Taboola vs Dianomi
Zero of fourteen models named Taboola first on the direct prompt; zero named Dianomi. Taboola was named by fourteen of the fourteen models and Dianomi by nine and Taboola carries 69 labels and Dianomi 14, so the shares are not directly comparable.
New York City, United States, founded 2007. Named in five categories this edition.
London, founded 2003. 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 native and content advertising page.
By framing
TaboolaFirst choices, of fourteen modelsDianomi
Across every category in the November 2026 Edition, Taboola and Dianomi were named in the same answer twenty-two times, of the 221 answers naming Taboola and the 24 naming Dianomi. In those answers Dianomi took the first choice five times and Taboola 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 | | | | | |
Taboola Dianomi first choice named as an alternative argued againstblank: not namedEach cell is one answer, Taboola on the left and Dianomi 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 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
What the models said about Dianomi
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“I'd start with Dianomi if your B2B buyers are executives, finance professionals, or IT decision-makers.” GPT-6 Luna · paraphrase prompt · first choice
“Test Dianomi if your buyers are C-suite/finance executives reading tier-1 publications.” Gemini 3.5 Flash · direct prompt · alternative
“with Dianomi as a specialist test if the audience is concentrated in finance” Perplexity Sonar · 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.