GitHub Copilot vs. ChatGPT: Developer AI Tools Comparison

GitHub Copilot is an AI-powered code completion tool developed by GitHub in collaboration with OpenAI that offers real-time code suggestions. Think of it as your coding partner, one that reads your code and suggests lines or even entire blocks of code as you work. It’s designed to help you write code faster, reduce repetitive tasks, and give you a smoother coding experience.

GitHub Copilot features

The following are some key features of GitHub Copilot:

• Code suggestions: Copilot provides real-time suggestions that can help you complete lines of code or even write entire functions.
• Multi-language support: It supports multiple programming languages, such as Python, JavaScript, and even markup languages like Markdown, making it versatile for different projects.
• Context awareness: It understands the context of your code. This means it can provide more relevant context-aware code suggestions based on the code you’ve already written.

How does GitHub Copilot work?

Let’s see an example of how GitHub Copilot creates an AWS EC2 instance on Visual Studio Code.

GitHub Copilot is an extension that integrates directly into your favorite code editor, such as Visual Studio Code, Neovim, or JetBrains IDEs. Once installed, interacting with Copilot is as simple as writing code — just start typing, and it will suggest lines or blocks of code based on what you’re working on.

After you install Copilot, it runs alongside your code editor. As you type, Copilot monitors your code in real time and offers suggestions in the editor’s interface, which you can accept, modify, or ignore. It becomes an interactive assistant, constantly learning from your input and adjusting its suggestions to match your coding style and project needs.

Under the hood, GitHub Copilot is powered by Codex, a 12-billion-parameter model developed by OpenAI. Parameters in machine learning models are essentially the “memory” that enables the model to identify patterns and make intelligent predictions.

The 12B parameters give Codex the capacity to generate contextually relevant suggestions by analyzing large datasets of publicly available code. This fine-tuning process ensures it performs better than its predecessor, GPT-3, at code-specific tasks.

Codex itself was trained on 159 GB of Python code sourced from 54 million public GitHub repositories. This allows it to deliver high-quality code suggestions and solve complex programming problems with impressive accuracy.

ChatGPT is an AI-powered conversational model developed by OpenAI. It’s designed to engage in human-like conversations, answer questions, provide explanations, and even help brainstorm ideas. ChatGPT is versatile and can be used for a wide range of applications, from chatbots to content generation.

ChatGPT features

Some key features of ChatGPT include:

• Conversational AI: ChatGPT is designed to respond in a way that feels like you’re talking to another person. This makes it ideal for casual chats or more complex discussions.
• Wide knowledge base: It has been trained with tons of text data from books, websites, and other sources, which allows it to provide answers on a wide range of topics.
• Customizability: You can fine-tune ChatGPT to generate responses that match your desired tone, style, or content.
  

How does ChatGPT work?

In the example below, ChatGPT responds to a prompt to create an EC2 instance using Terraform.

ChatGPT works by utilizing a type of deep learning model called GPT (Generative Pretrained Transformer). It processes input text by analyzing the words you provide and predicting the most appropriate response based on patterns it has learned from vast datasets. You can interact with 

ChatGPT, through an API or a web interface, provides prompts or questions that generate text-based responses.

When you provide a prompt, question, or statement, ChatGPT processes this input to understand the context and intent behind your message. Based on its training, ChatGPT generates a response that’s coherent and relevant to your input. It predicts the next word or phrase in a sequence, refining its output step-by-step to form complete and natural sentences.

Under the hood, ChatGPT is powered by a massive language model with 175 billion parameters (in the GPT-3 variant). These parameters determine how well the model can generalize patterns from its training data and adapt to various prompts. Additionally, ChatGPT uses a metric called perplexity during training to evaluate how well the model predicts a sequence of words — the lower the perplexity, the better its predictive capabilities.

Now that we’ve explored the features and workings of GitHub Copilot and ChatGPT, let’s highlight some key differences between the two tools.

The primary distinction between GitHub Copilot and ChatGPT lies in their core functionality. GitHub Copilot is designed to assist developers with code completion and generation, whereasChatGPT is focused on generating human-like text responses and engaging in conversations.

Additionally, the following points outline some key differences between GitHub Copilot and ChatGPT:

1. Use case

GitHub Copilot is primarily used within code editors for code generation and completion. n the other hand, ChatGPT can be used in various platforms for general-purpose conversations and problem-solving across different domains.

Copilot is most effective when embedded within an Integrated Development Environment (IDE), whereas ChatGPT can be used on websites, applications, and APIs to support a wide range of workflows, from documentation writing to brainstorming.

2. Input and output

Copilot processes code snippets and provides relevant code suggestions, whereas ChatGPT works with text prompts and generates text-based responses.

This means Copilot’s primary focus is understanding and generating structured code that aligns with existing programming syntax and logic. It suggests lines or blocks of code based on the project context. 

ChatGPT generates freeform text responses, making it more suitable for answering conceptual questions, explaining coding concepts, or engaging in general discussions. 

Whereas Copilot delivers precise, functional code snippets, ChatGPT provides a broader, more flexible interaction model.

3. Training data

GitHub Copilot is trained on open-source code from GitHub repositories. However, ChatGPT is trained on a diverse range of text data from books, websites, and other sources.

Because Copilot’s training is rooted in publicly available source code, it is highly proficient at suggesting relevant coding patterns, functions, and best practices across multiple programming languages. However, its knowledge is code-specific and does not extend much beyond software development.

ChatGPT has been trained on a vast amount of diverse text, making it more capable of handling topics beyond programming, such as business, science, creative writing, and general problem-solving. 

4. Interaction

The interaction with each tool differs significantly. GitHub Copilot integrates directly into code editors, providing suggestions as you code. Meanwhile, ChatGPT can be accessed via APIs or chat interfaces.

Copilot’s seamless integration into IDEs allows it to function as a real-time coding assistant, offering inline suggestions that developers can accept, modify, or ignore as they work. This makes the experience fluid and intuitive, as it adapts to the ongoing code without requiring explicit input from the user. 

ChatGPT operates through a prompt-based system, where users must actively type questions or requests to receive responses. It can be accessed via web interfaces, APIs, or chatbots, making it a more interactive and dialogue-driven tool rather than a background assistant.

5. Context

GitHub Copilot provides code suggestions based on the context of your code, while ChatGPT generates responses based on the input text and the patterns it has learned from training data.

This means Copilot is highly contextual within the coding environment, analyzing the specific file, function and surrounding lines of code to provide relevant suggestions. It understands programming logic and adapts its recommendations accordingly. 

However, ChatGPT does not retain detailed contextual awareness between interactions unless specifically designed to do so (e.g., in multi-turn conversations). Instead, it relies on pattern recognition from its training data to generate responses.

6. Code generation

GitHub Copilot specializes in real-time code generation, offering developers inline suggestions for functions, loops, and entire code structures as they write. It leverages patterns from publicly available open-source code to provide context-aware completions, making coding faster and reducing manual effort. 

Whereas Copilot can generate code line-by-line or in small blocks, ChatGPT can produce larger code snippets or full scripts based on a descriptive prompt. However, because ChatGPT lacks direct IDE integration, users must manually copy and paste its output.

GitHub Copilot and ChatGPT serve different purposes, but they share several similarities due to their underlying AI technology. Both tools are powered by models developed by OpenAI.

The following are some key similarities between GitHub Copilot and ChatGPT:

• AI-powered: Both GitHub Copilot and ChatGPT are driven by AI models built on OpenAI’s technology. This allows them to analyze input and provide intelligent, context-aware suggestions and responses.
• Deep learning models: GitHub Copilot and ChatGPT are powered by advanced deep-learning models developed by OpenAI. GitHub Copilot is based on Codex, while ChatGPT is built on the GPT series of models (e.g., GPT-3.5 or GPT-4).
• Assistive tools: Both tools are designed to assist users. GitHub Copilot helps developers looking to speed up their coding process, and ChatGPT assists individuals seeking information or content creation.
• Productivity tools: They are designed to enhance productivity by automating tasks and providing quick and relevant information.
• Continuous improvement: Thanks to ongoing updates and improvements by OpenAI, both tools continue to evolve. For example, GitHub Copilot has seen enhancements in context awareness, allowing it to better adapt its suggestions based on larger codebases. Similarly, ChatGPT has been updated to include multi-turn conversation tracking, enabling it to handle more complex dialogues.

To make it easier to understand the key differences and similarities between GitHub Copilot and ChatGPT, here’s a quick comparison in table format:

When it comes to choosing between GitHub Copilot and ChatGPT, it depends on the task at hand. Both tools serve slightly different purposes, so their value really depends on your specific needs and situation.

If you’re a developer working within a code editor, GitHub Copilot is clearly the better choice. Its focus on code completion and generation makes it a powerful tool for writing code. It speeds up your workflow and reduces repetitive coding tasks. Copilot works really well in environments where the goal is to write code quickly, troubleshoot issues, or explore different coding approaches, all while staying within the context of your current project.

On the other hand, if you’re looking for a tool that can engage in dynamic conversations, answer questions, or help with a wide variety of tasks beyond coding, ChatGPT is the one to choose. It excels in areas like customer support, brainstorming ideas, and even helping you learn new things by providing detailed explanations. ChatGPT’s versatility in handling a wide range of topics makes it ideal for tasks that require a more conversational, text-based approach.

So, rather than pitting these tools against each other, it’s important to think about which tool will help you achieve your specific goals. GitHub Copilot is your go-to for coding, while ChatGPT is more suited for tasks requiring conversational AI or general problem-solving.

If you’re exploring alternatives to GitHub Copilot and ChatGPT, here are some options worth considering:

• Claude: Developed by Anthropic, Claude is an AI assistant designed for safe and reliable conversations. Like ChatGPT, it can generate text and answer questions, but it focuses on being more user-friendly and ethical.
• Gemini: Formerly known as Bard, Gemini is Google’s answer to conversational AI. Like ChatGPT, it can answer questions and generate text-based content on a variety of topics.
• Microsoft’s Copilot (Office 365): Integrated into Microsoft Office products, this  AI-powered tool helps with document writing, analysis, and task automation within applications like Word, Excel, and Outlook.
• Tabnine: Though similar to GitHub Copilot, this AI-powered code completion tool supports more programming languages and integrates with different IDEs, including Visual Studio Code and IntelliJ.
• Replit: This coding assistant provides code suggestions and completions within the Replit platform. It uses AI to help developers write code more efficiently, much like GitHub Copilot.

Read more: 17 Best AI-Powered Coding Assistant Tools

Spacelift is an infrastructure orchestration platform that improves developer velocity by offering a powerful policy engine based on OPA, self-service infrastructure, and the ability to build multi-tool workflows with dependencies and output sharing. Spacelift has its own Terraform/OpenTofu provider, and also its own Kubernetes operator which makes it ideal to pair it with an AI-powered coding assistant.

You can easily have AI-powered coding assistants generate Spacelift Terraform/OpenTofu/Kubernetes code by showing them how you would like the code generated (for example, you want to use for_each on your resources and map(objects) variables when you are using Terraform/OpenTofu). 

Read more about how Spacelift can help you with your infrastructure orchestration workflows here. If you want to take your infrastructure automation to the next level, create a Spacelift account today or book a demo with one of our engineers.

GitHub Copilot and ChatGPT are powerful AI tools, each serving a unique purpose.

GitHub Copilot is designed to assist developers by speeding up the coding process with context-aware code suggestions, whereas ChatGPT excels at providing human-like text responses across a wide range of topics. Although they have different focuses, both tools share a common goal: to make your work easier and more efficient.

These AI-powered assistants can help you in different areas — whether you’re coding, engaging in conversations, or creating content. 

The key is to pick the right tool for the job. Whether it’s Copilot for your development needs or ChatGPT for more versatile problem-solving, both can boost your productivity and help you achieve more with less effort.