Index › Vendors › X · September 2026 Edition

5 categories · Named, not ranked

X

17Judge labels

0First choices

15Negative labels

10 of 12Models named it

5Categories

September 2026 Edition. Every number here is derived from the raw labels under vendor table v2026-09-16.3, every buyer segment counted.

Standing

7 labels, too few to rank

A product needs 10 labels in a category before a share or quadrant is stated. X was named 7 times in Content marketing and 4 other categories, where HubSpot Content Hub led with 30%. The labels and the evidence are below, counted exactly.

By buyer segmentRead the same way at every buyer size.

Small business140 of 150

under the label threshold

Mid-market155 of 159

under the label threshold

Enterprisenot named

No model named it for this buyer.

In content marketing · each standing computed within its segment · bars are 0 to 100 · the accent bar is the product's own best reading

Standing by category

Every category where a model named X 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
Content marketing platforms	Marketing	0%	155 of 159	100%	2	under 10 labels · led by HubSpot Content Hub at 30%
Video advertising	Marketing	0%	100 of 121	50%	2	under 10 labels · led by StackAdapt at 24%
Identity resolution	GTM data and infrastructure	0%	142 of 151	100%	1	under 10 labels · led by Hightouch at 15%
Programmatic and display advertising	Marketing	0%	85 of 96	100%	1	under 10 labels · led by StackAdapt at 61%
Video marketing and hosting	Marketing	0%	39 of 56	100%	1	under 10 labels · led by Wistia at 56%

Movement

This is the first edition on this tier, so no move can be computed for X 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 X across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.

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

Direct0 labelsNone

Paraphrase0 labelsNone

Comparative1 labelNone

Budget-constrained0 labelsNone

Scale-constrained0 labelsNone

Negative16 labelsNone

First choiceAlternativeMentionNegative17 labels in all, every segment counted; 0 of the 0 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.

No positive label carried a quote.

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.

“Google, Facebook, X and similar massive platforms - Why avoid” GLM 4.7 FlashX · Identity · negative prompt · hard negative

“avoid heavy reliance for paid amplification” Grok 4.1 Fast · Content marketing · negative prompt · hard negative

“X (formerly Twitter) is a notable recent example... this created a new loophole where X inventory could appear in automated campaigns” Kimi K2 · Programmatic · negative prompt · soft negative

“offer discovery but don't provide a direct, portable relationship with your audience” Claude Haiku 4.5 · Content marketing · negative prompt · soft negative

Named alongside

The products named in the same answers as X, over the 17 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and X was named but was not.

ProductSame answerTook the first choice insteadHead to head

TikTok Ads8 of 170Not in the top three

Meta Ads6 of 170Not in the top three

YouTube Ads5 of 173Not in the top three

Snapchat5 of 170Not in the top three

TikTok5 of 170Not in the top three

Instagram4 of 170Not in the top three

Facebook3 of 170Not in the top three

LinkedIn Video Ads3 of 170Not in the top three

Meta3 of 170Not in the top three

YouTube3 of 170Not 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 X. 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 6 of the 17 answers that named X and are not a share of its labels.

Domains cited

ads.tiktok.com2

epom.com2

support.google.com2

admanage.ai1

angelortegacastro.com1

anura.io1

basis.com1

beehiiv.com1

blasto.ai1

blog.hubspot.com1

No domain is on file for X, so its own site is not marked.

Pages cited

admanage.ai/blog/google-ads-alternatives1

ads.tiktok.com/business/en-US/blog/spark-ads-101-make-tiktoks-into-ads1

ads.tiktok.com/business/en-US/brand-safety-suitability1

angelortegacastro.com/en/publicidad-programatica-retos-para-el-20231

anura.io/fraud-tidbits/programmatic-ad-fraud-is-stealing-your-ad-budget1

basis.com/insights/transparent-programmatic-advertising-platforms-a-2026-guide-to-brand-sa1

beehiiv.com/blog/content-creator-platform1

blasto.ai/blog/26-predictions-redefining-programmatic-advertising1

Pages are listed as the models cited them.

Names read as X

What the judge wrote, as written, with how often. The vendor table decides that these count as X; a claim can dispute any of them.

X (Twitter) 3X (formerly Twitter) 2

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 X'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 X, 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 the vendor's own domain 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.