Every category where a model named AdLib for a mid-market B2B company. Share is first choices across the direct, paraphrase, budget and scale prompts; rank is within every product named in that category.

Category	Function	Share	Rank	Negative rate	Labels	Quadrant
Native and content advertising	Marketing	2%	9 of 72	17%	6	under 10 labels · led by StackAdapt at 23%
Programmatic and display advertising	Marketing	0%	16 of 96	0%	5	under 10 labels · led by StackAdapt at 61%
Retail, proximity and IoT marketing	Marketing	0%	112 of 131	50%	2	under 10 labels · led by Radar at 19%

Movement

This is the first edition on this tier, so no move can be computed for AdLib yet. The next is due October 1, 2026. From the next edition this section shows, per buyer segment, whether its share moved by more than the measured noise floor.

By model

How each model treated AdLib across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.

Model	First choice	Alternative	Mention	Negative	Labels
Claude Haiku 4.5	0	1	1	0	2
GPT-5.4 mini	0	0	0	0	0
Gemini 3.5 Flash	0	2	1	0	3
Perplexity Sonar	0	0	0	1	1
Grok 4.1 Fast	0	0	2	1	3
Mistral Small	0	0	0	0	0
DeepSeek V4 Flash	0	0	0	0	0
Llama 4 Maverick	1	0	0	0	1
Qwen 3.7 Flash	0	1	0	0	1
Kimi K2	0	1	0	0	1
GLM 4.7 FlashX	0	1	0	0	1
MiniMax M2.5	0	0	0	0	0

By framing

Which of the six questions produced the naming. By model says how often; this says asked what. The first-choice count on the right carries the marks of the models that produced it.

FramingLabels by classFirst choices

Direct2 labelsNone

Paraphrase2 labelsNone

Comparative14 labels1not counted in share

Budget-constrained12 labels4

Scale-constrained5 labels2

Negative1 labelNone

First choiceAlternativeMentionNegative36 labels in all, every segment counted; 6 of the 7 first choices count toward share, since the comparative and negative framings do not. The bar is one segment per label class, to scale within the framing.

What the models said for it

Verbatim evidence the judge attached to positive labels.

“AdLib is recommended for unified programmatic buying” Llama 4 Maverick · Native ads · scale prompt · first choice

“An excellent mid-market DSP that allows you to buy native ad placements across 20+ different native supply sources simultaneously.” Gemini 3.5 Flash · Native ads · paraphrase prompt · alternative

“Best for Risk Mitigation & Efficiency: AdLib ... AdLib is a top contender.” Qwen 3.7 Flash · Native ads · budget prompt · alternative

“want "enterprise-level" reach without the enterprise cost... No minimum spend” Gemini 3.5 Flash · Programmatic · budget prompt · alternative

And against it

Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.

“or AdLib ($799/mo)—too pricey for limited budgets” Grok 4.1 Fast · Proximity · budget prompt · hard negative

“less clearly the best *native-only* option for a company simply trying to spend less” Perplexity Sonar · Native ads · budget prompt · soft negative

Named alongside

The products named in the same answers as AdLib, over the 36 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and AdLib was named but was not.

ProductSame answerTook the first choice insteadHead to head

StackAdapt21 of 368Not in the top three

Taboola15 of 364Not in the top three

Teads15 of 364Not in the top three

MGID13 of 365Not in the top three

The Trade Desk12 of 361Not in the top three

Nativo10 of 361Not in the top three

Revcontent9 of 360Not in the top three

Simpli.fi8 of 360Not in the top three

Amazon DSP7 of 360Not in the top three

Choozle6 of 360Not in the top three

A head-to-head page exists where both products are in a category's top three. The other rows are the same fact without a page behind them, so they link to the product instead.

What carried it into the answer

The sites and pages cited by the answers that named AdLib. A fact about retrieval, not a lever on the model.

Citations exist only for the models that return a source list, four of the twelve in this edition, so these counts come from 7 of the 36 answers that named AdLib and are not a share of its labels.

Domains cited

getadlib.com7

6sense.com4

aidigital.com4

guideflow.com4

realize.com4

business.quora.com3

digitaladvertisinghub.net3

gitnux.org3

improvado.io3

joinative.com3

Thirty-eight of the thirty-eight domain citations in answers naming AdLib came from somebody else's page.

Pages cited

getadlib.com/blog/best-native-advertising-platforms4

guideflow.com/blog/native-advertising-software4

realize.com/marketing-hub/native-advertising-platform4

6sense.com/tech/native-advertising3

aidigital.com/blog/native-advertising-platforms3

business.quora.com/marketing-advertising-resources/best-native-advertising-platforms-b2b3

digitaladvertisinghub.net/best-native-advertising-platforms-for-performance-marketers3

getadlib.com/blog/top-programmatic-advertising-tools3

Pages are listed as the models cited them.

Is this your product?

Claim this page

Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when AdLib's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as AdLib, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.

It does not get any change to labels, shares or verdicts, any preview, or any say over which quotes appear. A verification link goes to your work email; an address at adlibrary.com is approved on the spot, any other address is reviewed by hand.

Your name and company appear on the claimed page, or the company alone if you ask below. A title and a LinkedIn address appear there too if you give them, and are left off if you do not. Your email address is never published.