Skip to content

Buying question

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

Which brands each model API named when asked this question, and which pages it cited. Answer text is not published.
Snapshot Sep 12, 2026. Each category is re-run in rotation, every few weeks.

Commercial

Kind of question

3

Model APIs

2

Brands named

8

Pages cited

Brands

Brands the answers named

Rank#BrandModel APIsMentionsOwn-site citationsNamed by
1MongoDB Atlas Vector Searchmongodb.com101OpenAI
2Zilliz Cloud / Milvuszilliz.com101Google

Model APIs

What each model API returned

Model APIModelBrands namedCitationsDomainsAnswer hash
OpenAIopenai/gpt-4o-miniMongoDB Atlas Vector Search77672a4fa7…7ac60
Anthropicanthropic/claude-haiku-4-5None named00e1a061c9…55d7f
Googlegoogle/gemini-2.5-flashZilliz Cloud / Milvus33c8248eb7…731ee

Sources

Pages the answers cited

Snapshots
794067bc-f4fa-456f-8063-1db7efff0c35
Question set
vector-database-platforms-2026-07-27
Benchmark version
4-vector-database-platforms-2026-07-27-models-64232fd32583
What is kept
Measured in Vector DBs. Full answers are not stored or published, and no customer data is included.

Enter your website. In about two minutes, Rank.ai asks ChatGPT, Claude and Gemini 12 questions your customers ask and grades how often they name you.

  • Your grade out of 100How often AI names you, cites your site, and how high it ranks you.
  • Who gets namedEvery competitor in the answers, most named first.
  • The pages AI readsThe sources behind each answer.
  • Three fixesWhat to fix first, with a brief for the first article.