GraphRAG vs. Vector RAG: Where the Accuracy Gap Actually Comes From

The comparison between GraphRAG and vector RAG gets framed as a philosophical debate more often than an empirical one. Proponents of each approach tend to argue from first principles: graphs are better for structured knowledge, vectors are better for semantic similarity, and so on.

The empirical picture is less ambiguous. On GraphRAG-Bench (Xiang et al., ICLR 2026), the FalkorDB GraphRAG SDK scores 71.48 overall against 55.39 for the vector-RAG baseline with reranking. That’s a 16.09-point absolute gap, or roughly 29% relative improvement, using identical generation models and judges. The question worth answering isn’t whether the gap exists. It’s where it comes from and when it matters.

What GraphRAG-Bench Actually Measures

GraphRAG-Bench evaluates retrieval systems across four task types, each designed to stress a different capability of the retrieval layer:

The full scorecard for FalkorDB GraphRAG SDK 1.3.0 versus the vector-RAG baseline:

The gap is largest on Fact Retrieval (+17.74 points) and Contextual Summarization (+14.44 points). These are exactly the task types where entity resolution and relationship traversal matter most, and where vector search’s approximation model breaks down fastest.

Why the Gap Is Largest on Fact Retrieval

Fact retrieval sounds like vector RAG’s strongest suit. Find the chunk most similar to the question, extract the fact. In practice, it breaks down in two ways.

Ambiguous Entity References

If the question asks about “the agreement with Acme,” and your corpus contains 40 documents that mention Acme in different contexts, vector search returns the most semantically similar chunks, which may or may not contain the specific agreement being asked about. A knowledge graph resolves “Acme” to a canonical entity node and retrieves the specific agreement relationship directly.

Attribute Lookups on Specific Entities

“What is the renewal date of the Acme contract?” requires finding a specific node (the Acme contract) and reading a specific attribute (renewal date). Vector search approximates this by finding text that discusses Acme contracts and renewal dates. Graph traversal retrieves the exact node and reads the exact attribute.

The difference is precision versus approximation. Vector search is a similarity engine. A knowledge graph is a lookup engine. For fact retrieval, lookup wins by 17.74 points.

Why the Gap Persists on Contextual Summarization

Contextual summarization requires synthesizing information across multiple sources. Vector RAG retrieves the top-k most similar chunks and passes them to the generation model. The model synthesizes across whatever was retrieved.

The problem: top-k retrieval optimizes for similarity to the query, not for coverage of the relevant knowledge subgraph. If the question requires information from five related entities, and three of those entities don’t appear in the top-k results, the summary will be incomplete. The model can’t synthesize what it wasn’t given.

Graph traversal starts from a query entity and expands outward through relationships, collecting all connected nodes relevant to the question. This naturally produces broader, more complete context for summarization tasks.

The structural reason: vector retrieval is query-centric (find what’s similar to this). Graph retrieval is entity-centric (find everything connected to this). For summarization, entity-centric wins because completeness matters more than similarity.

This is why GraphRAG’s advantage on Contextual Summarization (+14.44 points) is nearly as large as its advantage on Fact Retrieval, despite summarization being a task where you might expect vector search’s breadth to help.

Where Vector RAG Holds Its Ground

The benchmark also shows where vector RAG remains competitive. On Complex Reasoning and Creative Generation, the gap narrows.

Complex Reasoning (multi-step inference) is an area where both approaches struggle, because the difficulty lies in the reasoning chain, not just the retrieval. A graph traversal retrieves the right facts; the model still has to reason across them correctly. Vector RAG retrieves noisier context but sometimes surfaces the right facts by proximity.

Creative Generation shows the smallest gap because the task is least dependent on retrieval precision. When the goal is open-ended generation grounded in general context, the quality difference between graph-retrieved and vector-retrieved context diminishes.

The practical implication: if your application is primarily creative generation or open-ended Q&A over general knowledge, the architectural overhead of a knowledge graph may not be justified. If your application involves specific entity lookups, compliance questions, multi-source synthesis, or relationship traversal, the benchmark gap reflects a real difference in production accuracy.

Most enterprise AI applications fall into the second category. Compliance questions, contract analysis, threat intelligence, regulatory research, and knowledge management all involve specific entities, defined relationships, and auditability requirements.

The Architecture Behind the Gap

The accuracy difference isn’t magic. It comes from three concrete architectural choices that separate GraphRAG from vector RAG at the retrieval layer.

Canonical Entity Storage

In a knowledge graph, each entity exists once, as a canonical node with defined attributes and relationships. In a vector store, the same entity may appear in hundreds of chunks with slightly different representations, phrasings, and levels of detail. At query time, graph retrieval finds the entity; vector retrieval finds chunks that mention the entity. For fact-heavy questions, that distinction determines whether the answer is right or wrong.

Relationship-Aware Traversal

Graph queries explicitly traverse relationships between entities. “Find all vendors flagged in the compliance database who supply components to active product lines” is a native graph query that executes in a single traversal. It’s an approximation problem for vector search: retrieve chunks about vendors, chunks about compliance flags, chunks about product lines, and hope the model reasons correctly across three separate retrieval passes.

This is consistent with Microsoft’s GraphRAG research (Edge et al., 2024), which found graph-based retrieval achieved 86% comprehensiveness on complex multi-entity queries versus 57% for traditional vector RAG. The gap grows as query complexity increases.

Structured Context Assembly

When a graph traversal completes, it returns a structured subgraph: specific nodes, specific attributes, specific relationship paths. The generation model receives precise, structured context with explicit provenance. When vector retrieval completes, it returns a ranked list of text chunks. The model receives unstructured text that may or may not contain the relevant information, with no explicit relationship structure to reason over.

These three differences compound on complex queries. Simple lookups show a modest gap. Multi-hop, multi-entity questions show a large one.

Choosing the Right Architecture

The decision between vector RAG and GraphRAG isn’t binary, and it isn’t permanent. Most production systems will use both: vector search for broad semantic retrieval over unstructured text, graph traversal for precise entity lookup and relationship reasoning. The question is which layer does the primary retrieval work for your specific query types.

Use Vector RAG as the Primary Layer When:

• Queries are primarily semantic (“find documents similar to this topic”)
• The knowledge base is unstructured and entity relationships aren’t well-defined
• Precision on specific entity attributes isn’t critical
• The application is creative or exploratory rather than factual

Use GraphRAG as the Primary Layer When:

• Queries require specific entity lookups or attribute retrieval
• Answers must be traceable to specific sources with explicit provenance
• Multi-hop reasoning across related entities is common
• The knowledge base has well-defined entities and relationships
• Access control must be enforced at the entity level
• Agent memory needs to persist and accumulate across sessions

For most enterprise AI applications, the query mix skews toward the second category. The benchmark gap reflects that reality: a 16.09-point overall advantage on the same model, the same judge, and the same evaluation set.

The FalkorDB GraphRAG SDK is designed for this query mix: graph-native retrieval for relationship-heavy questions, with vector search available in the same engine for semantic lookups. The full benchmark methodology, datasets, and reproduction steps are available at the FalkorDB GraphRAG accuracy benchmark.