Zero of fourteen models named Amazon Marketing Cloud first on the direct prompt; two named AWS Clean Rooms. Both were named by all fourteen models and Amazon Marketing Cloud carries 36 labels and AWS Clean Rooms 48, so the shares are not directly comparable.
By Amazon, Seattle, United States, founded 1994. Named in two categories 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 November 2026 Edition, Amazon Marketing Cloud and AWS Clean Rooms were named in the same answer sixty-four times, of the 83 answers naming Amazon Marketing Cloud and the 154 naming AWS Clean Rooms. In those answers AWS Clean Rooms took the first choice fifteen times and Amazon Marketing Cloud thirteen.
| Model | DirectAMAC | ParaphraseAMAC | ComparativeAMAC | Budget-constrainedAMAC | Scale-constrainedAMAC | NegativeAMAC |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | AMAC | AC | AM | |||
| GPT-5.4 mini | AC | AC | AC | AC | ||
| Gemini 3.5 Flash | AC | AMAC | AMAC | AM | ||
| Perplexity Sonar | AMAC | AMAC | AMAC | |||
| Grok 4.20 | AMAC | AMAC | AC | AMAC | AMAC | |
| Mistral Small | AC | AMAC | AM | |||
| DeepSeek V4 Flash | AMAC | AMAC | AMAC | AMAC | AMAC | AMAC |
| Llama 4 Maverick | AMAC | AM | AM | |||
| Qwen 3.7 Flash | AC | AMAC | ||||
| Kimi K2 | AMAC | AMAC | ||||
| GLM 4.7 FlashX | AMAC | |||||
| MiniMax M2.5 | AC | AC | AMAC | |||
| GPT-6 Luna | AC | AC | AMAC | AMAC | ||
| Muse Glimmer 30B | AC | AMAC |
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.
“not relevant for most B2B data collaboration use cases” DeepSeek V4 Flash · paraphrase prompt · hard negative
“What to approach with caution: Native clean rooms operated by major media platforms (e.g., Google Ads Data Hub, Amazon Marketing Cloud, Meta Advanced Analytics)” Gemini 3.5 Flash · negative prompt · soft negative
“Walled Garden Clean Rooms (Use with Caution) ... Vendor lock-in ... be aware you're trading data sovereignty for convenience” DeepSeek V4 Flash · negative prompt · soft negative
“Best all‑around for most advertisers: Google Ads Data Hub (if you're Google‑heavy) or Amazon Marketing Cloud (if you're Amazon‑heavy). Both are free and easy to set up.” GLM 4.7 FlashX · budget prompt · first choice
“likely to be a free walled-garden option such as Google Ads Data Hub, Amazon Marketing Cloud, or Meta Advanced Analytics” Llama 4 Maverick · budget prompt · first choice
“Start with Amazon Marketing Cloud (AMC) or Google Ads Data Hub depending on where your biggest ad spend lies.” Qwen 3.7 Flash · budget prompt · first choice
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
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.