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Native and content advertising · November 2026 Edition

MGID vs Taboola

Zero of fourteen models named MGID first on the direct prompt; zero named Taboola. Both were named by all fourteen models and MGID carries 43 labels and Taboola 69, so the shares are not directly comparable.

MGID

accepted challenger

Named in three categories this edition.

Taboola

criticized challenger

New York City, United States, founded 2007. Named in five categories this edition.

First-choice share12%8%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate21%42%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#3#4A position in a field of 10; printed, not drawn.
Labels4369A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, MGID reading right to left. Rank and label count are printed, not drawn.LinkedIn Sponsored Content was named alongside these two in thirteen of the fourteen direct answers. StackAdapt vs MGID · StackAdapt vs Taboola · LinkedIn Sponsored Content vs MGID

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

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
MGIDFirst choices, of fourteen modelsTaboola
Direct004 against Taboola
Paraphrase037 against Taboola
Comparative010
Budget-constrained601 against MGID · 8 against Taboola
Scale-constrained011 against MGID
Negative027 against MGID · 10 against Taboola
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Across every category in the November 2026 Edition, MGID and Taboola were named in the same answer 116 times, of the 130 answers naming MGID and the 221 naming Taboola. In those answers Taboola took the first choice seventeen times and MGID twenty-seven.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where MGID and Taboola stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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
MGID Taboola first choice named as an alternative argued againstblank: not namedEach cell is one answer, MGID on the left and Taboola on the right.

The direct prompt

The plain question, one answer per model, grouped by where MGID and Taboola stood in it.

Neither was the first choice, one was named

7 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Claude Haiku 4.5LinkedIn Sponsored Content alternatives: Forbes BrandVoice, Harvard Business Review, LinkedIn articles, Quora Ads, Taboola, Teads
Gemini 3.5 FlashLinkedIn Sponsored Content alternatives: Dianomi, StackAdapt, Taboola, Teads
Grok 4.20LinkedIn Sponsored Content alternatives: StackAdapt, Taboola, Teads
Kimi K2StackAdapt alternatives: LinkedIn Sponsored Content, MGID, Revcontent
GLM 4.7 FlashXLinkedIn Sponsored Content alternatives: Influ2, StackAdapt, Taboola, Teads
MiniMax M2.5LinkedIn Sponsored Content alternatives: Google Demand Gen, Taboola, Teads
Muse Glimmer 30BStackAdapt alternatives: AdRoll, LinkedIn Sponsored Content, MGID, Revcontent, Taboola, Teads

Neither was named

7 of 14 modelsThe answer made no first choice from these two in this category.
GPT-5.4 miniStackAdapt alternatives: AdRoll ABM, LinkedIn Sponsored Content
Perplexity SonarLinkedIn Sponsored Content alternatives: AdRoll ABM, StackAdapt
Mistral SmallLinkedIn Sponsored Content alternatives: StackAdapt, Teads, The Trade Desk
DeepSeek V4 FlashStackAdapt alternatives: LinkedIn Sponsored Content, Teads
Llama 4 MaverickStackAdapt alternatives: AdRoll, HubSpot
Qwen 3.7 FlashStackAdapt alternatives: LinkedIn Sponsored Content
GPT-6 LunaLinkedIn Sponsored Content alternatives: StackAdapt

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

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
MGID leads by twenty-five points.
MGID33%#2 of 9
Taboola8%#4 of 9
The full small business standing →
Mid-marketThe figures above
MGID leads by four points.
MGID12%#3 of 10
Taboola8%#4 of 10
The full mid-market standing →
Enterprise
The order flips: Taboola leads at enterprise.
Taboola16%#3 of 10
MGID0%#9 of 10
The full enterprise standing →

What the models said about MGID

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.

“Frequently cited as an arbitrage platform with low-quality publisher mix.” GLM 4.7 FlashX · negative prompt · hard negative
“These smaller networks carry elevated invalid traffic baselines” Kimi K2 · negative prompt · hard negative
“appear in "top lists" but have scattered complaints about traffic quality or stricter policies—treat them as mid-tier with due diligence” Grok 4.20 · negative prompt · soft negative
“MGID stands out as the best native advertising platform for companies with a limited budget.” Grok 4.20 · budget prompt · first choice
“MGID is likely the best starting point for a company with a limited budget.” Perplexity Sonar · budget prompt · first choice
“or MGID for a mainstream self-serve native network with a $100 minimum deposit” Muse Glimmer 30B · budget 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
Also compared

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.