Teads vs Taboola
Zero of fourteen models named Teads first on the direct prompt; zero named Taboola. Both were named by all fourteen models and Teads carries 74 labels and Taboola 61, so the shares are not directly comparable.
By Q28872999, New York City, United States, founded 2005. Named in four 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
TeadsFirst choices, of fourteen modelsTaboola
Across every category in the October 2026 Edition, Teads and Taboola were named in the same answer 175 times, of the 198 answers naming Teads and the 186 naming Taboola. In those answers Taboola took the first choice sixteen times and Teads six.
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 | | | | | |
Teads Taboola first choice named as an alternative argued againstblank: not namedEach cell is one answer, Teads on the left and Taboola on the right.The direct prompt
Neither was the first choice, one was named
6 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
8 of 14 modelsThe answer made no first choice from these two in this category.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
What the models said about Teads
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“Avoid the "big two" (Taboola/Outbrain)” Kimi K2 · budget prompt · hard negative
“be very careful reading Taboola or Outbrain agreements. They historically include aggressive exclusivity clauses and auto-renewal traps.” Gemini 3.5 Flash · negative prompt · soft negative
“Reputable alternatives (fewer complaints): Outbrain, Taboola (with controls), Sharethrough.” Grok 4.20 · negative prompt · first choice
“Best for: Performance marketing, content-driven brands, premium publisher environments” Kimi K2 · comparative prompt · first choice
“Choose Taboola or Outbrain if you want scale and mainstream publisher reach” Perplexity Sonar · comparative 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. Five of six in this category shown.
“Avoid the "big two" (Taboola/Outbrain)” Kimi K2 · budget prompt · hard negative
“Taboola served 61.1% of all problematic native ads analyzed ... While Taboola is safe for massive brands with strict compliance teams, if you are a smaller advertiser, you risk your brand” GLM 4.7 FlashX · negative prompt · soft negative
“"Best all-round reach: Taboola, through its Realize platform—450 billion recommendations a month, and the deepest, most liquid auction on the open web."” Muse Glimmer 30B · comparative prompt · first choice
“Best for: Massive reach, brand awareness, large-scale campaigns ... Deepest liquidity in the open web, massive scale” Kimi K2 · comparative prompt · first choice
“The undisputed leader in sheer volume and liquidity... If your goal is volume, start with Taboola or Teads.” Qwen 3.7 Flash · 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.