Head-to-head
Elasticsearch vs Qdrant
Elasticsearch leads: the AI models rank it above its rival on 1 of the 1 leaderboard they share. Based on how ChatGPT, Claude, Gemini & Grok rank both across the leaderboard they share — re-polled weekly, reasoning shown verbatim.
| Leaderboard | Elasticsearch | Qdrant |
|---|---|---|
| Best vector databases for hybrid semantic and keyword search | #2 / 7 | #3 / 7 |
Why the models rank Elasticsearch — on best vector databases for hybrid semantic and keyword search
“The industry standard for traditional search, integrating a world-class BM25 engine with dense vectors via its native Retriever API and Reciprocal Rank Fusion (RRF). Note: OpenSearch is a near-tie here, but Elasticsearch edges it out with faster release cycles for native hybrid-search query features.”
Why the models rank Qdrant — on best vector databases for hybrid semantic and keyword search
“First-class hybrid retrieval in the core engine — named dense + sparse vectors (BM25-style and learned sparse like SPLADE/miniCOIL) fused server-side via RRF/DBSF in a single Query API call, so no client-side result stitching; Rust core delivers strong latency/recall per dollar, quantization and on-disk options keep costs down, and Apache-2.0 self-host plus a fairly priced cloud make it the best default value for the typical RAG/search practitioner in 2026”
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Ranks from the merged 4-model leaderboards · re-polled weekly · methodology