StackAdapt vs Teads
Six of fourteen models named StackAdapt first on the direct prompt; zero named Teads. StackAdapt was named by thirteen of the fourteen models and Teads by fourteen and StackAdapt carries 43 labels and Teads 75, so the shares are not directly comparable.
Named in seven categories this edition.
By Q28872999, New York City, United States, founded 2005. Named in five 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 native and content advertising page.
By framing
StackAdaptFirst choices, of fourteen modelsTeads
Across every category in the November 2026 Edition, StackAdapt and Teads were named in the same answer 122 times, of the 418 answers naming StackAdapt and the 227 naming Teads. In those answers Teads took the first choice eleven times and StackAdapt twenty-eight.
Every model, every framing
| 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 | | | | | |
StackAdapt Teads first choice named as an alternative argued againstblank: not namedEach cell is one answer, StackAdapt on the left and Teads on the right.The direct prompt
StackAdapt first, Teads an alternative
6 of 14 modelsTeads was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
8 of 14 modelsThe answer put something else first and named one of the two as an alternative.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.
By buyer segment
StackAdapt29%#1 of 10Teads2%#6 of 10StackAdapt29%#1 of 10Teads6%#4 of 10What the models said about StackAdapt
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.
“Avoid Taboola, Outbrain, and StackAdapt if your budget is under $600–$2,000/month” DeepSeek V4 Flash · budget prompt · hard negative
“StackAdapt: ~$2,000+ monthly minimum” Kimi K2 · budget prompt · hard negative
“focus on platforms with strong contextual targeting, brand safety controls, and transparent reporting, such as StackAdapt, Viant Technology, or Quantcast Platform” Mistral Small · negative prompt · first choice
What the models said about Teads
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“Taboola and Outbrain - Content Recommendation Widgets ... repeatedly called out for low-intent, curiosity-driven traffic and poor transparency” Muse Glimmer 30B · negative prompt · hard negative
“Platforms to Avoid on a Tight Budget - Taboola/Outbrain managed accounts: Often require $3,000–$5,000+/month minimums” Kimi K2 · budget prompt · hard negative
“Platforms to Avoid (or approach carefully) with a Small Budget ... Outbrain: Generally requires a minimum of $600 per month” Qwen 3.7 Flash · budget prompt · hard negative
“Taboola and Outbrain dominate the content recommendation space with massive reach and premium publisher networks” GLM 4.7 FlashX · comparative prompt · first choice
“Outbrain is often preferred by premium publishers and offers advanced AI-driven personalization and higher CTRs” Mistral Small · comparative prompt · first choice
“For Advertisers (Tier 1 / Brand Safe): Taboola, Outbrain, Nativo, TripleLift, Yahoo Native, NewsBreak” Gemini 3.5 Flash · negative 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.