Two of fourteen models named AWS Clean Rooms first on the direct prompt; one named Databricks Clean Rooms. AWS Clean Rooms was named by twelve of the fourteen models and Databricks Clean Rooms by twelve and AWS Clean Rooms carries 42 labels and Databricks Clean Rooms 27, so the shares are not directly comparable.
Named in one category 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 October 2026 Edition, AWS Clean Rooms and Databricks Clean Rooms were named in the same answer forty-six times, of the 142 answers naming AWS Clean Rooms and the 73 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 | ||||||
| GPT-5.4 mini | AC | AC | AC | AC | ||
| Gemini 3.5 Flash | AC | AC | AC | |||
| Perplexity Sonar | AC | AC | AC | AC | ||
| Grok 4.1 Fast | AC | AC | AC | AC | AC | |
| Mistral Small | AC | AC | ||||
| DeepSeek V4 Flash | AC | AC | AC | AC | AC | |
| Llama 4 Maverick | ||||||
| Qwen 3.7 Flash | AC | AC | ||||
| Kimi K2 | AC | AC | ||||
| GLM 4.7 FlashX | AC | AC | AC | |||
| MiniMax M2.5 | 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. Six of seven in this category shown.
“Pay-per-use (CRPU-hours); no free tier; costs can escalate quickly” Kimi K2 · budget prompt · hard negative
“Cloud-based (e.g., AWS Clean Rooms, Snowflake, Databricks) | Strong infrastructure, but integration complexity and variable TCO” Grok 4.1 Fast · negative prompt · soft negative
“Snowflake and AWS Clean Rooms get reasonable marks when properly configured — but watch for the cost/complexity pitfalls” DeepSeek V4 Flash · negative prompt · soft negative
“AWS Clean Rooms is often the safer default because the pricing is usage-based and there's no separate license to buy just to get started” GPT-5.4 mini · budget prompt · first choice
“AWS Clean Rooms is the top recommendation for most small businesses due to its pay-as-you-go pricing and free tier” Mistral Small · budget prompt · first choice
“Already on AWS → AWS Clean Rooms pay-as-you-go is the most budget-friendly entry point, no big upfront investment.” Muse Glimmer 30B · 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.
“Cloud-based (e.g., AWS Clean Rooms, Snowflake, Databricks) | Strong infrastructure, but integration complexity and variable TCO” Grok 4.1 Fast · negative prompt · soft negative
“Some concerns about identity resolution rules not being well-documented” Kimi K2 · negative prompt · soft negative
“user reviews of Databricks Clean Rooms have scored poorly here” DeepSeek V4 Flash · negative prompt · soft negative
“Snowflake, Databricks, and LiveRamp Safe Haven provide balanced scalability and governance support.” Claude Haiku 4.5 · direct prompt · first choice
“Choose Snowflake, Databricks, or AWS if you already have a mature data team” Gemini 3.5 Flash · comparative prompt · first choice
“If your data ecosystem is built on Databricks, their Delta Sharing and Unity Catalog offer a similar, highly secure distributed data clean room experience.” Gemini 3.5 Flash · paraphrase 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.