Qdrant benchmarks rerankers across 5 datasets: confirmed gains on only 2, tune fusion first
qdrant_engine · x · 2026-10-27
Qdrant's new guide answers whether your search pipeline actually needs a reranker. First measure the headroom: score your candidate set as if perfectly ordered and compare with today's nDCG@10 — at 200 candidates the gap spans 0.247–0.487 across five datasets.
- Empirically, reranking beat tuned fusion on only 2 of 5 datasets; on WANDS, tuned fusion won
- The chart shows CodeSearchNet and DBpedia-entity gain most (+0.11–0.14) as candidate count grows from 10 to 200, while ArguAna and WANDS sit at or below zero
- Recommendation: test with 10 candidates first and validate the gain before paying reranker inference cost
- Includes a 'Decide whether to rerank' agent prompt built on Qdrant's search-quality skills to automate the call
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