Job advertising and distribution · November 2026 Edition
Workable vs LinkedIn Jobs
Zero of fourteen models named Workable first on the direct prompt; zero named LinkedIn Jobs. Workable was named by ten of the fourteen models and LinkedIn Jobs by fourteen and Workable carries 12 labels and LinkedIn Jobs 30, so the shares are not directly comparable.
Named in fourteen categories this edition.
Named in two categories this edition.
First-choice share13%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%37%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#4#8A position in a field of 13; printed, not drawn.
Labels1230A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Workable reading right to left. Rank and label count are printed, not drawn.Appcast was named alongside these two in thirteen of the fourteen direct answers. Appcast vs Workable · Appcast vs LinkedIn Jobs · JobTarget vs Workable
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the job advertising and distribution page.
By framing
How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
WorkableFirst choices, of fourteen modelsLinkedIn Jobs
Direct00
Paraphrase81
Comparative09
Budget-constrained00
Scale-constrained00
Negative0011 against LinkedIn Jobs
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.
Every model, every framing
The eighty-four answers behind the chart above, one cell each: where Workable and LinkedIn Jobs stood in it.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | | ||||
| GPT-5.4 mini | | | ||||
| Gemini 3.5 Flash | | | | |||
| Perplexity Sonar | | | | |||
| Grok 4.20 | | | | |||
| Mistral Small | | | ||||
| DeepSeek V4 Flash | | | | |||
| Llama 4 Maverick | | | | |||
| Qwen 3.7 Flash | | | ||||
| Kimi K2 | | | | |||
| GLM 4.7 FlashX | | | | |||
| MiniMax M2.5 | | | ||||
| GPT-6 Luna | | | ||||
| Muse Glimmer 30B | | | |
Workable LinkedIn Jobs first choice named as an alternative argued againstblank: not namedEach cell is one answer, Workable on the left and LinkedIn Jobs on the right.The direct prompt
The plain question, one answer per model, grouped by where Workable and LinkedIn Jobs stood in it.
Neither was named
14 of 14 modelsThe answer made no first choice from these two in this category.Claude Haiku 4.5Appcast, Joveo alternatives: LinkedIn Ads, Recruitics, StackAdapt
GPT-5.4 miniJoveo alternatives: JobTarget, PandoLogic, Veritone Hire
Gemini 3.5 FlashPandoLogic alternatives: Appcast, Joveo, Recruitics
Perplexity SonarJobTarget alternatives: Appcast, Joveo, Recruitics
Grok 4.20Joveo alternatives: Appcast, JobTarget, Talroo, Veritone Hire
Mistral SmallAppcast, JobTarget alternatives: Joveo, Radancy
DeepSeek V4 FlashJobTarget alternatives: Appcast, Joveo
Llama 4 MaverickAppcast alternatives: JobTarget, Recruitics
Qwen 3.7 FlashAppcast alternatives: Joveo, PandoLogic
Kimi K2Appcast, JobTarget alternatives: Talroo
GLM 4.7 FlashXAppcast, Joveo alternatives: JobTarget, Recruitics, VONQ
MiniMax M2.5Joveo alternatives: Appcast, JobTarget, Recruitics
GPT-6 LunaJobTarget Programmatic+ alternatives: Appcast
Muse Glimmer 30BAppcast, JobTarget Programmatic+, Joveo alternatives: JobAdX, Radancy, Recruitics, Veritone Hire Programmatic formerly PandoLogic
Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.
By buyer segment
The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Level: the same share of first choices.
Workable2%#7 of 14LinkedIn Jobs2%#6 of 14Mid-marketThe figures above
Workable leads by eleven points.
Workable13%#4 of 13LinkedIn Jobs2%#8 of 13Enterprise
Level: the same share of first choices.
Workable0%#– of 16LinkedIn Jobs0%#14 of 16What the models said about Workable
No label in this category carried a quote.
What the models said about LinkedIn Jobs
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Scammers can post or impersonate employers on mainstream platforms too—including Indeed and LinkedIn—so treat the *listing and recruiter* as unverified until you check them.” GPT-6 Luna · negative prompt · soft negative
“extra cautious on any platform where anyone can post jobs easily—including big sites like Indeed, LinkedIn, and social media” GPT-5.4 mini · negative prompt · soft negative
“it's not immune—scammers create fake profiles or ads mimicking real companies... Privacy risks are higher due to data sharing.” Grok 4.20 · negative prompt · soft negative
“For Hard-to-Fill Corporate or Executive Roles: Choose LinkedIn to search for and proactively reach out to passive candidates” Gemini 3.5 Flash · comparative prompt · first choice
“LinkedIn is considered the best overall job search site due to its reach and networking opportunities” Llama 4 Maverick · comparative prompt · first choice
“For most employers, start with a combo: LinkedIn for targeted roles + Indeed/ZipRecruiter for volume.” Grok 4.20 · comparative prompt · first choice
Also compared
Appcast vs WorkableAppcast vs LinkedIn JobsJobTarget vs WorkableJobTarget vs LinkedIn JobsJoveo vs WorkableJoveo vs LinkedIn JobsWorkable vs IndeedWorkable vs ZipRecruiterWorkable vs BroadbeanIndeed vs LinkedIn JobsZipRecruiter vs LinkedIn JobsBroadbean vs LinkedIn JobsAll twenty-eight head-to-head pages and the full job advertising standing
Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.