Devin AI Review 2026: We Gave the Autonomous Coding Agent Real Tasks

What is Devin AI?

Devin AI is an autonomous AI software engineer built by Cognition AI. Unlike Cursor, Copilot, or Windsurf — which sit inside your editor and help you write code in real time — Devin operates independently. You assign it a ticket from Linear, Jira, or Slack, and it goes off on its own to understand the codebase, write the code, run tests, create a pull request, and iterate on review feedback.

Think of it less as a coding assistant and more as a junior engineer who never sleeps. You describe what needs to happen in natural language, and Devin figures out the implementation. It has its own IDE, shell, and browser — a full development environment that runs in the cloud while you work on something else.

We spent three weeks testing Devin on real tasks across two production codebases: a Next.js monorepo and a Python data pipeline. We gave it migration tickets, data engineering tasks, bug fixes, and refactoring work. The results were genuinely interesting — and honestly mixed in ways that matter.

The paradigm shift here is real. With Cursor, you are the developer and AI is your copilot. With Devin, you are the project manager and AI is the developer. That distinction changes everything about when and how you should use it — and who should be evaluating it in your organization.

Key Features

Here are the six capabilities that define what Devin can actually do — and what sets it apart from the IDE-based assistants:

How Devin Works: Step-by-Step

The workflow is fundamentally different from IDE-based assistants. Here is what the actual process looks like when you hand Devin a task:

Pricing Plans

Devin's pricing is built around ACUs (Agent Compute Units) — a billing metric that bundles compute time, model inference, and tool usage into a single number. This sounds simple but gets confusing fast, because the ACU cost per task varies wildly depending on complexity.

Our honest take on pricing: The ACU model is Devin's biggest friction point. A simple bug fix might cost 2-3 ACUs ($4.50-$6.75 on Core), but a complex migration across 50 files could burn 30+ ACUs ($67.50+). Until you have run a few dozen tasks, you genuinely cannot predict your monthly bill. The Team plan at $500/month with 250 included ACUs is where the math starts to work — if your team is consistently feeding Devin 10-15 tasks per week, the per-task cost drops to a level that makes the ROI obvious.

For context: a senior engineer costs $150-250K/year fully loaded. If Devin handles even 20% of your team's ticket volume at $500/month, the economics are compelling. But if your usage is sporadic, the Core plan's per-ACU costs will feel expensive compared to a $20/month Cursor subscription.

Pros and Cons

Devin vs Cursor vs Copilot: Full Comparison

This comparison is the one everyone asks about, but it is slightly misleading. Devin and Cursor/Copilot are not direct competitors — they solve different problems. But since teams need to decide where to allocate budget, here is how they stack up:

The real answer: Most teams should use both Devin and a coding IDE (Cursor or Copilot). They are complementary, not competing. Use Cursor for your daily coding sessions where you need real-time AI assistance. Use Devin for the backlog of well-defined tickets that do not need a human sitting at the keyboard. Trying to pick one over the other misses the point.

Real-World Results: Nubank Case Study

The most compelling evidence for Devin comes from Nubank, one of the world's largest digital banks. They deployed Devin to assist with migrating a monolithic codebase of over 6 million lines of code. The numbers they reported are striking:

The key detail in the Nubank story is the fine-tuning. Their initial results were good but not exceptional. After investing time in creating detailed playbooks and teaching Devin their codebase conventions, performance improved by 4x. This matches our experience — Devin out of the box is a capable generalist, but Devin fine-tuned on your specific patterns becomes a specialist that knows your codebase better than most new hires.

The 20x cost savings figure deserves context. Nubank was comparing the cost of Devin ACUs against the cost of equivalent engineering hours for repetitive migration work. At enterprise scale with thousands of similar tasks, the math is overwhelming. For smaller teams, the savings ratio will be lower but still significant if you are sitting on migration or refactoring backlogs.

Final Verdict

Devin AI is the most capable autonomous coding agent available in 2026. It is also one of the hardest to evaluate, because it does not fit neatly into the categories most developers use to compare tools.

If you are looking for an AI assistant that helps you code faster inside your editor, Devin is not the answer — get Cursor or Copilot instead. But if your team has a backlog of well-defined engineering tasks that eat up senior developer time — migrations, refactoring, data pipeline work, dependency updates, boilerplate — Devin can handle a significant chunk of that work autonomously.

The rating of 4.0/5 reflects the tension between Devin's impressive capabilities and its real friction points. The ACU pricing model is genuinely confusing and makes budgeting difficult. The fine-tuning investment is non-trivial. The web-based IDE is a downgrade from modern editors. And the async nature means mistakes take longer to catch than they would with a real-time coding assistant.

But when Devin hits its stride — and especially after fine-tuning — it delivers results that no IDE plugin can match. Taking a Linear ticket and producing a reviewed, tested PR without any human writing a single line of code is not just a party trick. For the right team with the right workload, it is a genuine step change in engineering throughput.

Who should use Devin: Engineering teams with consistent backlogs of well-defined tasks — migration projects, data engineering, repetitive refactoring, infrastructure work. Teams with 5+ engineers where at least 20-30% of the ticket queue is addressable by an autonomous agent. Companies already invested in Linear/Jira/Slack workflows.

Who should skip it: Solo developers, small teams without clear ticket queues, anyone expecting a replacement for their daily coding editor, and teams that primarily do greenfield product development where requirements are ambiguous.