Refact.ai ranked number one on SWE-bench and I wanted to understand why so I tested it properly

When a tool ranks first on SWE-bench verified, resolving over 70% of real-world software engineering tasks, it gets my attention. I have been using Copilot and Cursor for a while and I was curious whether Refact.ai was actually meaningfully different or just benchmark-optimized. Spent a few weeks with it in VS Code and here is what I found.

The codebase context is the first thing that stands out. It does not just work from the current file or a few tagged references. It analyzes your entire codebase and fine-tunes itself to your specific project, so suggestions are grounded in how your code actually works rather than generic patterns. That difference is noticeable when you are working in a large or idiosyncratic codebase rather than a clean greenfield project.

Autonomous operation is the capability that separates it from standard autocomplete tools. You can give it a task and it plans, executes and deploys code without you micromanaging each step. The diff view shows you what changed so you can review before accepting, and file rollbacks are available if something goes wrong.

Model flexibility is a genuine advantage. You can choose between GPT-4o, Claude 3.5 Sonnet and Gemini 2.5 Pro, or bring your own API keys. For teams with model preferences or cost constraints that is practically useful rather than just a feature checkbox.

Integrations with GitHub, GitLab, MySQL, Postgres and the Chrome browser for testing round out a platform that covers the full development workflow rather than just the code editor step.