GTM AI Index
September 2026 Edition, expanded tier · The six flagship models on every category, as published September 9, 2026. The public index runs on the standard tier; this record is what the Expanded Edition Pack delivers on one category. The category page →
Index Marketing September 2026 Edition

Native and content advertising

Asked as “native advertising platform”, and as “content recommendation and native ads network”, on behalf of a mid-market B2B software company. 44 first choices recorded across the direct, paraphrase, budget and scale prompts, six models each.
Standing
Contested
23% of first choices, contested.

01The standing

Share is the count of first choices across the direct, paraphrase, budget and scale prompts, over all six models, for a mid-market B2B software company. Ordered by share.
ProductFirst-choice shareNegative rateLabelsQuadrant
01StackAdapt23%5%40accepted challenger
02MGID20%21%47accepted challenger
03Teads14%23%70accepted challenger
04Revcontent14%23%47accepted challenger
05Taboola11%36%59criticized challenger
Show the three products at 0%, ordered by negative rate
06Nativo0%7%28accepted challenger
07TripleLift0%0%17accepted challenger
08Sharethrough0%0%11accepted challenger
Bars are the share of first choices, 0 to 100Every product with at least 10 labels here. Every product name links to its vendor page.

Recommended versus criticized

Every product with at least 10 labels here, on both axes. The 30% line names a quadrant, not the verdict above: that one needs more than 40%.

Criticized challengerCriticized default
Negative label rate →
01
02
03
04
05
06
07
S08
Accepted challengerEndorsed leader
0%First-choice share → · lines at 30% share and 25% negative40%
Key
01StackAdapt23%
02MGID20%
03Teads14%
04Revcontent14%
05Taboola11%
06Nativo0%
07TripleLift0%
08Sharethrough0%

02What they warned about

A high negative share on a product with few labels is a warning. A low share on a product with many labels is salience, not sentiment.
Taboola
36%
21 of 59 labels negative · 11 of 6 models · 2 hard negative
“Many advertisers report 100% bot traffic with 100% bounce rates ... Rating: 2.7/5 on Trustpilot (rated "Poor")” GLM 4.7 FlashX, negative prompt
Revcontent
23%
11 of 47 labels negative · 10 of 6 models · 5 hard negative
“Revcontent enforces a hard campaign minimum daily budget of $50 to $100 per day... too steep for a limited-budget test” Gemini 3.5 Flash, budget prompt
Teads
23%
16 of 70 labels negative · 9 of 6 models · 4 hard negative
“Platforms to Avoid on a Tight Budget: Outbrain... Running ads with less than this often yields poor results.” Gemini 3.5 Flash, budget prompt
MGID
21%
10 of 47 labels negative · 9 of 6 models · 3 hard negative
“Similar to Revcontent with large international reach but questionable inventory quality” Mistral Small, negative prompt

03What they cite

Citations exist only for the models that return a source list: four of the six in this edition.

Sites the answers cite

20 of 72 answers in this category came back with a source list, from 4 of 6 models: citations where the model returns them, or the search results it consulted. 6 of those lists are Google grounding redirects that name no site and are left out of the counts. 174 links across 97 sites, every framing counted. Ranked by the number of answers carrying the site or page. The other 2 models searched through a gateway that returned no source list on this edition.

8 answers · 10 citations · 2 models
vendor site · Realize7 answers · 7 citations · 2 models
vendor site · SmartyAds6 answers · 6 citations · 2 models
vendor site · Quora5 answers · 5 citations · 2 models
vendor site · Guideflow5 answers · 5 citations · 2 models
5 answers · 5 citations · 2 models
4 answers · 4 citations · 1 model
vendor site · Madgicx4 answers · 4 citations · 2 models
vendor site · Reddit3 answers · 5 citations · 2 models
vendor site · LinkedIn3 answers · 4 citations · 2 models
3 answers · 4 citations · 1 model
vendor site · Adcash3 answers · 3 citations · 1 model

Pages the answers cite

The ten pages named in the most answers, by full address. A page here is one the models returned with a recommendation, not one the index endorses.

04How they answered

Six framings of the same buying question, each sent to every model in a fresh session with search on. One row per model, so a row shows whether it held its answer under rewording, what it named when cost was the constraint, and what it argued against. Computed from the raw judge labels.
ModelDirect“What is the best native advertising platform for a mid-market B2B company?”Paraphrase“Which content recommendation and native ads network would you recommend to a mid-sized B2B company?”Comparative“What are the top native advertising platforms and how do they differ?”Budget-constrained“What is the best native advertising platform for a company with a limited budget?”Scale-constrained“We are a 500 person company evaluating a native advertising platform. What should we look at?”Negative“Which native advertising platforms should I avoid or be cautious about?”
Claude Opus 5no first choiceno first choiceno first choiceno first choiceno first choicenothing named
Claude Opus 4.8no first choiceno first choiceno first choiceno first choiceno first choicenothing named
GPT-6 Astrano first choiceno first choiceno first choiceno first choiceno first choicenothing named
GPT-5.6 Solno first choiceno first choiceno first choiceno first choiceno first choicenothing named
Gemini 3.1 Prono first choiceno first choiceno first choiceno first choiceno first choicenothing named
Perplexity Sonar Prono first choiceno first choiceno first choiceno first choiceno first choicenothing named
Bold is the first choiceAlternatives are counted; the count opens them.What the answer argued against

05The record

One row per call: the version string exactly as returned, whether the model searched, sources cited, and latency. Full answer text is in the free responses file. Download the record
Zero rows: every prompt, every model, every answer.
PromptModelVersion stringTime (UTC)SearchedSourcesLatency

Normalization in this category

Every judgment call made between the raw labels and the numbers above, listed so it is visible and reversible.

Category-scoped readings
LinkedIn read as LinkedIn Sponsored Content
Meta (Facebook & Instagram Sponsored Posts) read as Meta Audience Network
Unresolved, counted raw
7SearchPPC
AdUp
AdsKeeper
AdsNative
Google Discovery / Demand Gen
Life360
LinkedIn Ads/Campaign Manager
LinkedIn Native Advertising
LockerDome
MSN/Microsoft Audience Network
MintFunnel
MoPub
Mobidea
Qortex
Taboola Self-Serve
Yahoo Native / Yahoo Partner Ads
ZergNet
Discontinued, still offered
No shut-down product was recommended here.
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