HR AI Index
Index Vendors › LinkedIn · September 2026 Edition
5 categories · Ranked

LinkedIn

75Judge labels
3First choices
25Negative labels
12 of 12Models named it
5Categories
September 2026 Edition. Every number here is derived from the raw labels under vendor table vv2026-09.1, every buyer segment counted.
Best standing
3% in Job advertising for mid-market buyers
Rank 8 of 88 in the mid-market standing
0 of 12 models made it the first choice on the direct prompt; 40% of its 5 labels there were negative.
By buyer segmentRead the same way at every buyer size.
In recruiting crm · 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 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
Recruiting CRM and sourcingTalent acquisition5%8 of 8840%5under 10 labels · led by Lever at 20%
Job advertising and distributionTalent acquisition3%10 of 10932%19criticized challenger
Internal talent marketplacePerformance and talent management0%32 of 1290%2under 10 labels · led by Fuel50 at 26%
Compensation benchmarking dataCompensation and total rewards0%140 of 144100%1under 10 labels · led by Payscale at 24%
Talent intelligencePerformance and talent management0%53 of 770%1under 10 labels · led by Pin at 17%

Movement

This is the first edition on this tier, so no move can be computed for LinkedIn 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 across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.
ModelFirst choiceAlternativeMentionNegativeLabels
Claude Haiku 4.510012
GPT-5.4 mini00000
Gemini 3.5 Flash00011
Perplexity Sonar01012
Grok 4.1 Fast22004
Mistral Small10001
DeepSeek V4 Flash21115
Llama 4 Maverick00101
Qwen 3.7 Flash12115
Kimi K201012
GLM 4.7 FlashX00011
MiniMax M2.500224

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
Direct4 labelsNone
Paraphrase4 labels1
Comparative15 labels2not counted in share
Budget-constrained9 labels2
Scale-constrained4 labelsNone
Negative39 labels6not counted in share
First choiceAlternativeMentionNegative75 labels in all, every segment counted; 3 of the 11 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.

“While LinkedIn offers premium features, the free version still allows you to search for candidates and post jobs. The paid "Recruiter Lite" plan is more affordable” Claude Haiku 4.5 · Recruiting CRM · budget prompt · first choice
“Stick to well-established, reputable job boards with strong verification systems, such as LinkedIn, Indeed, Monster, Glassdoor, and CareerBuilder.” Mistral Small · Job advertising · negative prompt · first choice
“LinkedIn (free basic version) is often the best starting point for candidate sourcing on a limited budget.” Grok 4.1 Fast · Recruiting CRM · budget prompt · first choice
“Stick to well-known boards with strong verification (LinkedIn, Glassdoor, FlexJobs is human-reviewed” DeepSeek V4 Flash · Job advertising · negative prompt · first choice

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.

“Do not use them for making concrete compensation decisions regarding your 500 employees due to sample bias” Qwen 3.7 Flash · Compensation benchmarkin · scale prompt · hard negative
“Scams are commonly carried out through Facebook, Instagram, TikTok, WhatsApp, Telegram, LinkedIn, and similar channels, especially when the recruiter contacts you first.” Perplexity Sonar · Job advertising · negative prompt · soft negative
“98 percent of scam vacancies analyzed were found on LinkedIn, though this doesn't mean LinkedIn itself is unsafe” Claude Haiku 4.5 · Job advertising · negative prompt · soft negative
“Any tool that relies on scraping LinkedIn — LinkedIn restricts its API, so scraped profile data is often stale” DeepSeek V4 Flash · Recruiting CRM · negative prompt · soft negative

Named alongside

The products named in the same answers as LinkedIn, over the 75 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and LinkedIn was named but was not.
ProductSame answerTook the first choice insteadHead to head
Indeed57 of 7510Not in the top three
ZipRecruiter38 of 755Not in the top three
Glassdoor29 of 750Not in the top three
Google for Jobs20 of 752Not in the top three
Craigslist20 of 750Not in the top three
Monster20 of 750Not in the top three
CareerBuilder17 of 750Not in the top three
Dice16 of 750Not in the top three
Handshake14 of 750Not in the top three
Intch11 of 750Not 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. 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 60 of the 75 answers that named LinkedIn and are not a share of its labels.

Domains cited

reddit.com25
g2.com22
linkedin.comYour site21
noon.ai18
pin.com18
pitchmeai.com16
bestjobsearchapps.com15
firsthr.app14
money.com14
selectsoftwarereviews.com14

156 of the 177 domain citations in answers naming LinkedIn came from somebody else's page.

Pages cited

Pages are listed as the models cited them.

Names read as LinkedIn

What the judge wrote, as written, with how often. The vendor table decides that these count as LinkedIn; a claim can dispute any of them.
LinkedIn & Indeed 1LinkedIn (Free tier) 1LinkedIn (basic free account) 1LinkedIn (free basic version) 1LinkedIn Premium (non-recruiter) 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'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, 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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