I Tracked Every AI Suggestion for 30 Days (My Spreadsheet Got Weird)

I tested 7 AI coding assistants on real Python projects for 30 days. Copilot hit 68% acceptance, but CodeWhisperer surprised me in ways I didn’t expect.

Look, I’ve been where you are. Last year, I burned three full days trying to debug a FastAPI project because my AI assistant kept hallucinating endpoints that didn’t exist. That was the moment I realized I’d been blindly trusting whatever tool had the shiniest marketing page. So I did what any reasonable person would do: I ran a 30-day stress test across seven different AI coding assistants, tracked everything in a spreadsheet my therapist would probably call “obsessive,” and now I’m sharing the results so you don’t have to suffer through the same mess.

Finding the best AI code completion tools for Python shouldn’t require a PhD in trial and error. But that’s exactly what it felt like when I started Stackweave back in the day. I’d jump between tools weekly, never really knowing which one was actually helping versus which one was just autocompleting faster garbage.

My breaking point came during a client project last quarter. I was building a web scraper for a data pipeline, and my AI assistant confidently suggested BeautifulSoup syntax that hadn’t worked since 2019. Twice. The same wrong suggestion. That’s when I decided to get methodical about this.

This article isn’t another feature comparison you can find on any vendor’s website. I actually used these tools on real projects with real deadlines. And I tracked metrics that matter: acceptance rate, debugging assistance quality, time-to-working-code, and how well each tool handled Python-specific quirks like type hints and async patterns.

My Testing Setup: 3 Project Types, 7 Tools, and Metrics That Actually Matter

Here’s how I set this up. I built three distinct projects over 30 days:

Project 1: Web Scraper (Days 1 through 10) A multi-threaded scraper pulling data from five e-commerce sites, handling rate limiting, and parsing inconsistent HTML structures.

Project 2: Data Analysis Pipeline (Days 11 through 20) Pandas-heavy analysis of 500K rows of user behavior data, including visualization with Matplotlib and statistical testing.

Project 3: REST API (Days 21 through 30) FastAPI backend with SQLAlchemy ORM, JWT authentication, and WebSocket support for real-time features.

Seven tools made the cut:

• GitHub Copilot (with and without GPT-4 mode)
• Amazon CodeWhisperer
• Cursor
• JetBrains AI Assistant
• Sourcegraph Cody
• Tabnine
• Codeium (the free option I wanted to believe in)

What did I actually measure? Here’s what mattered:

• Suggestion acceptance rate: How often did I use what the tool offered?
• First-try accuracy: Did the code work without modification?
• Context awareness: Did it understand my existing codebase?
• Library fluency: How well did it handle NumPy, Pandas, FastAPI, etc.?
• Debug assistance: Could it actually help when things broke?

I rotated tools every two to three days per project to ensure fair comparison across all three project types. Yes, it was tedious. No, I wouldn’t recommend this to anyone who values their sanity.

Copilot vs CodeWhisperer: The Real Differences

When people ask about GitHub Copilot vs. CodeWhisperer for Python, they usually want a simple answer. Here’s mine: Copilot’s better, but CodeWhisperer is sneaky good in specific situations.

GitHub Copilot

Copilot felt like having a junior developer who’d memorized Stack Overflow. Suggestions came fast, showed solid contextual awareness, and surprised me with how well they captured my intent from comments. During the API project, I’d type # endpoint to get user by email with error handling and it’d scaffold something usable about 70% of the time.

GPT-4 mode (Copilot X features) pushed accuracy even higher, especially for complex async patterns. My acceptance rate for Copilot hit 68% across all three projects.

But here’s where it stumbled: library-specific edge cases. It suggested deprecated Pandas methods more than once, and its FastAPI suggestions sometimes mixed conventions from Flask. Annoying when you’re trying to maintain consistency.

Amazon CodeWhisperer

CodeWhisperer surprised me. I went in expecting AWS-flavored mediocrity and found a tool that actually excelled at boto3 integrations (obviously) but also held its own with general Python.

Where it really shone: security scanning. It flagged two potential injection vulnerabilities in my scraper code that Copilot missed entirely. That’s worth something.

What about the downsides? Slower suggestion latency, and it struggled more with newer libraries. Its Pandas knowledge felt about 18 months behind.

Verdict: Copilot wins on speed and general accuracy. CodeWhisperer takes security-conscious projects and anything touching AWS infrastructure.

Underdogs Worth Your Attention: Cursor, JetBrains AI, Cody, and Free Options

Now for the Copilot alternatives that don’t get enough attention from Python developers.

Cursor

Cursor became my personal favorite by day 15. It’s not just a code completion tool. It’s a full IDE built around AI assistance. That “chat with your codebase” feature? Genuinely useful. I could ask, “Where am I handling rate limiting?” and get accurate answers.

During the scraper project, Cursor’s understanding of my existing code was noticeably better than Copilot’s. It suggested helper functions that matched my naming conventions, almost like an AI assistant that learns your Python coding style actually exists.

One catch worth mentioning: it’s another IDE to learn. If you’re deeply invested in VS Code or PyCharm, switching has friction.

JetBrains AI Assistant

Already living in PyCharm? This is the obvious choice. Integration is seamless, and it leverages PyCharm’s existing code intelligence beautifully.

My acceptance rate landed at 61%, just behind Copilot. Type hints? Handled beautifully. It caught several issues before runtime too. And the debugging suggestions were actually helpful, not just generic Stack Overflow regurgitation.

Sourcegraph Cody

Cody’s strength is large codebases. My projects were relatively small, so it felt like overkill. But I’ve used it at work for navigating massive monorepos, and it’s excellent at understanding code you didn’t write.

How did it perform in this test? Middle of the pack. Good, not great.

Tabnine and Codeium (Free Options)

Free AI coding assistants for Python programming work, but you feel the difference.

Codeium impressed me most among the free options. Suggestions were relevant about 50% of the time, which is actually solid for something that costs nothing. Students or anyone building side projects should absolutely try it before paying for anything.

Tabnine felt faster but dumber. Good for completing obvious patterns, less helpful for anything requiring real understanding.

Project-by-Project Winners: Scraping, Data Analysis, and APIs

Which AI assistant writes the best Python code for each project type? Let me break it down.

Web Scraping Winner: Cursor

My scraper project involved messy HTML, retry logic, and proxy rotation. Cursor’s codebase awareness meant it understood my existing patterns and suggested consistent solutions. When I was building the parser for site #3, it referenced how I’d handled similar structures in site #1. That kind of memory matters.

Runner-up: Copilot, specifically for its speed on boilerplate-like request headers and basic BeautifulSoup navigation.

Data Analysis Winner: GitHub Copilot

When it comes to Pandas operations, Copilot was unmatched. It nailed groupby operations, merge logic, and even suggested reasonable visualization approaches. Looking for the AI coding assistant with the best Python library support for data science? Still Copilot, hands down.

I’d type df.groupby('user_id'). and it’d complete with exactly the aggregation I needed more than half the time.

Runner-up: JetBrains AI, which handled NumPy array operations better than expected.

API Development Winner: JetBrains AI Assistant (in PyCharm)

Specifically for FastAPI, that PyCharm integration made JetBrains AI the winner. It understood Pydantic models, dependency injection patterns, and async/await contexts with impressive accuracy.

Copilot kept suggesting synchronous patterns even in async functions. Small thing, but annoying when it happens repeatedly.

Runner-up: Cursor, which was close behind and offered better debugging conversations.

What Most Comparisons Miss: Style Learning, Debugging Help, and Library Support

AI tools that help debug Python code aren’t all equal, and the difference matters when you’re stuck at 2 AM, wondering why nothing works.

Style Learning

Only Cursor and Tabnine felt like they actually adapted to my coding style over time. Copilot’s suggestions stayed consistent regardless of my patterns. Do you work on a team with established conventions? Tools that learn will matter more than you’d think.

Debugging Assistance

I intentionally introduced bugs to test this. Real bugs, the kind that throw cryptic tracebacks.

Cursor’s chat feature was best for debugging. I could paste an error, and it’d walk through possible causes in order of likelihood. Copilot’s chat (via the /fix command) was decent but more generic.

CodeWhisperer’s debugging help was limited. JetBrains AI leveraged PyCharm’s debugger integration, which is cheating but effective.

Library Support

Evaluating AI coding tools for Python development comes down to this: do they know the libraries you actually use?

Data science (Pandas, NumPy, scikit-learn): Copilot > JetBrains > Cursor Web frameworks (FastAPI, Django): JetBrains > Copilot > Cody Async patterns (asyncio, aiohttp): Cursor > Copilot > CodeWhisperer

My Recommendations After 30 Days of Testing

After 30 days, multiple broken tests, and way too much caffeine, here’s how to choose the best AI coding assistant for Python:

Best Overall: GitHub Copilot. It’s the safest choice. Good at everything, great at data science work. Worth the $19/month for professionals.

Best for Serious Python Development: JetBrains AI in PyCharm. Already in the JetBrains ecosystem and doing production Python? This is the move. Integration depth is unmatched.

Best for Learning Your Style: Cursor. Want to level up? Cursor’s chat features are incredibly educational. You can ask “Why did you suggest this?” and get useful answers.

Best Free Option: Codeium Solid baseline performance, no cost. Perfect for students and side projects.

Best for Security-Conscious Work: CodeWhisperer Security scanning alone might justify trying it on financial or healthcare projects.

My personal setup now? I run Copilot as my default, switch to Cursor for debugging sessions, and keep Codeium installed for when I’m streaming and don’t want to share my Copilot suggestions with 200 viewers.

These AI code completion tools have gotten genuinely good in 2024. Going back to pure manual coding feels a bit like choosing to type while wearing oven mitts. Pick one, commit to learning its quirks for a week, and adjust from there.

And if you break something seventeen times, figuring it out? Well, at least I already did that part for you.