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If you’ve ever asked yourself, “Does GraphRAG really outperform vanilla RAG — and by how much?”, you’re not alone. It’s a question that’s been floating around among devs and researchers alike, especially those working on RAG tasks.

A recent study dives right into this exact question, using a focused and rigorous setup: textbook-level retrieval QA, page by page.

It used the undergraduate math textbook “An Infinite Descent into Pure Mathematics” as their dataset. After OCR processing using the GPT Vision model, they created a custom benchmark of 477 samples, which were manually reviewed and filtered down from an initial set of 628. Each consisting of a question, answer, and the specific textbook page it’s based on.

RAG Settings

For the baseline RAG, It tested five popular embedding models (think: voyage-3-large, nvidia/nv-embed-v2, and others). On the other hand, GraphRAG was built to leverage inter-page relationships—essentially modeling how concepts flow across pages to support richer retrieval context.

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Figure 1: A representative diagram of RAG pipeline. [Source].

Figure 1 is a quick breakdown of how the RAG pipeline works, illustrated through a simple three-step process:

1. Indexing. First, the source documents are either embedded into vectors or structured as relational entities when using GraphRAG. This…