Six of fourteen models named Snowflake Data Clean Rooms first on the direct prompt; one named Databricks Clean Rooms. Snowflake Data Clean Rooms was named by fourteen of the fourteen models and Databricks Clean Rooms by twelve and Snowflake Data Clean Rooms carries 52 labels and Databricks Clean Rooms 27, so the shares are not directly comparable.
By Snowflake Inc., San Mateo, United States, founded 2012. 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, Snowflake Data Clean Rooms and Databricks Clean Rooms were named in the same answer sixty-eight times, of the 158 answers naming Snowflake Data Clean Rooms and the 73 naming Databricks Clean Rooms. In those answers Databricks Clean Rooms took the first choice zero times and Snowflake Data Clean Rooms thirteen.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | ||||||
| GPT-5.4 mini | ||||||
| Gemini 3.5 Flash | ||||||
| Perplexity Sonar | ||||||
| Grok 4.1 Fast | ||||||
| Mistral Small | ||||||
| DeepSeek V4 Flash | ||||||
| Llama 4 Maverick | ||||||
| Qwen 3.7 Flash | ||||||
| Kimi K2 | ||||||
| GLM 4.7 FlashX | ||||||
| MiniMax M2.5 | ||||||
| GPT-6 Luna | ||||||
| Muse Glimmer 30B |
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.
“Usage-based through Snowflake consumption; costs scale with data volume” 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
“Choose Snowflake, Databricks, or AWS if you already have a mature data team, store most of your data in these warehouses” Gemini 3.5 Flash · comparative prompt · first choice
“Start with Snowflake Secure Data Sharing/Clean Rooms. It offers the highest probability of successful collaborations” Qwen 3.7 Flash · paraphrase prompt · first choice
“Choose Snowflake if you are a data-led organization that needs to run complex SQL queries, ensure strict data governance” GLM 4.7 FlashX · comparative 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.