Devin vs Copilot: What’s the best AI tool for software engineers?

AI coding assistants are everywhere. From pair programming helpers to autonomous agents, the tools promising to transform software development are multiplying (and improving). Two of the most talked-about right now are GitHub Copilot and Cognition's Devin.

On the surface, both tools aim to help engineers write and ship software faster. But they operate in very different ways. Copilot traditionally acted as a silent partner in your IDE, but it's starting to take on more agentic capabilities. Devin, meanwhile, goes a step further — operating as a fully sandboxed, autonomous agent.

For senior engineers, though, the real question isn’t “which tool is more powerful?” It’s “which tool actually makes me better at my job?”

We looked at how Copilot and Devin handle real engineering tasks to see which one actually fits into a senior developer’s workflow.

What is Copilot?

GitHub Copilot, launched by GitHub and OpenAI, is designed to sit inside your code editor (like VS Code) and help you write code faster. It uses machine learning to suggest code snippets, complete lines, write functions, and even generate tests or documentation.

Copilot is well-integrated into the development workflow. It reads from your open files and editor context, making it feel like a natural extension of how you already code.

What is Devin?

Devin, created by Cognition, is a newer kind of tool. It’s being called the first “fully autonomous AI software engineer.” Rather than operating within your IDE, Devin works in its own sandbox environment with a command line, code editor, and browser. 

Cognition says to “treat Devin like a Junior Engineer.” You give Devin a task, and it goes to work, planning out steps, writing code, running tests, and even troubleshooting along the way. It can search the web, fix errors, and iterate on its own code. Devin is especially well-suited for small, well-scoped engineering tasks. 

Importantly, Devin is designed for human-in-the-loop collaboration. Engineers can follow along as it works, edit its output, or redirect its progress in real time.

Comparing Copilot and Devin by workflow

To understand which tool is better for real engineering work, let’s walk through common software development tasks and see how each one performs.

Debugging

Copilot: Copilot can help with syntax errors, missing imports, or suggesting fixes for known bugs. It can often recognize what you’re trying to do and recommend small patches or refactors. However, it doesn’t run your code or see logs.

Devin: Devin takes a more hands-on approach. It can run your code, see the errors, search Stack Overflow, try a fix, and re-run the code. In theory, this makes it more autonomous for debugging, but in practice, it still needs human oversight to verify the fixes are meaningful and don’t introduce regressions.

Verdict: Copilot is great for tight loops; Devin is more exploratory, but needs babysitting.

Documentation

Copilot: Excellent at generating docstrings, summarizing functions, and formatting markdown. It can also auto-generate README content based on your code.

Devin: Can write documentation as part of its task flow, but the quality is inconsistent. Its summaries tend to be generic and sometimes miss key context.

Verdict: Copilot wins here. It’s fast, well-integrated, and generally more accurate.

Architecture and planning

Copilot: Not its strength. Copilot focuses on implementation, not design. Copilot now does some lightweight planning, outlining steps, and generating implementation plans. But it doesn’t reason deeply about architecture or make strategic design decisions. Devin, by contrast, attempts to plan entire tasks, but its reasoning still lacks depth.

Devin: Devin attempts to plan out tasks before coding. It creates step-by-step plans and executes them. However, the depth of its planning is still limited. It can propose boilerplate scaffolding, but doesn’t replace thoughtful architecture design.

Verdict: Neither tool is ready to lead on system design. Human judgment still matters most here.

Working in existing codebases

Copilot: This is where Copilot truly shines. It uses the context of your open files, your cursor location, and your history to make relevant suggestions. You can stay in flow and write faster.

Devin: Devin struggles with large or unfamiliar repos. It may get confused by complex folder structures or domain-specific logic. Without rich context, its outputs can miss the mark.

Verdict: Copilot is far more effective when working in real, production codebases.

Speed and integration

Copilot: Fast, integrated, and low friction. You don’t need to leave your IDE or change how you work. It feels invisible when it’s working well.

Devin: Slower, more involved, and requires a separate interface. Watching Devin work can be interesting, but waiting for results — especially if it fails — is a real cost.

Verdict: Copilot is better for quick iterations. Devin is better for sandboxed experiments.

How these tools actually support your workflow

Copilot tends to fit naturally into an experienced workflow. It’s fast, unobtrusive, and helps you move through familiar tasks with less friction. It doesn't try to replace judgment, and that’s part of what makes it useful.

Devin, on the other hand, takes a bigger swing. It wants to own the task, not just assist with it. And while it can do solid work, especially with repeatable bugs or scoped features, it still struggles with the kind of nuanced decision-making that defines senior engineering work. For anything beyond basic structure or isolated bugs, Devin still requires tight boundaries and careful review. It’s not quite ready to reason about systems the way a human does.

The difference matters most when the stakes go up. When the task touches multiple domains, or when long-term maintainability is on the line, the tradeoffs between automation and control start to show. Copilot is easier to keep on a leash. Devin is more ambitious, but that ambition still needs guardrails.

What most senior engineers want from AI right now is augmentation, not automation. They want tools that make them sharper, not tools that replace their involvement. In this context, Copilot feels like a natural companion, while Devin still feels like an intern — eager but unpredictable.

When to use Copilot and when to use Devin

Use Copilot when:

• You’re writing new code and want to move faster
• You’re refactoring or cleaning up functions
• You’re documenting code or writing tests
• You want to stay in your current workflow

Try Devin when:

• You have a well-scoped, non-critical task to explore
• You want to prototype an idea quickly and don’t mind debugging the output
• You’re curious about what autonomous tools can do
• You’re working in a sandbox or proof-of-concept repo

The debate between Copilot and Devin reflects a bigger shift in software engineering. We’re moving from tools that offer autocomplete to tools that attempt to act. Some engineers will love the idea of delegating entire tasks. Others will prefer to stay closer to the work.

Senior engineers should focus on staying hands-on with these tools. Learn how they work. Understand their limits. And use them in ways that complement your skills rather than replace them.

Use the tool that helps you think better

If you’re choosing between Devin and Copilot, think less about features and more about fit. Copilot is better for tight feedback loops, fast suggestions, and everyday help. Devin is better for contained tasks where you’re open to experimentation.

Ultimately, the best tool is the one that helps you think more clearly, work more confidently, and stay in control of your craft.

If you're exploring how to use AI more effectively in your work, you're not alone. That’s why we built Ship with AI — a program for senior engineers who want to work more efficiently and improve their skills, with AI as a collaborator. 

Learn more and apply here: formation.dev/ship-with-ai