GTM AI Index
Index Marketing Native ads › MGID vs Teads
Native and content advertising · September 2026 Edition

MGID vs Teads

Zero of twelve models named MGID first on the direct prompt; one named Teads. Both were named by all twelve models and MGID carries 47 labels and Teads 70, so the shares are not directly comparable.

MGID

accepted challenger

Named in two categories this edition.

Teads

accepted challenger

By Q28872999, New York City, United States, founded 2005. Named in three categories this edition.

First-choice share20%14%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate21%23%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#3A position in a field of 8; printed, not drawn.
Labels4770A 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.StackAdapt was named alongside these two in eleven of the twelve direct answers. StackAdapt vs MGID · StackAdapt vs Teads

Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all twelve 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 twelve models made each the first choice, per way of asking, and how many argued against it.
MGIDFirst choices, of twelve modelsTeads
Direct01
Paraphrase031 against MGID · 2 against Teads
Comparative04
Budget-constrained916 against Teads
Scale-constrained01
Negative009 against MGID · 8 against Teads
Bars are first choices, 0 to 12 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to twelve.

The direct prompt

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

Teads first, MGID not the choice

1 of 12 modelsMGID was named in the answer but not as the choice, or not at all.
Claude Haiku 4.5Teads alternatives: AdRoll ABM, Demandbase, StackAdapt, ZoomInfo

Neither was the first choice, one was named

9 of 12 modelsThe answer put something else first and named one of the two as an alternative.
GPT-5.4 miniLinkedIn Sponsored Content alternatives: StackAdapt, Taboola, Teads
Gemini 3.5 FlashStackAdapt alternatives: AdRoll ABM, Dianomi, LinkedIn Sponsored Content, Taboola, Teads
Perplexity SonarStackAdapt alternatives: LinkedIn Ads/Campaign Manager, MGID, Nativo, Revcontent, Taboola, Teads
Grok 4.1 FastRevcontent alternatives: MGID, StackAdapt, Taboola
Mistral SmallStackAdapt alternatives: MGID, Revcontent, Teads
DeepSeek V4 FlashStackAdapt alternatives: MGID, Revcontent, Taboola, Teads
Qwen 3.7 FlashRevcontent alternatives: Nativo, StackAdapt, Teads
Kimi K2StackAdapt alternatives: AdRoll ABM, Revcontent, Teads
MiniMax M2.5StackAdapt alternatives: AdRoll ABM, MGID, Revcontent

Neither was named

2 of 12 modelsThe answer made no first choice from these two in this category.
Llama 4 MaverickAbmatic AI alternatives: AdRoll ABM, Revcontent
GLM 4.7 FlashXStackAdapt alternatives: AdRoll ABM, Revcontent

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 thirty-three points.
MGID37%#1 of 9
Teads4%#6 of 9
The full small business standing →
Mid-marketThe figures above
MGID leads by seven points.
MGID20%#2 of 8
Teads14%#3 of 8
The full mid-market standing →
Enterprise
The order flips: Teads leads at enterprise.
Teads9%#4 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.

“Similar to Revcontent with large international reach but questionable inventory quality” Mistral Small · negative prompt · hard negative
“Requires heavy manual filtering and pre-bid fraud integration to avoid bot traffic.” Kimi K2 · negative prompt · hard negative
“Verdict: Avoid (High Risk)” Qwen 3.7 Flash · negative prompt · hard negative
“Best Overall for Low-Cost Testing: MGID ... most accessible entry point for businesses new to native advertising or working with strict financial caps.” Qwen 3.7 Flash · budget prompt · first choice
“MGID represents the best starting point due to its extremely low minimum spend, competitive CPC rates, and self-serve interface” GLM 4.7 FlashX · budget prompt · first choice
“MGID is the clear winner for limited budgets – multiple sources confirm it's the most accessible entry point” Kimi K2 · budget prompt · first choice

What the models said about Teads

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

“Platforms to Avoid on a Tight Budget: Outbrain... Running ads with less than this often yields poor results.” Gemini 3.5 Flash · budget prompt · hard negative
“Avoid Taboola, Outbrain (now Teads), StackAdapt, and other premium platforms” DeepSeek V4 Flash · budget prompt · hard negative
“Avoid premium platforms (Taboola, Outbrain) until you validate ROAS” Grok 4.1 Fast · budget prompt · hard negative
“I'd recommend starting with Outbrain Engage for both native advertising and content recommendation widgets” Mistral Small · paraphrase prompt · first choice
“If I had to pick one default recommendation for B2B, I’d lean Outbrain for lead-gen/content-driven campaigns” GPT-5.4 mini · paraphrase prompt · first choice
“Teads offers a cleaner, more brand-safe user experience. It provides a full-funnel approach” Gemini 3.5 Flash · comparative prompt · first choice
Also compared

Comparisons are drawn for the top three products in each category. The output is the models' output; nothing here is a recommendation by the index.