Cursor vs. Windsurf vs. GitHub Copilot: Hands‑On Comparison of Three AI Code Editors

2024 marks a surge in AI programming tools, and many newcomers rely on AI to start coding. This article focuses on the three most popular AI code editors—Cursor, Windsurf, and GitHub Copilot—and presents a hands‑on testing methodology for readers.

Feature Overview

All three editors have converged on core functions such as code completion, conversational assistance, project‑wide context awareness, and multi‑file editing, which the author describes as the emerging optimal interaction model for AI‑assisted programming.

Test Case

The author uses the open‑source minimal web‑chat bot github.com/cssmagic/simple-chat as a test subject. The task is to add a configurable “Temperature” parameter to the bot’s settings, which requires three layers of modification:

UI : add an input field for the parameter.

Storage : persist the value locally.

Application : pass the parameter to the LLM during calls.

The three editors are then evaluated on how well they can perform these modifications through multi‑file editing.

First Contestant: Cursor

Cursor, highlighted as the most talked‑about AI editor of 2024, introduces its multi‑file editing feature called “Composer.” The test uses the model claude-3.5-sonnet-20241022 and an ambiguous prompt: “Add a field named temperature with a numeric value 0~1 in the configuration dialog.” No explicit file context is supplied, letting Cursor locate the relevant files automatically.

In the first dialogue round, Cursor correctly understands the requirement and implements UI, storage, and application changes, achieving a perfect score.

Second Contestant: Windsurf

Windsurf, released by Codeium in 2024, brands its multi‑file editing as “Cascade.” The same ambiguous prompt is used with the model Claude 3.5 Sonnet. In the first round, Windsurf modifies only the UI layer. After an additional prompt—“Continue implementing the other affected parts”—Windsurf completes the storage and application layers in a second round. The author notes that this performance is acceptable for experienced developers but may fall short for beginners.

Third Contestant: GitHub Copilot

GitHub Copilot, the pioneer of AI‑assisted coding, employs “Copilot Edit” for multi‑file editing. The initial attempt with the default prompt fails to locate the full context. Adding the directive #codebase activates its automatic search, but the second try still fails. The author then switches to the same Claude 3.5 Sonnet model used for the other tools. On the third attempt, Copilot successfully updates the UI layer. A subsequent prompt leads to the storage and application changes, matching Windsurf’s overall result.

Bonus Round: “Resurrection Test”

To account for LLM response variability, the author retests Windsurf and Copilot. Windsurf improves, completing UI and storage in the first round and application in the second. Copilot, in a single round, finishes all three layers, surprising the author with a top‑tier performance.

Conclusion

Across the practical evaluation, Cursor consistently delivers the full set of modifications in one interaction, securing the lead as the hottest AI code editor of 2024. Windsurf and GitHub Copilot both achieve the task within two rounds, with Copilot showing notable improvement on the retest. The study highlights the importance of prompt specificity and model selection when leveraging AI editors for multi‑file refactoring.