HR AI Index
Index Vendors › LinkedIn Jobs · September 2026 Edition
LinkedIn · 1 category · Named, not ranked

LinkedIn Jobs

16Judge labels
0First choices
2Negative labels
8 of 12Models named it
1Category
September 2026 Edition. Every number here is derived from the raw labels under vendor table vv2026-09.1, every buyer segment counted.
Standing
6 labels, too few to rank
A product needs 10 labels in a category before a share or quadrant is stated. LinkedIn Jobs was named 6 times in Job advertising, where JobTarget led with 21%. The labels and the evidence are below, counted exactly.
By buyer segmentRead the same way at every buyer size.
In job advertising · 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 LinkedIn Jobs 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.
CategoryFunctionShareRankNegative rateLabelsQuadrant
Job advertising and distributionTalent acquisition0%18 of 1090%6under 10 labels · led by JobTarget at 21%

Movement

This is the first edition on this tier, so no move can be computed for LinkedIn Jobs yet. 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 LinkedIn Jobs across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.
ModelFirst choiceAlternativeMentionNegativeLabels
Claude Haiku 4.500000
GPT-5.4 mini01102
Gemini 3.5 Flash10001
Perplexity Sonar00000
Grok 4.1 Fast00101
Mistral Small01001
DeepSeek V4 Flash00000
Llama 4 Maverick00000
Qwen 3.7 Flash00000
Kimi K200000
GLM 4.7 FlashX10001
MiniMax M2.500000

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
Direct1 labelNone
Paraphrase2 labelsNone
Comparative11 labels4not counted in share
Budget-constrained0 labelsNone
Scale-constrained0 labelsNone
Negative2 labelsNone
First choiceAlternativeMentionNegative16 labels in all, every segment counted; 0 of the 4 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.

“If you are hiring for office-based, management, or highly technical roles, prioritize LinkedIn.” Gemini 3.5 Flash · Job advertising · comparative prompt · first choice
“Use LinkedIn for professional/salaried roles.” GLM 4.7 FlashX · Job advertising · comparative prompt · first choice
“LinkedIn excels at reaching professionals and passive job seekers” Mistral Small · Job advertising · comparative prompt · alternative
“LinkedIn: best for professional targeting and passive talent.” GPT-5.4 mini · Job advertising · comparative 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.

No model argued against it.

Named alongside

The products named in the same answers as LinkedIn Jobs, over the 16 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and LinkedIn Jobs was named but was not.
ProductSame answerTook the first choice insteadHead to head
Indeed14 of 164Not in the top three
ZipRecruiter13 of 161Not in the top three
Google for Jobs6 of 161Not in the top three
Joveo5 of 164Not in the top three
Appcast5 of 163Not in the top three
CareerBuilder5 of 160Not in the top three
Glassdoor5 of 160Not in the top three
Handshake5 of 160Not in the top three
Wellfound5 of 160Not in the top three
Craigslist4 of 160Not 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 LinkedIn Jobs. 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 12 of the 16 answers that named LinkedIn Jobs and are not a share of its labels.

Domains cited

g2.com7
ziprecruiter.com6
100hires.com4
business.linkedin.comYour site4
cxeverywhere.com4
guideflow.com4
pitchmeai.com4
adway.ai3
builtin.com3
fitsmallbusiness.com3

Thirty-eight of the forty-two domain citations in answers naming LinkedIn Jobs came from somebody else's page.

Pages cited

Pages are listed as the models cited them.

Names read as LinkedIn Jobs

What the judge wrote, as written, with how often. The vendor table decides that these count as LinkedIn Jobs; a claim can dispute any of them.
LinkedIn job distribution tools 1LinkedIn's native job ads 1
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 LinkedIn Jobs'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 LinkedIn Jobs, 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 linkedin.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.

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