With the pervasive use of large language models (LLMs) across various domains, addressing the inherent ableist biases within these models requires more attention and resolution. This paper examines ableism in three LLMs (GPT-3.5, GPT-4, and Llama 3) by analyzing the intersection of disability with two additional social categories: gender and social class. Utilizing two task-specific prompts, we generated and analyzed text outputs with two metrics, VADER and regard, to evaluate sentiment and social perception biases within the responses. Our results indicate a marked improvement in bias mitigation from GPT-3.5 to GPT-4, with the latter demonstrating more positive sentiments overall, while Llama 3 showed comparatively weaker performance. Additionally, our findings underscore the complexity of intersectional biases: These biases are shaped by the combined effects of disability, gender, and class, which alter the expression and perception of ableism in LLM outputs. This research highlights the necessity for more nuanced and inclusive bias mitigation strategies in AI development, contributing to the ongoing dialogue on ethical AI practices.