Efforts to automate LLM generations which further instructed a robotic synthesizer have been accomplished by White Laboratory's ChemCrow to foster innovation in the fields of organic synthesis, drug discovery, and materials . Some synthetic generations were logical for automated synthesis to occur, but results were limited to an older GPT-4 model acting as an evaluator, control, and the primary model in agentic workflows . Alternatively, to focus on AI performance with less steps in a more explainable way, recent more capable LLMs or LMMs using Retrieval Augmented Generation (RAG) or Document Retrieval (DR) workflows have been employed. Both RAG and DR methods have been tested extensively by the author, with the LMM-DR workflow being preferred due being readily available and producing more innovative and accurate results . Therefore, a more thorough investigation of LMM-DR workflows on a variety of images and text was needed to update the definition of chemical research using natural language processing. Specifically, the purpose of this study was to reveal exact LMM performance on a variety of characterization and synthetic chemistry problems with fine grain detail that are significantly faster than researchers. The two judges chosen for this study have both shown higher performance vs. other LLMs performing on a variety of tasks . Explainability was also achieved than previous studies due to the ability to identify context directly from documents and text.