Benchmarking Generative AI Models for Skill-Aligned Job Posting Generation: A Multi-Domain Comparative Evaluation

Generative Artificial Intelligence (Gen AI) is reshaping recruitment, with organizations increasingly using it to draft job postings and screen candidates. Research on labor market analytics has concentrated on extracting skills from existing postings; the complementary task of generating postings aligned with a required skill set has received little systematic attention. This paper addresses that gap by benchmarking ten Gen AI models on skill-aligned job posting generation across three ESCO-derived skill domains: Finance, Healthcare, and Craft and related trades workers. Each model produced 100 postings per domain, giving 3000 synthetic postings, evaluated with five complementary metrics—Perplexity, Cosine Similarity, Word Mover’s Distance, LIWC-style divergence, and Explicitness—and verified through inferential statistical testing. No single model dominated across metrics or domains. The models prioritized explicit skills over the broader contextual information found in real postings, and performance showed a marked domain sensitivity, with a significant model-by-domain interaction for four of the five metrics. These findings offer guidance for HR practitioners and business decision-makers selecting a Gen AI tool for recruitment tasks. Recruitment outcomes such as time-to-hire and candidate–job fit were not measured, so such benefits remain potential practical implications rather than empirically demonstrated effects.