  The advent of Large Language Models (LLM) has revolutionized the efficiency
and speed with which tasks are completed, marking a significant leap in
productivity through technological innovation. As these chatbots tackle
increasingly complex tasks, the challenge of assessing the quality of their
outputs has become paramount. This paper critically examines the output quality
of two leading LLMs, OpenAI's ChatGPT and Google's Gemini AI, by comparing the
quality of programming code generated in both their free versions. Through the
lens of a real-world example coupled with a systematic dataset, we investigate
the code quality produced by these LLMs. Given their notable proficiency in
code generation, this aspect of chatbot capability presents a particularly
compelling area for analysis. Furthermore, the complexity of programming code
often escalates to levels where its verification becomes a formidable task,
underscoring the importance of our study. This research aims to shed light on
the efficacy and reliability of LLMs in generating high-quality programming
code, an endeavor that has significant implications for the field of software
development and beyond.
