Two of fourteen models named AWS Clean Rooms first on the direct prompt; one named Databricks Clean Rooms. AWS Clean Rooms was named by fourteen of the fourteen models and Databricks Clean Rooms by thirteen and AWS Clean Rooms carries 48 labels and Databricks Clean Rooms 26, so the shares are not directly comparable.
Named in two categories this edition.
By Databricks, San Francisco, United States, founded 2013. 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, AWS Clean Rooms and Databricks Clean Rooms were named in the same answer fifty-eight times, of the 154 answers naming AWS Clean Rooms and the 74 naming Databricks Clean Rooms. In those answers Databricks Clean Rooms took the first choice zero times and AWS Clean Rooms ten.
| Model | DirectAC | ParaphraseAC | ComparativeAC | Budget-constrainedAC | Scale-constrainedAC | NegativeAC |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | AC | AC | ||||
| GPT-5.4 mini | AC | AC | AC | AC | ||
| Gemini 3.5 Flash | AC | AC | AC | |||
| Perplexity Sonar | AC | AC | AC | |||
| Grok 4.20 | AC | AC | AC | AC | AC | |
| Mistral Small | AC | AC | ||||
| DeepSeek V4 Flash | AC | AC | AC | AC | AC | AC |
| Llama 4 Maverick | AC | |||||
| Qwen 3.7 Flash | AC | AC | ||||
| Kimi K2 | AC | AC | ||||
| GLM 4.7 FlashX | AC | |||||
| MiniMax M2.5 | AC | AC | AC | |||
| GPT-6 Luna | AC | AC | AC | AC | ||
| Muse Glimmer 30B | AC | AC |
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. Four of five in this category shown.
“AWS Clean Rooms charges for query compute, so budget for usage rather than assuming it's a free tool” GPT-6 Luna · budget prompt · soft negative
“AWS Clean Rooms or Snowflake Data Clean Rooms offer the best balance of capability, scalability, and cost-effectiveness for mid-market B2B companies” Kimi K2 · direct prompt · first choice
“Prefer neutral, interoperable platforms (like Snowflake, Databricks, AWS Clean Rooms, InfoSum, or independent providers)” DeepSeek V4 Flash · negative prompt · first choice
“Best for AWS Users: AWS Clean Rooms ... Pay-as-you-go (no upfront platform fees or monthly SaaS subscriptions)” Gemini 3.5 Flash · 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.
“Databricks requires data to live in Databricks storage or an accessible cloud bucket, which can add setup complexity” GPT-5.4 mini · budget prompt · soft negative
“Strong for advanced analytics if you're in that ecosystem, but steeper learning curve for mid-market teams.” Grok 4.20 · direct prompt · soft negative
“more accessible than heavy engineering-heavy options like raw Databricks or AWS Clean Rooms” Grok 4.20 · paraphrase prompt · soft negative
“Databricks Clean Rooms is generally the most flexible for mid-size companies needing cross-cloud, privacy-policy governed collaboration with full audit logs.” Muse Glimmer 30B · paraphrase prompt · first choice
“Prefer neutral, interoperable platforms (like Snowflake, Databricks, AWS Clean Rooms, InfoSum, or independent providers)” DeepSeek V4 Flash · negative prompt · first choice
“start with your primary Cloud Data Warehouse's native DCR (typically Snowflake DCR or Databricks Clean Rooms)” Gemini 3.5 Flash · direct 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.