GPT-4 vs. GitHub Copilot: A Comparison

Introduction

As artificial intelligence (AI) continues to make strides in various domains, one area where AI-powered tools have made a significant impact is software development. Two prominent tools that have gained popularity among developers are OpenAI’s GPT-4 and GitHub Copilot. Both are designed to assist programmers in writing code, but each has its unique strengths and weaknesses. In this article, I will compare GPT-4 (and refer to it as ChatGPT occasionally) and GitHub Copilot, to help you determine which tool is better suited for your specific needs.

Photo by Tara Winstead: https://www.pexels.com/de-de/foto/hand-finger-zukunft-roboter-8386440/

GPT-4 - A General-Purpose Language Model

GPT-4, the latest iteration in OpenAI’s Generative Pre-trained Transformer series, is a highly advanced language model. It is designed to understand and generate human-like text based on a given context. Although GPT-4 is not specifically built for coding, it is capable of generating code snippets when provided with appropriate context and examples.

Key Features of GPT-4

1. Versatility: GPT-4 can generate Python code snippets, as well as assist in a variety of other tasks, including natural language processing, translations, and content creation.
2. Context Awareness: GPT-4 is designed to understand the context and generate appropriate responses based on a given input.
3. Creativity: GPT-4 can suggest alternative solutions or approaches to a given problem, which may encourage developers to explore different methods. This holds for both GPT-4 and CoPilot and is a powerful tool. We get a fresh view of a coding problem based on the power of all the training data combined.

Drawbacks of GPT-4

1. Limited Coding Accuracy: GPT-4 is not specifically built for coding, which may result in less accurate code generation compared to specialized tools like GitHub Copilot.
2. No Native IDE Integration: GPT-4 does not come with native integration for popular IDEs, making it less convenient to use during coding sessions.
3. Manual Input/Output: GPT-4 requires developers to manually provide context and extract code snippets, which may be less efficient than tools with seamless integration like GitHub Copilot.

GitHub Copilot - The AI-Powered Coding Assistant

GitHub Copilot, a collaboration between GitHub and OpenAI, is an AI-powered code completion tool specifically designed for software development. It uses a language model trained on a vast dataset of public code repositories, which allows it to suggest accurate and relevant code completions in various programming languages, including Python. Under the hood, GitHub Copilot currently uses GPT-3.5 as an engine.

Key Features of GitHub Copilot

1. Code Autocompletion: Copilot can predict and autocomplete code snippets as developers type, reducing the need to search for syntax or library documentation.
2. Multi-Language Support: Copilot supports several programming languages (e.g. C/C++, Javascript, and Python), making it a versatile tool for developers working on diverse projects.
3. Learning from Context: Copilot understands the codebase and its context, offering suggestions tailored to the specific project and coding style.
4. Integration: GitHub Copilot can be integrated directly into different IDEs, like VSCode and PyCharm.

Drawbacks of GitHub Copilot

1. Incomplete Solutions: GitHub Copilot may occasionally generate incomplete or incorrect code snippets, which requires developers to carefully review and modify the suggestions to ensure they meet their specific needs.
2. Overreliance: Using Copilot as a crutch for code completion might lead to a reduced understanding of programming concepts and dependencies, as developers might not be forced to learn underlying principles.
3. Intellectual Property Concerns: Copilot is trained on public code repositories, which may raise concerns about the potential for generating code snippets that infringe on copyrights or licenses.

Comparison

Focus

GPT-4, being a general-purpose language model, is trained on a diverse range of data, including but not limited to code. This broader training data makes it capable of handling various tasks, such as content creation, translations, and natural language processing. However, because GPT-4’s training data is not specifically focused on coding, it might not always provide the most accurate code suggestions or be as familiar with the nuances of programming languages as a specialized tool like GitHub Copilot. GPT-4’s code generation accuracy is often highly dependent on the clarity and specificity of the input context provided by the user. As a result, the accuracy of GPT-4 in generating code snippets can be somewhat inconsistent, and developers might need to manually review and modify the generated code to ensure its correctness.

On the other hand, GitHub Copilot is designed specifically for code generation and completion. It is trained on a vast dataset of public code repositories, which enables it to learn from a wealth of programming examples and patterns. This specialized training data equips Copilot with a deeper understanding of programming languages and their nuances, allowing it to provide more accurate and contextually relevant code suggestions. Copilot’s ability to learn from the codebase and adapt to a specific project and coding style also contributes to its accuracy. However, it is important to note that GitHub Copilot, like any AI-powered tool, is not infallible. It may occasionally generate incomplete or incorrect code snippets, and developers should always review the suggestions to ensure they align with the intended functionality. Overall, GitHub Copilot’s specialized focus and training data make it a more accurate tool for code generation when compared to the general-purpose GPT-4.

Integration

GitHub Copilot’s integration with IDEs offers a more seamless coding experience compared to GPT-4, which requires manual input and output.

This makes coding standard tasks quite fast and easy to do. In a lot of cases, the times searching for the correct syntax or necessary keyword are just gone. We, quite literally just have to type a comment describing what we want to achieve in the IDE editor and Copilot will do the rest.

Versatility

While GPT-4 is more versatile in terms of tasks it can handle, Copilot’s specialization in coding makes it a more efficient tool for developers. On the other hand, it is easier to have GPT-4 generate a code outline spanning multiple files and tweak them by just chatting with the engine.

To be fair, this feature is currently developed for CoPilot — however, just for VSCode — and one can sign up for the waiting list.

Cost

GPT-4/ChatGPT is developed by OpenAI, and its pricing model can be found on the OpenAI Pricing page. OpenAI offers different pricing tiers, including a free tier with limited access to their API and ChatBot.

GitHub Copilot, on the other hand, has its pricing details on the GitHub Copilot Pricing page. At the time of writing, GitHub Copilot offers a free trial for users to test the service. After the trial period, users can choose to subscribe to the GitHub Copilot plan, which has a fixed monthly fee. It is important to note that GitHub Copilot’s pricing is separate from the cost of a regular GitHub subscription, and users must pay for both services if they want to use Copilot alongside their GitHub account.

Conclusion

While both GPT-4 and GitHub Copilot offer assistance in Python coding, they cater to different needs. GPT-4 is a versatile language model that can generate code snippets along with handling a variety of other tasks, but its accuracy and integration may be limited compared to Copilot. On the other hand, GitHub Copilot is designed specifically for coding, offering a more seamless and accurate experience for developers. I use both in my daily work and find them quite helpful in what they have to over.

However, understanding underlying code principles is crucial for developers, as it helps them write efficient, maintainable, and secure code. And this cannot be replaced by either of these tools. By grasping the core concepts and design patterns, developers are better equipped to tackle complex problems, optimize their solutions, and troubleshoot issues effectively.

For example, knowledge of the SOLID principles ensures that a developer can create a well-structured and maintainable software design, promoting code reusability and reducing the likelihood of introducing bugs.

Similarly, understanding the principles of functional programming, such as immutability and higher-order functions, enables a developer to write more concise and predictable code, improving overall code quality and reducing the cognitive load required to comprehend the codebase. In summary, a solid foundation in underlying code principles is essential for any developer to master their craft and deliver high-quality software solutions.

Outlook

In the upcoming blog articles, I plan to dive deeper into the core principles that every developer should be familiar with, and will also examine how GPT-4 and Copilot can aid in applying these principles and tackling real-world coding challenges.

By combining a strong foundation in coding principles with the power of AI-driven tools, developers can elevate their skills and productivity to new heights, creating more robust and efficient software solutions.

Stay tuned for these insightful discussions and practical tips on leveraging GPT-4 and GitHub Copilot for effective problem-solving in the ever-evolving landscape of software development.