# A Comparative Study of Code Generation using ChatGPT 3.5 across 10 Programming Languages

arXiv: https://arxiv.org/abs/2308.04477 · HTML: https://mlsanity.com/paper/2308.04477/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2023.

## Built on by (most cited first, 12 of 12)

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- [Using Large Language Models to Generate Clinical Trial Tables and Figures](https://mlsanity.com/paper/2409.12046/) (2024)
- [Large Language Models for Multilingual Code Intelligence: A Survey](https://mlsanity.com/paper/2604.25960/) (2026)
- [Leveraging Large Language Models to Build and Execute Computational Workflows](https://mlsanity.com/paper/2312.07711/) (2023)
- [Can OpenSource beat ChatGPT? -- A Comparative Study of Large Language Models for Text-to-Code Generation](https://mlsanity.com/paper/2409.04164/) (2024)
- [A Systematic Evaluation of Parameter-Efficient Fine-Tuning Methods for the Security of Code LLMs](https://mlsanity.com/paper/2509.12649/) (2025)

## Abstract (arXiv)

Large Language Models (LLMs) are advanced Artificial Intelligence (AI) systems that have undergone extensive training using large datasets in order to understand and produce language that closely resembles that of humans. These models have reached a level of proficiency where they are capable of successfully completing university exams across several disciplines and generating functional code to handle novel problems. This research investigates the coding proficiency of ChatGPT 3.5, a LLM released by OpenAI in November 2022, which has gained significant recognition for its impressive text generating and code creation capabilities. The skill of the model in creating code snippets is evaluated across 10 various programming languages and 4 different software domains. Based on the findings derived from this research, major unexpected behaviors and limitations of the model have been identified. This study aims to identify potential areas for development and examine the ramifications of automated code generation on the evolution of programming languages and on the tech industry.