Large Language Models (LLMs) are gaining traction in various hiring-related tasks for both candidates and employers. For instance, employers are increasingly using LLMs to rate applicants based on the match between their resume content and job description requirements. Meanwhile, candidates use LLMs to write cover letters and tailor resume content. However, research shows LLMs can impart biases for LLM-generated content and against minorities, such as disabled candidates in hiring contexts. Thus, we conduct an observational study to investigate how widely used LLMs – OpenAI’s GPT-4, Google’s Gemini and Anthropic’s Claude – perform in supporting resume tasks for employers and candidates. Using a real-world dataset of job descriptions and resumes from disabled (n = 209) and non-disabled (n = 209) candidates, we examine the capabilities of these models across resume rating and resume generation tasks in a zero-shot setting. Our main findings show moderate alignment across models for resume matching and no significant differences in ratings between disabled and non-disabled candidates. However, we did observe increased ratings for LLM-generated resumes for GPT-4 and Claude. We discuss the implications for both candidates and employers based on our findings, aiming to promote non-biased and equitable AI-based hiring processes and motivate human-AI collaboration in the design of future hiring systems.