The 3 LLM speed advantages featured a) real-time search vs. author search, b) model vs. author 10 paper summaries, and c) model vs. author full paper review summaries. Each model was run in triplicates in part a), with averaged multiplier speeds ranging from 87-190x faster than the manuscript author performing a similar task. The search feature for ChatGPT-4oS yielded helpful insights regarding 2024 mAb bioprocess engineering kinetics breakthroughs found in Frontiers in Biotechnology with 15 total references, but were not yet detailed enough for use in subsequent sections of this study. Perplexity models PGPT-4o and P3.5Sonnet performed at lower multiplier speeds and typically returned less Frontier paper references using the same prompt. The Cu3.5Sonnet "computer use" service by Claude.ai utilizing 3.5 Sonnet was slow and less helpful, but provided screen shots of progress for each of its agentic steps leading up to the main Frontiers in Biotechnology page before timing out. Further attempts to optimize processing variables through Docker Desktop to increase speed and prevent timeout errors did not improve results. LLM speed advantage b) was built upon two previous manuscript author works , in which individual papers were summarized with user prompts to find breakthroughs in mAb bioprocess engineering kinetics, citing selections of original author's data in quotes. In general, 3.5 Sonnet and ChatGPT-4o had average multiplier speeds of 60-144x compared to the manuscript author performing a similar task for each of 10 bioprocess engineering papers. The third speed advantage c) focused on the time it took to summarize a bioprocess engineering review paper. 3.5 Sonnet achieved the fastest overall speed multiplier vs. the manuscript author in the study at 666x, while ChatGPT-4o was 411x faster than the author, primarily due to the length of time it took the author to read the entire paper. Additional triplicate LLM answers to prompts identified document search, natural language processing, and other "straightforward" methods were used to process c), with surprisingly less responses attributing other advanced types of processing or machine learning inferencing methods.