Qdrant tests Perplexity's PDF embeddings: 21× smaller vectors, 96.5% recall kept
jrxna · reddit · 2026-10-17
Qdrant benchmarked Perplexity's new visual document embeddings (which search pages as images, preserving tables and layout) on 11,609 pages across four ViDoRe v3 datasets. Findings: 1-bit TurboQuant shrinks compressed vectors from 856 KB to 40 KB per page (21×) while keeping 96.5% of the float32 retrieval score; rescoring against originals recovers 99.9%. The catch: 40 KB excludes the original vectors Qdrant still stores, and the million-page figures are projections, not benchmarks. Open question: when does the compression-vs-rescoring tradeoff stop being worth it?
Related event: Qdrant Tests Perplexity's Visual Document Embedding Model(2 posts)→
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