in total, with 100 questions from each of the four subjects. They carefully extracted the text, the correct answers, and the images, organizing them so that the artificial intelligence could be tested under controlled conditions. The study focused on three specific variants of the GPT-5.6 model, named Sol, Terra, and Luna. These models were fed the exam questions one by one, acting as a senior pharmacist who must choose the correct answer from a list of options. For the questions containing images, the researchers ran two types of tests: one where the computer received the actual image file along with the text, and another where the computer received only the text and a placeholder note indicating where an image should be, without the visual data itself. This allowed the researchers to measure exactly how much the visual information helped the models solve the problems.
The results showed that all three models were capable of passing the entire examination, achieving scores well above the 60 percent threshold required for a license in every single subject. However, not all models performed equally. The GPT-5.6-Sol and GPT-5.6-Terra models significantly outperformed the GPT-5.6-Luna model. The Sol version achieved an overall accuracy of 91.5 percent, while Terra scored 89.0 percent. In contrast, Luna scored 83.3 percent. While all three passed, the statistical analysis confirmed that the difference between the top two performers and the Luna model was not due to chance. The researchers also found that the models generally performed better on questions that contained only text compared to those that included images. For the Sol model, the accuracy dropped by more than 15 percentage points when moving from text-only questions to those with images. This suggests that while these models are highly capable, interpreting visual information remains a more challenging task for them than processing written words.
When the researchers examined the impact of providing actual images versus just text placeholders, they observed a clear trend: the models answered more correctly when they could actually see the images. For the Sol model, accuracy on image-containing questions rose from 66.7 percent without images to 81.0 percent with images. Similar improvements were seen for the other two models, with Terra and Luna both showing roughly a 19 percentage point increase when the visual data was included. However, because the number of image-based questions in the exam was relatively small, the researchers noted that these specific improvements did not reach the strict level of statistical certainty required to be called definitive proof. The study also distinguished between images that appeared in the question itself and those that appeared in the answer choices. The data suggested that images in the answer options were particularly important, as the models struggled more when these visual clues were missing, though the small sample size prevented a firm conclusion on this specific point.
The study concludes that while these advanced artificial intelligence models have reached a level of proficiency where they can pass a demanding professional pharmacy exam, their performance is not uniform across all model variants, and they still face challenges with visual data. The researchers emphasize that the models' ability to pass the exam does not mean they are ready to replace human pharmacists or to be used as the sole tool for medical assessment without further validation. Instead, the findings suggest a potential future where these tools could support human professionals by helping to create personalized training programs, identifying knowledge gaps, and assisting in the preparation for certification exams. The research highlights that for artificial intelligence to be truly effective in complex medical fields, it must be able to seamlessly integrate both text and visual information, a capability that is improving but still requires refinement.
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