Zero of fourteen models named Meta Advanced Analytics first on the direct prompt; zero named LiveRamp Clean Room. Meta Advanced Analytics was named by twelve of the fourteen models and LiveRamp Clean Room by thirteen and Meta Advanced Analytics carries 26 labels and LiveRamp Clean Room 35, so the shares are not directly comparable.
By Meta Platforms, Menlo Park, United States, founded 2004. Named in one category this edition.
By LiveRamp, San Francisco, United States, founded 2011. 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 November 2026 Edition, Meta Advanced Analytics and LiveRamp Clean Room were named in the same answer thirty times, of the 57 answers naming Meta Advanced Analytics and the 101 naming LiveRamp Clean Room. In those answers LiveRamp Clean Room took the first choice two times and Meta Advanced Analytics three.
| Model | DirectMA | ParaphraseMA | ComparativeMA | Budget-constrainedMA | Scale-constrainedMA | NegativeMA |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | MA | |||||
| GPT-5.4 mini | ||||||
| Gemini 3.5 Flash | MA | |||||
| Perplexity Sonar | MA | |||||
| Grok 4.20 | MA | |||||
| Mistral Small | MA | |||||
| DeepSeek V4 Flash | MA | MA | MA | |||
| Llama 4 Maverick | MA | MA | ||||
| Qwen 3.7 Flash | MA | |||||
| Kimi K2 | MA | MA | ||||
| GLM 4.7 FlashX | MA | |||||
| MiniMax M2.5 | ||||||
| GPT-6 Luna | ||||||
| Muse Glimmer 30B | MA | MA |
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.
“What to approach with caution: Native clean rooms operated by major media platforms (e.g., Google Ads Data Hub, Amazon Marketing Cloud, Meta Advanced Analytics)” Gemini 3.5 Flash · negative prompt · soft negative
“Walled Gardens: Google, Meta, Amazon—if most of your spend is there, but they limit cross-platform insights.” Grok 4.20 · scale prompt · soft negative
“Meta's history of data misuse is repeatedly cited as a reason to be cautious with its clean room offerings.” Muse Glimmer 30B · negative prompt · soft negative
“likely to be a free walled-garden option such as Google Ads Data Hub, Amazon Marketing Cloud, or Meta Advanced Analytics” Llama 4 Maverick · budget prompt · first choice
“Google, Amazon, and Meta each run a clean room that costs nothing in licence fees” Claude Haiku 4.5 · budget prompt · first choice
“Meta Advanced Analytics (currently in beta)” Mistral Small · budget prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Avoid Standalone SaaS Clean Rooms Initially... the $50k–$100k+ annual licensing fee for neutral enterprise tools (e.g., LiveRamp, InfoSum) won't deliver a positive ROI.” Gemini 3.5 Flash · budget prompt · hard negative
“What to Avoid - LiveRamp / Snowflake Data Clean Rooms: These are powerful but enterprise-grade in pricing and complexity.” DeepSeek V4 Flash · paraphrase prompt · hard negative
“Why Avoid Traditional Vendors Right Now? ... these are likely out of budget for a limited-fund operation.” Qwen 3.7 Flash · budget prompt · hard negative
“LiveRamp and OneTrust are the top recommendations due to their strong privacy safeguards, scalability, and industry adoption” Mistral Small · paraphrase prompt · first choice
“Top Recommendation: LiveRamp ... It is the industry standard for identity resolution and secure data exchange.” Qwen 3.7 Flash · paraphrase prompt · first choice
“For broad collaboration across ecosystems: LiveRamp or InfoSum.” Grok 4.20 · comparative 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.