Buyer prompt · published evidence

How should an engineering team evaluate vector search benchmark claims for recall, tail latency, throughput, scale, and total cost?

An exact prompt-level view of which reviewed brands surfaced and which public URLs the configured AI models cited. No answer text or tenant data is published.

Intent
commercial
Model providers
3
Categories
1
Last observed
Aug 4, 2026, 12:00 AM UTC
Competitive outcome

Brands surfaced

Ordered by provider breadth, then total mentions and owned-domain citations. A mention is not an endorsement or a position claim.

#BrandCategoryProvidersMentionsOwned citationsProvider evidence
01Qdrantqdrant.techVector DBs101Openai
Model comparison

Answer matrix

One row per configured provider and category snapshot. Hashes prove answer identity without publishing stored answer text.

ProviderModel versionCategoryBrands surfacedCitationsUnique domainsAnswer hash
Openaiopenai/gpt-4o-miniVector DBsQdrant6694e99e07…1f4eb
Anthropicanthropic/claude-haiku-4-5Vector DBsNone observed0040afc1af…9f745
Geminigoogle/gemini-2.5-flashVector DBsNone observed6618d8fe68…c3c37
URL-derived citations

Sources cited

Hostnames are derived from the returned public URLs. Best position is the smallest citation position observed.

Snapshot IDsb124696f-f8e4-4b21-8539-c7fd45268eeb
Corpus versionsvector-database-platforms-2026-07-27
Benchmark versions4-vector-database-platforms-2026-07-27-models-64232fd32583

Coverage: Vector DBs. Full answers stored: no · Full answers published: no · Tenant data included: no.