Grape finds the answer in milliseconds and gives your LLM only what it needs.
Vector RAG struggles with questions in another language, and it has to embed every change again. Grape does neither.
Grape rewrites a question into the language of your documents and searches again. An English embedding model finds almost nothing.
28 / 28Grape
vs 5 / 28 Vector RAG
Gujarati questions answered from English documentsVector RAG embeds a changed document again before it can be found. Grape re-indexes it in a tenth of a second.
0.1 sGrape
vs 97.7 s Vector RAG
Until a changed document is searchableMeasured on 300 long Wikipedia articles, 18 MB of text.
The index for 18 MB of text, next to the embeddings Vector RAG stores for the same text. Both keep the text as well.
12.4 MBGrape
vs 34.8 MB Vector RAG
Index for 18 MB of Wikipedia textGrape indexes large collections as they arrive. Vector RAG spends 46 minutes of CPU embedding the same articles.
1.1 sGrape
vs 46 min Vector RAG
Time to index 18 MBThe same documents, the same questions and the same LLM. Every number comes from one benchmark run.
28/28Grape
vs 24/28 Vector RAG
5 of 5 off-topic questions refused0.09 sGrape
vs 97.7 s Vector RAG
1,146x faster1,203Grape
vs 1,307 Vector RAG
8% fewer$0.00146Grape
vs $0.00155 Vector RAG
6% cheaper1.11Grape
vs 1.0 Vector RAG
11% more3.7 sGrape
vs 3.0 s Vector RAG
26% slower28 questions from grape-bench run fresh-20260930-162311: Claude haiku, thinking off, Anthropic list prices, the same documents and questions for both. Every question and answer is in the full report, and the summary is on the Benchmark page.
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