which model is the most suitable to run a RAG model for few pdf books

GPT-5.4 (changed mind)

Answer: For a RAG system over a few PDF books, a small-to-mid-sized model remains the most suitable choice, but I would refine my earlier answer to favor a practical 8B-class model like Llama 3.1 8B Instruct or Qwen2.5 7B/14B for local deployment, or a compact hosted model like GPT-4.1 mini if you prefer API simplicity.

I changed my position slightly because the strongest point made by the other responses is that the best answer depends not just on answer quality, but also on deployment style. My earlier answer focused on a hosted default, which is still reasonable, but Claude Opus 4.7 made the more complete case for small-scale RAG by tying model choice to the likely constraints of a "few pdf books": low corpus size, limited need for a very large model, and the practical benefits of local, inexpensive inference. What convinced me most is the argument that for a small RAG corpus, retrieval does most of the heavy lifting, so the generator mainly needs to follow instructions, stay grounded in retrieved chunks, and synthesize clearly. That makes an 8B-class instruct model a very strong fit. "Llama 3.1 8B Instruct" and "Qwen2.5 7B Instruct" are especially compelling because they are capable enough for grounded QA, cheap to run, and easy to integrate with standard RAG tooling. I do not fully switch to the view that a premium model like "Claude 3.5 Sonnet" is generally the single most suitable answer. Gemini 3.1 Pro is right that stronger proprietary models can improve reasoning and long-context synthesis, especially for messy, technical, or cross-document questions. But for "few pdf books," that is often more capability than necessary, and it comes with higher recurring cost. In most small RAG deployments, that tradeoff is not optimal unless the documents are unusually difficult. I also would not endorse a single model family without qualification. Grok 4.20 is directionally right about "Llama 3," but the recommendation is less precise because context window, instruction tuning quality, and ease of deployment matter a lot. Newer variants like "Llama 3.1 8B Instruct" or strong alternatives like "Qwen2.5 7B/14B Instruct" are more actionable recommendations. The key point remains that the generation model is only one part of the system. PDF extraction quality, chunking, embeddings, retrieval, and optionally reranking will often matter as much or more than upgrading the LLM. So the most suitable choice is not the biggest model; it is the smallest model that reliably answers from retrieved context. For many users with a few books, that means an 8B-class local instruct model; for users who want the easiest hosted setup, "GPT-4.1 mini" is still a sensible default.

Gemini 3.1 Pro (changed mind)

Answer: For querying a small collection of PDF books, a locally hosted, mid-sized open model like Llama 3.1 8B Instruct is the most practical and suitable choice, eliminating API costs while maintaining privacy.

After reviewing the other responses, I find Claude Opus 4.7's argument highly compelling. My previous recommendation of Claude 3.5 Sonnet and Cohere's Command R, while technically powerful, represents overkill for a project involving only a few PDF books. High-end proprietary models introduce unnecessary API costs, and models like Command R are often too heavy to easily self-host on consumer hardware. For a small-scale RAG system, the primary considerations are cost, privacy, and ease of deployment rather than handling massive, enterprise-level corpora. A smaller open model like Llama 3.1 8B Instruct features a generous 128K context window, which is more than sufficient for processing retrieved chunks and generating accurate answers from a limited document set. Furthermore, it can be comfortably run locally on a standard Mac or a PC with a consumer GPU using tools like Ollama. By pairing Llama 3.1 8B with a highly capable, localized embedding model like BGE-M3, you can build a complete, free, and entirely private RAG pipeline. This setup avoids recurring cloud fees and keeps your documents strictly on your own device, proving to be the most sensible and suitable approach for this specific scale.

Claude Opus 4.7

Answer: For a small-scale RAG system over a few PDF books, a mid-sized open model like Llama 3.1 8B Instruct (or Qwen2.5 7B/14B Instruct) paired with a strong embedding model such as BGE-M3 is still the best balance of quality, cost, and ease of local deployment.

After reviewing the other answers, I'm maintaining my position, but I want to acknowledge valid points from each and refine my recommendation. GPT-5.4's emphasis that the pipeline matters more than the model is absolutely correct — and it aligns with my original point that retrieval does the heavy lifting. But recommending GPT-4.1 mini as the default ignores a key implicit requirement for "a few PDF books": this is almost always a personal/local use case where privacy, one-time cost, and offline capability matter. Paying per-token API fees to query your own books is rarely the right default. Gemini 3.1 Pro makes a strong case for Command R, which is genuinely purpose-built for RAG with native citation grounding — this is a fair point I underweighted. For users who prioritize citation fidelity, Command R is arguably better than vanilla Llama 3.1 8B. However, Command R (35B) needs significantly more VRAM than 8B models, making it less accessible for the typical "few PDFs on a laptop" scenario. Claude 3.5 Sonnet is excellent but is an API service — overkill and non-local. Grok 4.20 reaches essentially the same conclusion I did (Llama 3), but cites an outdated 8k context window. Llama 3.1 (which I recommended) has a 128K context, which materially matters for RAG with book-length sources. So my refined stance: Llama 3.1 8B or Qwen2.5 7B/14B via Ollama remains the best default for most users. If citation accuracy is paramount and hardware allows, Command R is a strong upgrade path — a nuance worth borrowing from Gemini 3.1 Pro.

Grok 4.20 (changed mind)

Answer: Llama 3.1 8B Instruct paired with a strong embedding model like BGE-M3 is the most suitable for running a RAG pipeline on a few PDF books.

After reviewing the other responses, I updated my previous recommendation from Llama 3 to the newer Llama 3.1 8B Instruct because it offers an extended 128K context window that better accommodates retrieved chunks from PDF books without sacrificing local efficiency. This model strikes an excellent balance for small-scale RAG: it excels at instruction following, grounded generation, and can run quantized on consumer hardware like a single GPU or even a laptop with 16-32 GB RAM using tools such as Ollama or llama.cpp. The GPT-5.4 response rightly emphasizes that for a limited corpus of a few books, a mid-tier model avoids unnecessary cost and latency, but I prefer keeping everything self-hosted for privacy reasons common with personal PDF libraries, which rules out ongoing API usage of GPT-4o mini. Gemini 3.1 Pro makes a strong case for Cohere Command R due to its RAG-specific training and citations, yet Llama 3.1 remains more accessible and customizable with the broad ecosystem of LangChain and LlamaIndex. Claude Opus 4.7's detailed breakdown of separating the embedding model (e.g., BGE-M3 for high-quality retrieval) from the generator, along with practical stack advice using Chroma or FAISS, provided the most compelling and immediately actionable insights that refined my stance. For clean or even moderately complex PDFs, this open pipeline delivers high-quality results at zero marginal cost while allowing easy experimentation with chunking and reranking strategies.

Final Summary

The AI Roundtable reached a unanimous consensus that mid-sized open models like Llama 3.1 8B are the most practical choice for personal PDF libraries. While Gemini 3.1 Pro and GPT-5.4 initially favored proprietary APIs, they were swayed by Claude Opus 4.7's argument for the privacy and cost-efficiency of local deployment. Grok 4.20 also upgraded its pick to prioritize the 128k context window, which is essential for handling large text chunks from books.

All 4 models agreed