One of fourteen models named Databricks Clean Rooms first on the direct prompt; one named InfoSum. Databricks Clean Rooms was named by thirteen of the fourteen models and InfoSum by fourteen and Databricks Clean Rooms carries 26 labels and InfoSum 38, so the shares are not directly comparable.
By Databricks, San Francisco, United States, founded 2013. Named in one category this edition.
Named in three 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 November 2026 Edition, Databricks Clean Rooms and InfoSum were named in the same answer forty-six times, of the 74 answers naming Databricks Clean Rooms and the 109 naming InfoSum. In those answers InfoSum took the first choice five times and Databricks Clean Rooms two.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | ||||||
| GPT-5.4 mini | ||||||
| Gemini 3.5 Flash | ||||||
| Perplexity Sonar | ||||||
| Grok 4.20 | ||||||
| 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 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
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“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
“Avoid enterprise platforms initially: Skip InfoSum, Habu, or LiveRamp” DeepSeek V4 Flash · budget prompt · hard negative
“Avoid Snowflake, LiveRamp, InfoSum, or Decentriq” Perplexity Sonar · budget prompt · hard negative
“Prefer neutral, interoperable platforms (like Snowflake, Databricks, AWS Clean Rooms, InfoSum, or independent providers)” DeepSeek V4 Flash · negative prompt · first choice
“For broad collaboration across ecosystems: LiveRamp or InfoSum.” Grok 4.20 · comparative prompt · first choice
“Top Pick: InfoSum ... Highest level of security compliance” Qwen 3.7 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.