Zero of fourteen models named Google Ads Data Hub first on the direct prompt; zero named Decentriq. Google Ads Data Hub was named by twelve of the fourteen models and Decentriq by twelve and both carry 28 labels, so the shares below are directly comparable.
By Google, Mountain View, California, United States, founded 1998. Named in one category this edition.
Named in one category this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the data clean rooms page.
Across every category in the October 2026 Edition, Google Ads Data Hub and Decentriq were named in the same answer twenty times, of the 72 answers naming Google Ads Data Hub and the 87 naming Decentriq. In those answers Decentriq took the first choice one time and Google Ads Data Hub three.
| Model | DirectGA | ParaphraseGA | ComparativeGA | Budget-constrainedGA | Scale-constrainedGA | NegativeGA |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | GA | |||||
| GPT-5.4 mini | GA | |||||
| Gemini 3.5 Flash | GA | GA | GA | GA | ||
| Perplexity Sonar | ||||||
| Grok 4.1 Fast | GA | |||||
| Mistral Small | ||||||
| DeepSeek V4 Flash | GA | GA | ||||
| Llama 4 Maverick | ||||||
| Qwen 3.7 Flash | GA | |||||
| Kimi K2 | GA | |||||
| GLM 4.7 FlashX | GA | GA | GA | |||
| MiniMax M2.5 | GA | |||||
| GPT-6 Luna | GA | GA | ||||
| Muse Glimmer 30B | GA | GA |
Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Platform-owned environments such as Google Ads Data Hub, Amazon Marketing Cloud and Meta’s PET-based collaborations are widely adopted but raise lock-in, portability and conflict-of-interest questions.” Muse Glimmer 30B · negative prompt · soft negative
“Google Ads Data Hub and Amazon Marketing Cloud can be useful for analysis within their respective ecosystems, but don’t assume they provide a neutral view across all media.” GPT-6 Luna · negative prompt · soft negative
“Walled garden DCRs are incredibly powerful, but only within their own ecosystems. They are plagued by severe vendor lock-in.” Gemini 3.5 Flash · negative prompt · soft negative
“Crawl (The Low-Risk Start): Start with walled gardens. If 60% of your ad budget goes to Google or Amazon, set up Google Ads Data Hub or Amazon Marketing Cloud.” Gemini 3.5 Flash · scale prompt · first choice
“start with the free walled-garden options (Google, Amazon, Meta) depending on where you advertise most” MiniMax M2.5 · budget prompt · first choice
“If ad-focused, begin with free walled-garden DCRs (AMC/Google)—they solve 80% of needs without spend.” Grok 4.1 Fast · budget prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“positioned more for regulated or security-heavy use cases, which usually makes it less budget-oriented” Perplexity Sonar · budget prompt · soft negative
“these enterprise-grade platforms are almost always a classic case of over-engineering” Gemini 3.5 Flash · direct prompt · soft negative
“like LiveRamp (Habu), InfoSum, or Decentriq is rarely feasible” Gemini 3.5 Flash · budget prompt · soft negative
“neutral, pure-play DCR vendors with hardware-backed privacy ... These include: Decentriq (confidential computing/hardware-backed)” DeepSeek V4 Flash · negative prompt · first choice
“Optable or Decentriq are commonly cited data clean room options” Muse Glimmer 30B · paraphrase prompt · first choice
“I'd suggest starting with either Decentriq or InfoSum” MiniMax M2.5 · paraphrase prompt · first choice
Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.