GitHub Copilot vs ChatGPT: which should developers use for coding?

GitHub Copilot and ChatGPT both support software development, but they fit differently into a coding workflow. Copilot integrates directly with development environments to suggest code and support coding tasks in context. ChatGPT is a general purpose AI assistant that developers also use for explaining problems and generating standalone code. For many developers, the most useful approach is to use both according to the task.

How GitHub Copilot and ChatGPT compare for coding

The difference between Copilot and ChatGPT is increasingly about workflow rather than basic capability. OpenAI also offers Codex, which supports coding tasks within development environments. Copilot is designed specifically around software development and integrates with supported development environments. ChatGPT operates as a broader AI assistant, although its coding capabilities extend well beyond producing isolated snippets.

Both support a wide range of programming languages. Developers still need the knowledge to judge whether the code produced is appropriate, regardless of which tool generated it.

The right choice therefore depends less on whether either tool can produce Java or Python and more on where you want AI assistance to sit in your development process. Developers strengthening their underlying knowledge can use Java courses or other language specific training alongside AI tools rather than treating generated code as a substitute for that knowledge.

Pricing and product features change regularly, so current plan details should always be checked with the provider before choosing a subscription.

How to use GitHub Copilot

Getting started with GitHub Copilot depends on your development environment. Copilot is available across a range of supported editors and IDEs. You need a GitHub account and access to an appropriate Copilot plan, then follow the installation or setup process for your chosen environment.

Once configured, Copilot provides suggestions as you write code. A developer can review a completion before accepting it, ignore an unsuitable suggestion or continue typing to provide more context. Copilot Chat adds a conversational way to ask about code and request changes without leaving the development workflow.

This is where knowledge of the language matters. Someone using Copilot to write Python still needs to recognise whether a suggestion makes sense within the application. Python courses build the programming knowledge needed to assess AI generated suggestions rather than accepting them simply because they compile.

The same principle applies to more advanced Copilot features. Greater automation increases the importance of reviewing what the tool has done and understanding the consequences of proposed changes.

Where both tools fall short

AI coding tools produce plausible output very quickly. Plausible, however, is not the same as well designed.

We demonstrated this distinction in our Coding with AI webinar by asking GPT-4 to create a simple object oriented Java program. The generated program produced the expected results, yet closer examination raised questions about several design choices.

For example, the AI described a final class as contributing to immutability even though that modifier alone does not make its instances immutable. When challenged about other decisions, the assistant altered parts of the code despite not being asked to rewrite them. Some explanations also relied on assumptions that had not been specified in the original requirement.

The demonstration illustrates why successful execution is an incomplete test of AI generated code. A program may run and produce the expected output without every design decision being appropriate.

Neither Copilot nor ChatGPT removes the need to understand programming fundamentals. Developers need enough knowledge to interrogate generated code and recognise when an explanation does not stand up to scrutiny. Unit tests are valuable, but they do not necessarily expose questionable design choices.

Which should you choose?

GitHub Copilot makes sense when you want AI closely integrated with day to day development. Its inline suggestions reduce the need to move away from the editor, and its wider coding features extend that support beyond autocomplete.

ChatGPT is useful when the task benefits from a broader conversation. A developer might use it to unpack an unfamiliar concept or explore why an approach behaves in a particular way. It is also useful for working through a problem before taking the solution back into the development environment.

There is no requirement to choose one exclusively. A developer might use Copilot during implementation and turn to ChatGPT when they want to explore a problem in greater depth. The important skill is knowing how to evaluate the response from either tool.

AI makes code generation easier. It does not make programming knowledge optional. Our Coding with AI course focuses on using these tools with the technical understanding needed to question their output and make informed decisions.

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