Three of fourteen models named The Trade Desk first on the direct prompt; zero named Google Display & Video 360. The Trade Desk was named by fourteen of the fourteen models and Google Display & Video 360 by thirteen and The Trade Desk carries 59 labels and Google Display & Video 360 32, so the shares are not directly comparable.
By Trade Desk, Ventura, United States, founded 2009. Named in nine categories this edition.
By Google, Mountain View, California, United States, founded 1998. 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 programmatic and display advertising page.
Across every category in the October 2026 Edition, The Trade Desk and Google Display & Video 360 were named in the same answer 154 times, of the 350 answers naming The Trade Desk and the 176 naming Google Display & Video 360. In those answers Google Display & Video 360 took the first choice eight times and The Trade Desk thirty-nine.
| 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.
“Enterprise DSPs like The Trade Desk ($50K+/month) ... aren't practical for small budgets” Grok 4.1 Fast · budget prompt · hard negative
“$20K–$50K/month minimum, better for large-scale consumer campaigns” Kimi K2 · paraphrase prompt · hard negative
“mid-market companies usually cannot afford the $25,000–$50,000 monthly minimums required for a direct seat on enterprise demand-side platforms (DSPs) like *The Trade Desk*” Gemini 3.5 Flash · direct prompt · soft negative
“If you need open-web reach and CTV without being locked into a single vendor, The Trade Desk is a strong independent option.” GLM 4.7 FlashX · comparative prompt · first choice
“generally considered to offer better safety controls, more transparency, and stronger fraud prevention mechanisms” MiniMax M2.5 · negative prompt · first choice
“Prioritize platforms like Google DV360, The Trade Desk, MediaMath, or Amazon DSP, but test via demos or pilots.” Grok 4.1 Fast · scale prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“($35K–$50K+/month, no self-serve) ... have high thresholds, complex contracts ... aren't practical for small budgets” Grok 4.1 Fast · budget prompt · hard negative
“What I'd Avoid for Mid-Sized B2B: ... $50K+/month minimum, complex, overkill for mid-market” Kimi K2 · paraphrase prompt · hard negative
“The Caution: In a major 2025 report by Adalytics, it was found that programmatic ads from major advertisers were served on a website hosting child sexual abuse material” GLM 4.7 FlashX · negative prompt · soft negative
“Industry standards like Google DV360, The Trade Desk, and Amazon DSP are generally considered to offer better safety controls” MiniMax M2.5 · negative prompt · first choice
“Prioritize platforms like Google DV360, The Trade Desk, MediaMath, or Amazon DSP, but test via demos or pilots.” Grok 4.1 Fast · scale prompt · first choice
“If you prioritize Google's ecosystem ... DV360 is often the best choice.” GLM 4.7 FlashX · comparative 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.