Six of fourteen models named Snowflake Data Clean Rooms first on the direct prompt; zero named Habu. Snowflake Data Clean Rooms was named by fourteen of the fourteen models and Habu by seven and Snowflake Data Clean Rooms carries 52 labels and Habu 14, so the shares are not directly comparable.
By Snowflake Inc., San Mateo, United States, founded 2012. Named in one category this edition.
Named in two 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 October 2026 Edition, Snowflake Data Clean Rooms and Habu were named in the same answer thirty-nine times, of the 158 answers naming Snowflake Data Clean Rooms and the 52 naming Habu. In those answers Habu took the first choice two times and Snowflake Data Clean Rooms eight.
| 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 | HA | |||||
| 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. Five of five in this category shown.
“What to Avoid as a Mid-Market B2B - LiveRamp / Habu: Enterprise-grade” DeepSeek V4 Flash · direct prompt · hard negative
“Only purchase a dedicated independent SaaS DCR (like LiveRamp/Habu) if you have a specific, high-revenue partner usecase lined up” Gemini 3.5 Flash · scale prompt · soft negative
“Top Recommendation: Habu (now LiveRamp Clean Room)” DeepSeek V4 Flash · paraphrase prompt · first choice
“Recognized for its user-friendly interface and ability to handle multiple partnerships and advanced analytics, ideal for mid-market B2B companies.” Mistral Small · direct prompt · alternative
“modern integrations like Habu or InfoSum) that allow partners to query and analyze data directly where it already lives” Gemini 3.5 Flash · negative 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.