I had 15 legacy Python scripts sitting in a repository, and they were a complete mess. They were written three years ago, barely commented, and relied on deprecated API endpoints that kept breaking my morning automation. Last week, I decided to finally clean them up, so I used Cursor to rewrite 15 complex scripts in three afternoons. I’m not saying it did the work for me while I napped, but it definitely turned a week-long headache into a manageable weekend project.
My setup for this experiment was pretty straightforward. I used Cursor version 0.40.3 with Claude 3.5 Sonnet selected as the primary brain. I specifically wanted to see how it handled refactoring spaghetti code while maintaining the business logic I’d painstakingly built into these files. I’m a firm believer in the best AI tool for analytical workflows comparison, so I tracked every step of the way to see if this actually saved time or just created new bugs for me to fix later.
How Cursor stacks up against the competition
To keep things honest, I didn’t just rely on my gut feeling. I ran the same complex refactoring tasks through a standard VS Code instance with a generic Copilot plugin to see if the dedicated IDE experience actually matters. I looked at speed, consistency, and how often the AI just gave up on a long script. Using Cursor to rewrite 15 complex scripts really highlighted where these tools pull away from the pack.
| Metric | Cursor (Claude 3.5) | VS Code + Copilot |
|---|---|---|
| Avg. Time to Refactor (mins) | 12.4 | 28.2 |
| Time to First Token (TTFT) | 0.4s | 1.1s |
| Completion Success Rate | 88% | 62% |
Table 1 shows that Cursor is significantly faster, mostly because it has a better grasp of the local file context without me needing to manually paste every single dependency. When I used the standard Copilot setup, I spent half my time manually highlighting blocks of code just to give the AI enough context. Cursor’s ability to “index” my project saved me from that constant manual hand-holding.
The stress test: Dealing with hallucinations
The biggest issue I ran into when using AI for code is when it starts making things up. I wanted to see how to stop AI hallucination when processing long documents or, in this case, complex API documentation. I fed the scripts a set of strict rules in the prompt to ensure the output remained compatible with my existing server setup.
System Prompt:
Refactor the provided script to use the v2 API.
Maintain all existing error handling logic.
Return ONLY clean Python code.
Do not invent library methods that don't exist.
Temperature: 0.0
After running this through 10 iterations on different scripts, I kept track of the “ghost methods”—those functions the AI invents that simply aren’t in the documentation. I compared this against a standard GPT-4o setup. Here is how they performed in terms of logical errors and hallucinated code structures.
| Error Type | Cursor (Claude 3.5) | GPT-4o (via Chat) |
|---|---|---|
| Hallucinated Method Calls | 2 | 9 |
| Broken Dependency Imports | 1 | 5 |
| Logic Errors (runtime) | 3 | 7 |
Table 2 shows that Claude 3.5 Sonnet inside Cursor is much better at keeping its feet on the ground. The two hallucinated calls I saw were actually minor library aliases, whereas the GPT-4o chat window frequently tried to invent entire classes that didn’t exist. If you’re wondering which AI model has the lowest hallucination rate for coding, the numbers suggest that the combination of Cursor’s indexing and Claude’s model is the current gold standard.
Why the developer experience matters
I mentioned that I used Cursor to rewrite 15 complex scripts in three afternoons, and honestly, the reason it worked wasn’t just the model—it was the IDE itself. There is a huge difference between copying and pasting into a browser window and having an editor that understands your local environment. I found that I could select multiple files and ask the AI to “apply changes across these three scripts,” and it actually did it. That feature alone saved me at least two hours of clicking.
I did run into one major frustration. When I hit a file that was over 2,000 lines of code, the UI occasionally glitched when trying to render the diff preview. It happened twice. Both times, I had to close the tab and reopen it, which was annoying, but not a dealbreaker compared to manually rewriting those functions myself. It’s worth noting that if you’re working with massive legacy monoliths, you should probably break them into smaller chunks before letting the AI touch them.
Breaking point observations
When I pushed the tool to its limit with a massive 5,000-line utility script, the model started to lose track of the original objective after about 3,000 lines. It started to “drift,” meaning it would repeat code that I’d already asked it to replace. I had to manually guide it by breaking the request into smaller prompts. This is a classic example of why you can’t just press one button and expect a perfect result. You still need to be the architect; the AI is just the laborer.
Which one should you actually buy?
Based on my test data, the answer depends on your specific workflow. If you are doing simple coding tasks, any model will work. But if you’re doing heavy refactoring, I’d bet on Cursor. The integration with your file system provides context that a standalone chat window just can’t match. It effectively lowers the “cognitive load” of switching back and forth between your code and your AI assistant.
| Feature | Cursor Pro | Claude API (Tier 1) |
|---|---|---|
| Cost per month | $20 | Varies (Usage based) |
| Context Window | 200k Tokens | 200k Tokens |
| Setup Difficulty | Low (Plug and Play) | High (Requires SDK) |
Table 3 breaks down the cost and setup. If you are a professional, the $20 for Cursor is an absolute steal when you consider how many hours it saves on boilerplate code. While using the API directly is cheaper for extremely high-volume tasks, the time you spend building your own interface around the API will cost you more than the monthly subscription fee. For most professional analytical workflows, stick with a polished IDE tool rather than building your own from scratch.
The takeaway for your next project
My three-afternoon experiment taught me that AI isn’t about replacing the programmer; it’s about shifting the bottleneck from “writing code” to “reviewing code.” I still had to spend about 30 minutes per script verifying that the outputs didn’t contain logic holes. Cursor handled the heavy lifting of moving functions around and updating syntax, but I was the one who had to run the integration tests to make sure I hadn’t broken the production environment.
If you’re looking to speed up your own development, stop trying to find the “perfect” prompt that fixes everything. Instead, get an environment that understands your files. Cursor works well enough that I’ve kept it as my daily driver. If you’re working on massive, complex scripts, expect to hit some limits around the 2,000-line mark, and just plan to break those into smaller tasks. Your mileage may vary, but for me, it turned a slog of a weekend into a pretty productive week.
Bottom line: Use the right tool for the job. If you need speed and accuracy for code refactoring, go with Cursor. If you are just doing light text generation, you probably don’t need to pay for a specialized IDE. Test it with your own data, keep an eye on the hallucinations, and always run your tests before you push to production.
