ChatGPT vs. GitHub Copilot as AI Coding Environments: Which One to Choose

The use of GenAI tools such as ChatGPT vs. GitHub Copilot for coding has become a common optimization approach in custom software development solutions. According to the Stack Overflow 2025 Developer Survey, 84% of engineers are currently using or plan to use AI tools for assistance, up from 76% in the previous year.

 When asked which out-of-the-box agents and copilots they use for software development, 82% of respondents named ChatGPT, and 68% preferred GitHub Copilot. The popularity of these AI coding assistants means that working with GenAI tools has become a must-have skill for software engineers. But which one is better, and how do GitHub Copilot vs. ChatGPT handle different tasks?

This overview provides a ChatGPT vs. GitHub Copilot comparison, helping you understand the capabilities and limitations of each tool. Learn when to use them and how to adopt AI workflows within your engineering team.

Overview of GitHub Copilot: Strengths and Tradeoffs

GitHub Copilot is an AI-powered tool that provides code snippet suggestions and completes code written in an editor in real time. Developed by GitHub and OpenAI, it relies on large language models (LLMs) trained on publicly available code to predict the next lines or even entire blocks of code. The AI coding assistant takes into account the context of the code, making its completions highly relevant. Besides code recommendations, GitHub Copilot also helps with documentation comments and commit messages. 

Benefits of Using GitHub Copilot for Coding

• Syntactically correct code. The comparison of GitHub Copilot vs ChatGPT capabilities by the G2 software review service shows that 88% of respondents value Copilot for its code quality. Trained on large volumes of public code patterns, it follows the best practices and offers clear and relevant suggestions that are easy to reuse.
• IDE native. Copilot is integrated into your development environment (e.g., JetBrains, Visual Studio, or VS Code), which makes it convenient to use and minimizes the need to switch between multiple tools. Engineers see suggestions in real time while typing.
• Context awareness. The tool captures the broader context by taking into account the current file, typed code, variable names, function signatures, and project structure. This functionality ensures more relevant and better-formatted suggestions that match existing code.
• Test and documentation help. Copilot enables unit testing automation and streamlines documentation management by writing inline comments and explanations to code behavior.
• Optimized boilerplate code creation. GitHub Copilot is great for generating repetitive and predictable code, which is time-consuming if done manually. Engineers can focus on business logic and problem-solving while the tool fills in basic boilerplate code.
• Multi-language support. The tool supports multiple engineering languages and frameworks (e.g., Python, JavaScript, TypeScript, Go, Rust, Java, C#) and can be used for full-stack projects that require switching between languages.

Limitations of GitHub Copilot

• Misleading suggestions. Despite the high accuracy of GitHub Copilot, it’s an assistant, not a fully automated tool. It may offer faulty logic or incorrect code that requires human-led review and testing.
• Security weaknesses. Without supervision and testing, the tool may unintentionally release confidential information or suggest vulnerable code that undermines overall software security if left unattended
• Lack of deep understanding. Copilot generates suggestions based on existing coding patterns and context. It fails to understand the business logic or domain rules, requiring the critical thinking and guidance of engineers.

Overview of ChatGPT: Strengths and Tradeoffs

ChatGPT is a universal GenAI conversational model trained on diverse datasets that supports coding among many other tasks. It engages in human-like conversations to answer questions, explain different concepts, and provide structured guidelines. When it comes to using ChatGPT as an AI coding environment, it’s an effective tool to generate code from natural language prompts, refactor code, and prototype algorithms.

When GitHub Copilot was launched in 2021, it ran on OpenAI’s model called Codex. In 2023, it transitioned to OpenAI’s more advanced GPT-4 model during the Copilot X upgrade. As of 2025, OpenAI Codex is a standalone product available through the sidebar in ChatGPT.

Benefits of Using ChatGPT as a Coding Environment

• Use of natural language prompts. ChatGPT is a conversational AI model that enables engineers to write their request in English or any other language and receive the corresponding code. No need to remember complex syntax. The knowledge of prompt engineering for devs is enough for basic coding tasks.
• Logic explanation. ChatGPT turns tricky code lines and blocks into plain language explanations, helping engineers understand recursion, closures, and algorithms. It enables engineering teams to prevent development bottlenecks and simplify the coding process.
• Architectural advice and self-learning. ChatGPT relies on large volumes of data to provide engineers with real-time guidance on best coding practices, project structure, possible mistakes to avoid, and other common questions.
• Project prototyping. Engineers can use the chat to get a high-level representation of features before implementing them in the IDE. It also generates README files, test scripts, and usage examples for quick testing.
• Code refactoring. ChatGPT coding capabilities enable engineers to improve their existing code, eliminating repetitions or excessively long structures. It results in cleaner code that is easier to maintain and improves software performance.
• Extensive knowledge base. Trained on multiple sources, ChatGPT knows libraries, frameworks, and common patterns. When comparing GitHub Copilot vs. ChatGPT coding, ChatGPT is more suitable for seeking advice, brainstorming, and breaking down complex concepts into simpler explanations.

Limitations of ChatGPT for Coding

• Not integrated into the coding environment. Engineers need to copy and paste code manually or use additional coding plugins to avoid going back and forth between tools.
• Risk of using outdated approaches. ChatGPT may offer recommendations or generate code relying on obsolete information and coding practices due to its extensive knowledge base used for training.
• Licensing issues. The model may include patterns similar to public code, complicating the licensing process. Therefore, manual review and human supervision are essential.

ChatGPT vs. GitHub Copilot Comparison

Now that you better understand the strengths and tradeoffs of each solution, let’s cover GitHub Copilot vs. ChatGPT differences. While GitHub Copilot is best for code autocompletion directly in the editor, ChatGPT is a conversational assistant for understanding software development nuances and generating ideas. Follow this concise table to determine which solution meets your engineering needs.

Despite the differences, these tools have the same goal as both of them aim to optimize the software development process and developer productivity metrics. ChatGPT and Copilot share such vital features as AI-powered code generation, documentation assistance, and multi-language support. Your task is to find the most suitable application for each tool and make sure the engineering team uses AI coding IDEs with operational security in mind.

When to Use GitHub Copilot vs ChatGPT

The choice between ChatGPT vs. GitHub Copilot for pair programming is not about adopting a single tool for the entire software development process. They are both great for different tasks and engineering stages. You can use the solution that best solves your current problems or combine the tools for maximum efficiency. 

GitHub Copilot Use Cases

GitHub Copilot is considered a better choice than ChatGPT to generate boilerplate code and provide relevant coding suggestions inline within your IDE. It takes into account the context and generates code mimicking the same patterns and style, which considerably accelerates routine development tasks. Copilot enables much faster function or method generation compared to ChatGPT and can generate unit tests, filling in test cases. It also has refactoring capabilities useful for raw code drafts.

ChatGPT Use Cases

ChatGPT can help engineering teams with more strategic tasks, such as designing system architecture. Users provide a detailed prompt to obtain an architecture tailored to the specified uses with tech stack recommendations. Another common use case is initial prototype design. ChatGPT generates and reasons multiple ideas, defines an MVP scope, and differentiates between the core functionality and nice-to-have features. ChatGPT for code development is also helpful to generate detailed explanations, tutorials, and docstrings supporting the written code.

If you wonder which tool is better, there is no specific answer. The choice depends on your expectations from an AI-powered coding assistant and practical needs. GitHub Copilot is a recommended option for engineering teams who want increased productivity within an integrated development environment. ChatGPT is suitable for answering plain language questions, testing different ideas, and finding reasoning behind coding-related issues.

Tips on Adapting Your Engineering Team to AI Workflows

Beetroot has been at the forefront of AI coding implementation since the first GenAI releases and the growing popularity of GenAI services. We run custom AI workshops, teaching engineering teams how to use ChatGPT and other AI assistants to streamline core engineering tasks. 

If your team is still not ready to adopt AI into their workflows, our practical recommendations should help you become a step closer to AI-powered coding. Consider these best practices within your project:

Understand Pain Points Before Offering AI Solutions

If you have an established engineering team that has been working on your project for a while, they are probably facing the same issues over and over. Talk to your engineers to know their pains and select an AI-coding assistant accordingly. It will make your team members feel heard and reduce hesitance toward AI adoption since new workflows will simplify people’s lives. 

Note: It’s better to begin with low-risk use cases as your team is adapting to prevent the impact of security and business logic. Unit test generation, documentation, and internal scripts are a good start to test how AI works for your project.

Define and Document Clear AI Roles and Establish Governance

Standardize which tools to use in specific cases (e.g., GitHub Copilot for inline coding) and specify the responsibility for human oversight and final quality assurance. Inform team members about the changes and routine workflows and make sure they know their new roles. 

Such preparation will prevent overautomation and make engineers step in when needed. It’s also better to document the updated workflows to make sure each team member knows how AI affects their work and keep them accountable. 

Run Custom Training Sessions with Practical Workshops

Nurture prompt literacy within your team, teaching engineers to phrase prompts for high output quality. They should also know how to evaluate the code suggested by AI tools to detect security vulnerabilities and inaccuracies before deployment. You can engage an external vendor for custom tech workshops or encourage knowledge-sharing within your team to build the necessary expertise.

Summary on Top AI Coding Tools

The review of these best AI coding tools shows that there is no universal solution for automating the coding process. GitHub Copilot and ChatGPT can complement each other for different tasks or be used separately, depending on which capabilities are more critical in your AI coding environment. We recommend GitHub Copilot if you’re looking for speed and efficiency within the IDE. ChatGPT is generally considered a better choice for coding tasks that require explanation and reasoning, including debugging, planning, and prototyping.

Mind that GitHub Copilot and ChatGPT cannot fully automate coding operations. These are coding assistants that support and streamline the software development process, but still require human supervision to ensure the end product quality, as well as security and code privacy in AI tools. You can hire engineers with AI coding expertise from Beetroot or invite our experts to train your in-house team on the latest AI coding practices. Fill out the contact form to discuss your project and get help with optimizing development.

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