Four of fourteen models named StackAdapt first on the direct prompt; zero named Demandbase CTV. StackAdapt was named by fourteen of the fourteen models and Demandbase CTV by ten and StackAdapt carries 26 labels and Demandbase CTV 13, so the shares are not directly comparable.
Named in seven categories this edition.
By Demandbase, San Francisco, United States, founded 2006. 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 video advertising page.
| 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.
No label in this category carried a quote.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“If your priority is account-level targeting and connecting CTV to pipeline, start with Demandbase CTV/Piper.” Muse Glimmer 30B · paraphrase prompt · first choice
“Top Recommendation: Demandbase CTV” MiniMax M2.5 · paraphrase prompt · first choice
“Best if you're already doing ABM ... Caveat: It's an enterprise-grade platform and may be overkill or cost-prohibitive for some mid-sized companies.” DeepSeek V4 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.