Every category where a model named Monster 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
Job advertising and distribution	Talent acquisition	0%	108 of 109	73%	11	criticized challenger
Compensation management	Compensation and total rewards	0%	167 of 189	100%	1	under 10 labels · led by Pave at 18%

Movement

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

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

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

Comparative5 labelsNone

Budget-constrained2 labelsNone

Scale-constrained0 labelsNone

Negative24 labelsNone

First choiceAlternativeMentionNegative31 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.

“Stick to well-established, reputable job boards ... such as LinkedIn, Indeed, Monster, Glassdoor, and CareerBuilder.” Mistral Small · Job advertising · negative 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.

“Described as "the category's biggest current regret" by reviewers” Kimi K2 · Compensation management · negative prompt · hard negative

“Older, formerly dominant job boards (such as Monster or CareerBuilder) that still charge premium, flat-rate subscription fees” Gemini 3.5 Flash · Job advertising · negative prompt · soft negative

“generally less differentiated by network effects or social graph than LinkedIn, and less search-engine-like than Indeed” GPT-5.4 mini · Job advertising · comparative prompt · soft negative

“Users report expired/inaccurate listings, spam, and receiving calls for vague commission-based roles” DeepSeek V4 Flash · Job advertising · negative prompt · soft negative

Named alongside

The products named in the same answers as Monster, over the 31 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Monster was named but was not.

ProductSame answerTook the first choice insteadHead to head

Indeed28 of 315Not in the top three

CareerBuilder22 of 310Not in the top three

ZipRecruiter21 of 311Not in the top three

LinkedIn20 of 314Not in the top three

Glassdoor16 of 310Not in the top three

Craigslist13 of 310Not in the top three

Google for Jobs9 of 311Not in the top three

Dice9 of 310Not in the top three

Intch8 of 310Not in the top three

Facebook Jobs6 of 310Not 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 Monster. 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 27 of the 31 answers that named Monster and are not a share of its labels.

Domains cited

reddit.com17

linkedin.com15

g2.com13

totaldefense.com9

noon.ai8

consumer.ftc.gov7

pitchmeai.com7

hrmorning.com6

joveo.com6

findcandidatesforjobs.com5

Ninety-three of the ninety-three domain citations in answers naming Monster came from somebody else's page.

Pages cited

totaldefense.com/security-blog/job-board-safety-guide-how-to-avoid-employment-scams-on-fak9

linkedin.com/posts/fiona-de-villiers_ethical-give-activistsrevealcorruption-activity-742948

noon.ai/blog/articles/143-best-job-posting-sites8

g2.com/discussions/which-programmatic-job-advertising-platforms-are-the-highest-rated-for-6

hrmorning.com/articles/bad-job-boards6

consumer.ftc.gov/articles/job-scams5

findcandidatesforjobs.com/sourcing/job-boards5

firsthr.app/compare/hiring/job-boards5

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 Monster'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 Monster, 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 monster.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.