Which LLM is Best for RAG and to retrieve info from Large PDFs? Gemini, GPT-4, Claude, or Local LLM?

In this video, I test and compare several LLM (Large Language Model) AI models on their ability to retrieve accurate information from extremely large PDF files — some up to 1,000 pages in length. Using both RAG (Retrieval-Augmented Generation) and MCP (Multi-Context Prompting) methods, I evaluate top-tier models such as Gemini 2.5 Pro, Gemini Flash, Claude 3, GPT-4.1, and GLM 4, as well as several smaller and local models including LLaMA 3, Mistral, Qwen, and more. The test uses a specific passage (Box 6.2 from the 2025 HDR Report) as a retrieval benchmark across models. I measure: Retrieval accuracy (precision and recall), Token usage and response speed, Resource usage on my HP G1A Ryzen AI Max+ Pro 395 laptop. 💡 Watch to find out which models perform best in high-load, context-heavy RAG environments — and which models hallucinate, underperform, or impress. If you're interested in deeper testing (e.g. combining RAG with MCP or writing back to files), let me know in the comments! 👍 Like, 💬 comment, and 🔔 subscribe for more model comparisons and AI performance tests.