For prompts regarding the uploading of supplementary documents from journals, LMM performance experienced an opposite trend from that experienced of the first five prompts. In specific the two LLM Judges: Llama 3.1 405B and Nemotron 4 340B gave low average scores to 3.5 Sonnet responses (7.7, 6.8), while ChatGPT 4o experienced the highest scores (9.7, 9.4), followed closely by ScholarGPT (9.5, 9.4) shown in Figure and Figure . The close proximity of these scores was reflected by the correlation coefficient (Pearson's r) value of 0.64. The lower 3.5 Sonnet score may be tied to issues processing uploading documents over 40K words, despite the context window of the model being 200K tokens . Llama 3.1 405B responses were more organized and always gave a single decimal place as specified in the prompt. Both judges showed the capability to generate highly detailed answers, such as when responses were less relevant from Prompt 08 by 3.5 Sonnet. Both ScholarGPT and ChatGPT 4o LMMs performed excellent at the synthesis related prompt 06 and prompts regarding instrumentation and spectroscopy text. The methods described in this paper are explainable in that responses referring to context from image uploads were visually verified by a human. Selections of text in documents provided by LMMs were also readily interpretable by a human by searching for the text in original documents. Less bias was achieved with the use of LLMs from two different manufacturers as well as different LMM manufacturers vs. previous approaches. The LLM judges also came to a collective agreement regarding results across the three LMMs.