Zero of fourteen models named Google Ads Data Hub first on the direct prompt; zero named Habu. Google Ads Data Hub was named by twelve of the fourteen models and Habu by seven and Google Ads Data Hub carries 28 labels and Habu 14, so the shares are not directly comparable.
By Google, Mountain View, California, United States, founded 1998. Named in one category this edition.
Named in two categories 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 Habu were named in the same answer twenty-five times, of the 72 answers naming Google Ads Data Hub and the 52 naming Habu. In those answers Habu took the first choice zero times and Google Ads Data Hub two.
| Model | DirectGAHA | ParaphraseGAHA | ComparativeGAHA | Budget-constrainedGAHA | Scale-constrainedGAHA | NegativeGAHA |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | GA | |||||
| GPT-5.4 mini | GAHA | |||||
| Gemini 3.5 Flash | GA | HA | GA | GAHA | GAHA | |
| Perplexity Sonar | ||||||
| Grok 4.1 Fast | GA | |||||
| Mistral Small | HA | HA | ||||
| DeepSeek V4 Flash | HA | HA | GA | GAHA | ||
| Llama 4 Maverick | ||||||
| Qwen 3.7 Flash | GA | |||||
| Kimi K2 | HA | HA | GA | |||
| GLM 4.7 FlashX | GAHA | 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. Five of five in this category shown.
“What to Avoid as a Mid-Market B2B - LiveRamp / Habu: Enterprise-grade” DeepSeek V4 Flash · direct prompt · hard negative
“Only purchase a dedicated independent SaaS DCR (like LiveRamp/Habu) if you have a specific, high-revenue partner usecase lined up” Gemini 3.5 Flash · scale prompt · soft negative
“Top Recommendation: Habu (now LiveRamp Clean Room)” DeepSeek V4 Flash · paraphrase prompt · first choice
“Recognized for its user-friendly interface and ability to handle multiple partnerships and advanced analytics, ideal for mid-market B2B companies.” Mistral Small · direct prompt · alternative
“modern integrations like Habu or InfoSum) that allow partners to query and analyze data directly where it already lives” Gemini 3.5 Flash · negative prompt · alternative
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.