Windsurf vs Cursor

The author has had subscriptions to both Cursor and Windsurf for the past 4 months.

Cursor's subscription costs $20 per month and includes unlimited tab completions.

Cursor bills by token count, where tokens represent chunks of text sent and received, and every repository line, message, and output counts toward the limit.

After hitting the token limit in Cursor, users must buy more tokens or switch to pay-as-you-go.

Using smarter models like Claude 4.0 in Cursor burns through tokens much faster and costs more.

Windsurf costs $15 per month, provides 500 prompts per month, and offers additional 250 prompts for $10.

Windsurf has its own proprietary models, and like Cursor, smarter models cost more per interaction.

Cursor is pretty expensive.

It is easy to become addicted to prompting the AI to do all the coding work.

The author now does minimal manual coding because of the AI chat interface.

The author delegates coding to the AI even when it might not save time, out of laziness.

With AI tools, a feature that used to take hours can be implemented in a matter of minutes.

Cursor is much more performant than Windsurf.

Windsurf is more conservative and appears to cut prompts short to save tokens because it uses prompt-based billing.

Cursor was easier to interact with and did what the author wanted, possibly due to better context selection.

Cursor sometimes performs extra automatic reasoning that makes it hard to track token usage.

Users can game Windsurf's prompt system by replacing the AI's command with an echo command to piggyback on a previous prompt.

The AI retains context across small prompts and knows the code changes.

Keeping the same AI context for too long when errors occur causes the AI to repeat the same mistakes.

Starting a new conversation with fresh context is more cost-efficient for fixing persistent errors.

Claude 4.0 is significantly more effective than Claude 3.7; a problem that stumped 3.7 for hours was fixed by 4.0 in one prompt.

The AI often falsely claims to have fixed code, so you must force it to test before trusting the result.

Instructing the AI to create and run a test file with the same parameters verifies whether its logic actually works.

The test script can serve as a debugging tool that helps the AI apply the correct fix to the main code.

Giving the AI a vague high-level prompt for a complex feature can lead to undesirable decisions.

Being precise as a developer enables new features to integrate cohesively with existing AI-implemented features.

Asking the AI to generate README documentation reveals what the AI is actually doing, saving the need to inspect code.

AI-generated documentation often glosses over details unless explicitly instructed to be specific.

Having detailed documentation saves time and provides clear understanding of the project when revisiting it later.

Opening multiple AI IDE windows enables parallel coding and rapid prototyping of several features at once.

Features built in parallel can later be synthesized into a single project.

The best advice is to learn coding without AI first, understanding difficulties and bugs, before using AI tools to use them correctly.

AI tools should amplify existing skills, not replace creativity, and developers should think like architects rather than just coders.

The author keeps both subscriptions because Windsurf's $10 for 250 prompts is cheaper than Cursor's pay-as-you-go, making it a cost-effective backup.