# Programming with AI: Evaluating ChatGPT, Gemini, AlphaCode, and GitHub Copilot for Programmers

arXiv: https://arxiv.org/abs/2411.09224 · HTML: https://mlsanity.com/paper/2411.09224/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 59 references that are in the map.
- First posted on arXiv 14 Nov 2024.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [Copyright Violations and Large Language Models](https://mlsanity.com/paper/2310.13771/) (2023)
- [Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering](https://mlsanity.com/paper/2401.08500/) (2024)
- [Practical and Ethical Challenges of Large Language Models in Education: A Systematic Scoping Review](https://mlsanity.com/paper/2303.13379/) (2023)
- [Refining ChatGPT-Generated Code: Characterizing and Mitigating Code Quality Issues](https://mlsanity.com/paper/2307.12596/) (2023)
- [Bias of AI-Generated Content: An Examination of News Produced by Large Language Models](https://mlsanity.com/paper/2309.09825/) (2023)
- [An In-depth Look at Gemini's Language Abilities](https://mlsanity.com/paper/2312.11444/) (2023)
- [Fairness-Aware Structured Pruning in Transformers](https://mlsanity.com/paper/2312.15398/) (2023)
- [Benchmarks and Metrics for Evaluations of Code Generation: A Critical Review](https://mlsanity.com/paper/2406.12655/) (2024)
- [NaturalCodeBench: Examining Coding Performance Mismatch on HumanEval and Natural User Prompts](https://mlsanity.com/paper/2405.04520/) (2024)
- [Gemini in Reasoning: Unveiling Commonsense in Multimodal Large Language Models](https://mlsanity.com/paper/2312.17661/) (2023)
- [The Pursuit of Fairness in Artificial Intelligence Models: A Survey](https://mlsanity.com/paper/2403.17333/) (2024)
- [Introduction to Transformers: an NLP Perspective](https://mlsanity.com/paper/2311.17633/) (2023)
- [GitHub Copilot: the perfect Code compLeeter?](https://mlsanity.com/paper/2406.11326/) (2024)

## Built on by (most cited first, 5 of 5)

- [Software Reuse in the Generative AI Era: From Cargo Cult Towards AI Native Software Engineering](https://mlsanity.com/paper/2506.17937/) (2025)
- [Behavioral Augmentation of UML Class Diagrams: An Empirical Study of Large Language Models for Method Generation](https://mlsanity.com/paper/2506.00788/) (2025)
- [ChatGPT vs. DeepSeek: A Comparative Study on AI-Based Code Generation](https://mlsanity.com/paper/2502.18467/) (2025)
- [A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection](https://mlsanity.com/paper/2508.07139/) (2025)
- [Large Language Models for Education and Research: An Empirical and User Survey-based Analysis](https://mlsanity.com/paper/2512.08057/) (2025)

## Abstract (arXiv)

Our everyday lives now heavily rely on artificial intelligence (AI) powered large language models (LLMs). Like regular users, programmers are also benefiting from the newest large language models. In response to the critical role that AI models play in modern software development, this study presents a thorough evaluation of leading programming assistants, including ChatGPT, Gemini(Bard AI), AlphaCode, and GitHub Copilot. The evaluation is based on tasks like natural language processing and code generation accuracy in different programming languages like Java, Python and C++. Based on the results, it has emphasized their strengths and weaknesses and the importance of further modifications to increase the reliability and accuracy of the latest popular models. Although these AI assistants illustrate a high level of progress in language understanding and code generation, along with ethical considerations and responsible usage, they provoke a necessity for discussion. With time, developing more refined AI technology is essential for achieving advanced solutions in various fields, especially with the knowledge of the feature intricacies of these models and their implications. This study offers a comparison of different LLMs and provides essential feedback on the rapidly changing area of AI models. It also emphasizes the need for ethical developmental practices to actualize AI models' full potential.