Prompts with images or document uploads both typically returned a result within an average of 5 seconds. The results above indicate a high proficiency using the 3.5 Sonnet model to answer specific questions regarding several uploaded images from recent leading journals. Characterization related Prompts 01, 02, 03, and 05 based on spectroscopic techniques were answered with high proficiency. In specific, 3.5 Sonnet was able to identify 1H and 13C spectra shown in Figure , along with a number of trNOESY protons interactions through space correctly in Figure . The LMM average score of 9.8 missed a perfect score due to not lacking generation detail of impurity peaks for NL 74 Azepane in Prompt 05. The two other models: ScholarGPT and ChatGPT 4o could only generate a high level of generation accuracy for Prompt 04, which was the only synthesis related question in this set of image prompts to convert compound S11 to EBL-3025 in Table . Overall, all three LMMs were able to provide a general generation context for each of the 15 prompts, but ScholarGPT and ChatGPT 4o were not consistent in providing specific details along with the correct image identifications. The lower scores for these two GPT models for this task may be due to insufficient training data regarding interpreting chemical spectra.