Devin vs Copilot: Which is Best?

AI coding assistants have moved from novelty to daily necessity. They help developers write code faster, reduce repetitive work, and keep projects moving. Two names come up in almost every conversation about coding AI today: Devin and GitHub Copilot. They share the same goal of making developers more productive, but they take very different paths to get there. Devin aims to behave like an AI software engineer who can execute tasks end-to-end. Copilot focuses on real-time assistance inside your IDE, giving smart suggestions as you type.

This guide explains how the two tools compare, where each one excels, and how to decide which one fits your workflow. It follows a simple structure with clear sections so you can scan, evaluate, and choose with confidence.

What is Devin?

Devin is positioned as an autonomous AI developer rather than a traditional autocomplete tool. You do not use it only for inline code suggestions. You assign it a well-scoped task, and it runs with it. Devin plans the steps, edits code across files, runs builds and tests, handles retries, and opens a pull request when it is done. It communicates progress in channels like Slack and can ask for clarification when needed. Many teams describe it as adding a junior engineer who works in the background and reports back with a proposed change.

Devin operates as a cloud service that integrates with your repository and collaboration stack. It works best when the task is straightforward, the acceptance criteria are explicit, and the target codebase has a reliable test suite. In those conditions, Devin can deliver meaningful changes without frequent human intervention. It is not a replacement for engineers. You still review the pull request, run your quality gates, and decide what ships. Devin is an accelerator that removes busywork and frees people for higher-level design and problem-solving.

Devin is newer than GitHub Copilot and is still maturing. Early adopters appreciate the autonomy and the ability to offload boilerplate-heavy efforts. They also note that complex problems can take time and that Devin may loop on failing tests before it succeeds. That tradeoff is typical of agent-style tools. You exchange immediate keystroke speed for the value of offloading an entire task.

Features

• Autonomous task execution: Accepts a natural language request, plans the approach, edits multiple files, and creates a branch and pull request.
• Pull request automation: Includes a clear summary of changes, context of decisions, and follow-up steps if needed.
• Team collaboration: Share progress updates on Slack or similar tools, allowing stakeholders to follow along and contribute.
• Long-term project memory: Remembers conventions and past fixes across sessions where supported, improving consistency over time. But it is not guaranteed for every repo.
• Integrated tools: Uses a command line, a browser, and project scripts to run builds, execute tests, and read documentation.
• Enterprise options: Supports secure deployment patterns, role-based access, and compliance controls so that sensitive code stays protected.

What is Copilot?

GitHub Copilot is the most widely adopted AI coding assistant. It integrates into editors like VS Code, JetBrains IDEs, and Visual Studio. Its job is straightforward. Copilot predicts what you want to type next and offers code suggestions in real time. You accept what is useful, edit what is close, and reject what does not fit. Developers keep complete control. Copilot historically does not commit or run code on its own. However, newer features from 2025, like Agent Mode (currently in preview), allow Copilot to handle multi-file edits and scripted workflows.

Copilot shines during everyday coding. It is excellent at filling in boilerplate, writing small utilities from comments, suggesting tests, and nudging you toward idiomatic patterns in a language or framework. It also includes Copilot Chat, which lets you ask questions about errors, request refactors, or get an explanation for a tricky function, all without leaving your editor.

GitHub Copilot does not replace unit tests, peer review, or architectural thinking. It improves flow. It helps you stay in the code rather than in a browser searching for examples. That simple focus is why adoption is so high across companies, classrooms, and open source projects.

Features

• Real-time suggestions: Autocomplete single lines, functions, or blocks of code based on the active file and recent context.
• Context awareness: Uses surrounding code and comments to tailor the suggestion to your intent.
• Broad language coverage: Works across the major languages and many niche ones as well.
• Tight IDE integration: Install a plugin, sign in, and start coding—no heavy setup.
• Copilot Chat: Ask for explanations, fixes, or improvements inside the IDE.
• Growing ecosystem: Features for command line help and pull request assistance extend utility beyond typing.

Copilot's suggestion quality can vary, and developers should review outputs carefully. Studies from Cornell University have shown that AI-assisted code can sometimes introduce insecure or incorrect patterns.

Devin vs GitHub Copilot: Table Comparison

Aspect	Devin (AI Software Engineer)	GitHub Copilot (AI Pair Programmer)
Approach	Autonomous agent that plans, codes, tests, and creates pull requests	Inline IDE assistant suggesting code in real time
Workflow	Works outside the IDE via GitHub, Slack, and CLI integrations	Runs inside IDEs like VS Code, JetBrains, and Visual Studio
Autonomy	High- executes tasks end-to-end	Medium (real-time suggestions) but expanding to higher autonomy with Agent Mode
Speed	Slower, Runs configured builds and tests, using your repo's CI setup to self-verify changes.	Instant suggestions as you type
Memory	Long-term repo context and project knowledge	Session-based only, no memory once the IDE is closed
Collaboration	Functions like a team member with PRs and Slack updates	Primarily for individuals, though team features like AI-generated PRs exist
Setup	Heavier onboarding requires repo and tool integration	Very easy, just install the extension and log in
Pricing	Pricier -Core plan starts at $20/month with pay-as-you-go usage (ACU-based). Team plan with better features starts at $500/month with bundled ACUs. Enterprise custom pricing available.	Cheaper- Free tier, $10 Pro, $19 Business, $39 Enterprise
Best for	Teams with CI/CD pipelines, vigorous testing, and a need for automation	Developers wanting affordable, real-time coding help inside the editor

Who should choose Devin

Devin fits teams that want to delegate well-defined work to an AI agent and retrieve a pull request later. It is strongest when you can write crisp acceptance criteria and when your repository has a healthy test suite and automation.

Best fit scenarios

• Startups shipping MVPs: A small team can assign Devin to scaffold a set of API endpoints, wire an authentication flow, or port a feature from an older service. While Devin works, humans tackle product decisions, UX polish, and integration risks.
• Enterprises with repetitive patterns: Large codebases often require the exact change to be applied in multiple places. Devin can implement a pattern, project scripts, and open a single coordinated pull request for review.
• Consultancies and agencies: When juggling multiple client repos, Devin can clear standardized tasks in parallel while engineers focus on custom architecture and stakeholder work.
• Backlog reduction sprints: For a queue of documented bugs and small features, Devin can process tickets with a consistent pace and format, which reduces context switching for the team.

Strengths to count on

• End-to-end execution: You hand off a task, and Devin returns a proposed change, usually verified against your existing test suite.
• Transparent collaboration: Progress and decisions are visible in Slack and in the pull request description.
• Consistency over time: Devin remembers local conventions and reuses utilities it created earlier.

Tradeoffs to plan for

• Turnaround time: Autonomy takes time. Expect minutes per task rather than seconds.
• Iteration loops: Complex failures may require human hints or smaller scopes.
• Cost and capacity: Pricing is aligned with team use rather than individual hobby use.

How to get the most from Devin

1. Write clear tasks: Define inputs, outputs, constraints, and examples.
2. Invest in tests: The stronger your CI signals, the better Devin can self-verify.
3. Start narrow: Begin with repetitive changes, utilities, or small features before attempting deep refactors.
4. Review with intent: Use code review to align on style, security, and performance. Feed that feedback back into future task descriptions.

Choose Devin if you have well-defined tasks that you'd like to automate, if you're comfortable reviewing AI-generated code, and if accelerating development through autonomous assistance aligns with your workflow. Devin is best suited for early adopters of AI in coding who want to explore the frontier of having an AI assistant as part of the development team. You should also have a tolerance for occasionally guiding or correcting the AI, especially in its current early stage, much like mentoring a junior developer. If leveraged correctly, Devin can significantly boost productivity and handle the grunt work, letting your human developers focus on more complex or creative aspects of the project.

Who should choose Copilot?

GitHub Copilot is built for developers who want immediate lift inside the editor without changing how they work. It is ideal for daily coding, quick prototyping, and learning a new stack.

Best fit scenarios

• Every day features work: Writing handlers, building forms, crafting queries, or authoring tests. Copilot fills in the obvious patterns so you keep momentum.
• Learning and exploration: When picking up a new language or framework, Copilot proposes idiomatic snippets that keep you moving while you read documentation.
• Maintenance tasks: For minor fixes and refactors, Copilot helps you write the correct code faster.
• Team-wide adoption: With minimal setup and accessible pricing, teams can roll out Copilot broadly and see immediate impact.

Strengths to count on

• Speed and flow: Suggestions appear as you think through the problem.
• Low friction: No heavy onboarding or permissions.
• Breadth: Works across many languages and frameworks out of the box.
• Community and resources: There are many examples, tips, and patterns to learn from.

Tradeoffs to plan for

• Not autonomous: You are always in the loop. Copilot does not autonomously run your tests or open PRs in standard mode, though Agent Mode is starting to explore those workflows.
• Quality variance: Suggestions can be off target. Good judgment is required.
• No persistent memory: It does not remember choices across sessions.

Copilot has no persistent memory yet, but with Agent Mode, it is beginning to handle larger multi-file changes in some contexts.

How to get the most from Copilot

1. Write intention-revealing comments: Describe what you want before you code.
2. Accept then refine: Use suggestions as drafts, then adjust for clarity and performance.
3. Keep tests close: Verify that accepted code behaves as expected.
4. Tune settings: Adjust suggestion frequency and chat behavior to match your style.

In essence, choose GitHub Copilot if you're looking for a reliable, real-time coding assistant to enhance your productivity, and you prefer maintaining direct control over the code you write. Copilot excels at making suggestions that speed up mundane coding tasks and occasionally spark new ways to solve problems, all while fitting neatly into your existing workflow. It's the practical choice for the majority of developers who want immediate benefits with minimal risk or learning curve.

Devin vs Copilot: Which Is Best?

When it comes down to declaring a "best" between Devin and Copilot, the truth is that it depends on your specific needs, workflow, and priorities. Both tools represent different philosophies of how AI can assist in software development, and each shines in scenarios where the other might not be as effective.

• Choose Devin if you want an AI that works for you. You prefer to hand a task to the tool, let it plan and execute, and then review a pull request. You value automation in your pipeline and have a backlog of well-described tasks that consume human time. You can justify premium pricing because the hours saved translate into speed and focus elsewhere.
• Choose Copilot if you want an AI that works with you. You prefer to stay in the editor and keep a tight loop between thought and code. You want a tool that is simple to adopt, fast to respond, and helpful across languages and frameworks. You value affordability and broad team adoption.

Both require review; Devin can get stuck, Copilot can mis-suggest.

Both tools are evolving fast. Devin 2.0 recently dropped its entry price to $20/month with limited features, while Copilot is rolling out Agent Mode in preview. The distinction between pair programming and autonomous engineering is beginning to blur. Many teams combine both. Copilot is the daily driver for typing speed and flow. Devin is the specialist who tackles a self-contained feature or a repetitive refactor. When used together, you get micro-level acceleration and macro-level delegation.

A simple decision frame can help.

• Scope of work: If you need multi-file changes and a pull request, lean toward Devin. If you need code right now in the current file, lean toward Copilot.
• Time profile: If you want results in seconds, Copilot. If you can wait minutes for a complete change, Devin.
• Governance: If every line must be authored and inspected by a human during creation, Copilot. If review can happen at the pull request stage, Devin.
• Budget: If you need wide coverage at a low cost, Copilot. If you can invest in offloading tasks, Devin.

The verdict: There is no one-size-fits-all answer. GitHub Copilot is best for the vast majority of developers wanting to enhance their individual coding productivity with minimal fuss. Devin is best suited for teams or developers with well-defined tasks to delegate, who want to experiment with AI-driven development on a larger scale. The definition of "best" hinges on what problem you are trying to solve. It might even come down to combining tools: using Copilot as your daily driver and Devin as a specialist tool for heavy-lift tasks.

Conclusion

Devin and GitHub Copilot both raise the bar for developer productivity, but they do it in different ways. Devin behaves like an AI teammate. It accepts tasks, works through them, and proposes changes for your review. It suits teams looking to automate well-defined work, provided they have the necessary tests and workflows to support autonomous execution. Copilot behaves like an AI pair programmer. It speeds up daily coding inside the editor, reduces friction, and helps you maintain a smooth workflow.

As the field of AI in programming continues to evolve (with other major players like Amazon CodeWhisperer, Tembo, Tabnine, Cursor, and others also in the mix), developers may find themselves blending tools or switching depending on the task at hand. Adopting AI tools is no longer a question of if but how.

Tembo in particular is notable because it goes beyond prompt-based code generation. Instead of waiting for you to ask, it continuously monitors your repository, flags issues, and even applies performance optimizations on its own. That makes it a more proactive and autonomous tool, closer to Devin in ambition. Still, with its own unique approach, whether you opt for Devin, Copilot, or a combination of both, it's crucial to integrate these tools in a way that complements your team's strengths and mitigates their weaknesses. Keep an eye on players like Tembo, which point toward a future where AI development agents are not only reactive but also proactive partners in your codebase.

If your priority is instant assistance while you write, Copilot is the right starting point. If your priority is offloading tasks and compressing delivery time for scoped work, Devin is the right tool to evaluate. Many organizations will benefit from both. Use Copilot to accelerate the craft of writing code. Use Devin to automate the plumbing and repetitive tasks that take time away from product thinking.

In conclusion, the "best" tool is the one that aligns with your development needs and enhances your workflow. GitHub Copilot and Devin represent two different paths on the spectrum of AI-assisted coding. By understanding their differences, you can make an informed decision and harness the power of AI to write better code, faster. The future of software development is likely to include both incremental assistants and autonomous agents, and knowing when to use each will be a key skill for developers in this new era.

If you're looking for something even more autonomous than traditional coding assistants, it's worth trying Tembo. It continuously monitors your codebase, flags issues, and applies fixes, almost like having a dedicated engineer working alongside you. That frees you up to focus on design and higher-level problem solving.

So when deciding Devin vs Copilot, it's worth giving Tembo a try as well. Happy coding!