StackAdapt vs Taboola
Six of fourteen models named StackAdapt first on the direct prompt; zero named Taboola. StackAdapt was named by thirteen of the fourteen models and Taboola by fourteen and StackAdapt carries 43 labels and Taboola 69, so the shares are not directly comparable.
Named in seven categories this edition.
New York City, United States, founded 2007. 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 modelsTaboola
Across every category in the November 2026 Edition, StackAdapt and Taboola were named in the same answer 114 times, of the 418 answers naming StackAdapt and the 221 naming Taboola. In those answers Taboola took the first choice seventeen 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 Taboola first choice named as an alternative argued againstblank: not namedEach cell is one answer, StackAdapt on the left and Taboola on the right.The direct prompt
StackAdapt first, Taboola an alternative
6 of 14 modelsTaboola 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 10Taboola8%#4 of 10StackAdapt29%#1 of 10Taboola16%#3 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 Taboola
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“Multiple sources highlight Taboola as a platform with significant concerns, including a high negative share in AI model recommendations and a reputation for low-quality or "clickbait" placements.” Mistral Small · negative prompt · hard negative
“Avoid: Taboola, Outbrain, RevContent, and MGID unless you have strong controls and optimization processes.” GLM 4.7 FlashX · negative prompt · hard negative
“The two are repeatedly called out for low-intent, curiosity-driven traffic and poor transparency.” Muse Glimmer 30B · negative prompt · hard negative
“Content recommendation networks (often called "the duopoly" of native advertising)... Both are leaders in personalized content recommendations” Mistral Small · comparative prompt · first choice
“Prioritize established, premium options like Taboola, Outbrain, TripleLift, Nativo, Yahoo Advertising, or StackAdapt” Grok 4.20 · negative prompt · first choice
“Taboola and Outbrain dominate the content recommendation space with massive reach and premium publisher networks” 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.