Teads vs Dianomi
Zero of fourteen models named Teads first on the direct prompt; zero named Dianomi. Teads was named by fourteen of the fourteen models and Dianomi by nine and Teads carries 75 labels and Dianomi 14, so the shares are not directly comparable.
By Q28872999, New York City, United States, founded 2005. 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
TeadsFirst choices, of fourteen modelsDianomi
Across every category in the November 2026 Edition, Teads and Dianomi were named in the same answer twenty-two times, of the 227 answers naming Teads and the 24 naming Dianomi. In those answers Dianomi took the first choice four 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 Dianomi first choice named as an alternative argued againstblank: not namedEach cell is one answer, Teads on the left and Dianomi on the right.The direct prompt
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.Neither was named
6 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
Teads2%#6 of 10Dianomi2%#7 of 10Teads6%#4 of 10Dianomi4%#– of 10What 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
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.