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Buying question

Create a proof-of-concept scorecard for evaluating GPU clouds on real workload throughput, failure recovery, networking, 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 17, 2026. Each category is re-run in rotation, every few weeks.

Commercial

Kind of question

3

Model APIs

2

Brands named

4

Pages cited

Brands

Brands the answers named

Rank#BrandModel APIsMentionsOwn-site citationsNamed by
1Google Cloudcloud.google.com101Google
2Lambda Cloudlambda.ai101Google

Model APIs

What each model API returned

Model APIModelBrands namedCitationsDomainsAnswer hash
OpenAIopenai/gpt-4o-miniNone named00fc834653…484b7
Anthropicanthropic/claude-haiku-4-5None named0078741630…32d88
Googlegoogle/gemini-2.5-flashLambda Cloud · Google Cloud4447480766…d424c

Sources

Pages the answers cited

Snapshots
83bdc888-6ae7-4cb1-8b8d-c037b9aa2951
Question set
cloud-gpu-platforms-2026-07-27
Benchmark version
4-cloud-gpu-platforms-2026-07-27-models-64232fd32583
What is kept
Measured in GPU clouds. 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.