Python 3.13 - You Can Finally Disable the GIL (But Probably Shouldn't)

Python 3.13 shipped October 7, 2024, and honestly, we waited 20 years for GIL removal just to find out it makes our code slower. The core team finally gave us what we asked for, but like most monkey's paw situations, there's a catch - your regular Python code runs worse without the GIL because atomic reference counting is expensive.

Free-Threading Mode: We Got What We Asked For (And Regretted It)

The free-threaded mode disables the GIL but makes everything slower because now every object access needs atomic reference counting. I tested this on our Django app and requests were noticeably slower - maybe 30-50% worse, didn't time it precisely. Turns out the GIL wasn't the performance bottleneck we thought it was.

You only get benefits when running actual parallel CPU work. Your typical web app hitting a database? Slower. Your REST API returning JSON? Also slower. Only scientific computing with tight loops across multiple cores sees improvements, and even then, you're better off using multiprocessing.

C extensions crash constantly with free-threading. NumPy? Broken. Pandas? Broken. Pillow? You guessed it - broken. I spent some ungodly amount of time last week debugging segfaults just to realize I had free-threading enabled. Check the compatibility tracker but honestly, assume everything is broken until proven otherwise.

Don't use this in production. The Python core team is still fixing the C API and most package maintainers are overwhelmed trying to make their stuff thread-safe.

JIT Compiler: Only Helps If You're Doing Math (You're Probably Not)

The JIT compiler speeds up tight math loops and basically nothing else. Enable it with PYTHON_JIT=1 or python -X jit if you want to watch your startup time get worse.

I ran this on different workloads:

• NumPy matrix multiplication: way faster when it works (maybe 20-40%)
• Django view returning user data: slower due to JIT overhead
• FastAPI processing JSON: basically the same, maybe worse
• File operations: JIT doesn't help I/O at all

The copy-and-patch approach only works when your code runs the same hot path repeatedly. Most web apps jump around between different request handlers, so the JIT never gets warm. PyPy already does JIT compilation better, but we all ignore PyPy because package compatibility is a nightmare.

Mobile Support: Python on Mobile Is Still a Bad Idea

Python 3.13 supports iOS and Android as Tier 3 platforms, which basically means "good luck, you're on your own." You can build mobile apps with BeeWare or Kivy, but you probably shouldn't.

Tier 3 support means they barely test it and when shit breaks, you get to figure it out yourself:

• Debugging? Hope you like print statements because that's all you get
• App Store approval? Apple hates interpreted languages, prepare for rejection
• Performance? Your app will be slower than molasses and drain the battery
• Package compatibility? Most Python packages assume desktop Linux

I tried building an internal tool with BeeWare last year and spent more time fighting the toolchain than writing code. Just use React Native or Flutter - they actually work.

Free-Threading: When Everything Goes to Hell

Free-threading sounds amazing until you actually try to use it with real code. Here's what happens when you enable it:

C Extension Crashes: Every C extension you care about crashes. NumPy? Segfault. SciPy? Segfault. Pandas? You get three guesses. Pillow dies instantly with SIGSEGV at address 0x0. I spent way too long debugging an image processing script only to realize free-threading was enabled.

Atomic Operations Everywhere: Every time you access an object, Python does atomic reference counting instead of simple increments. This makes single-threaded code way slower because atomic operations are expensive. The CPython team had to rewrite half the interpreter for this.

Lock Contention Nightmare: Instead of one big lock, you get thousands of tiny locks. Debugging race conditions becomes impossible because the locking patterns are completely different. Stack traces become useless when everything is happening across multiple threads.

The garbage collector is now more complex to handle concurrent access. Memory usage goes up, performance becomes unpredictable, and debugging GC issues is a special kind of hell.

JIT Compiler: Good for Math, Useless for Everything Else

The copy-and-patch JIT only helps very specific code patterns. I benchmarked it extensively and here's what actually happens:

What Gets Faster (Sometimes):

• Tight mathematical loops that run the same code 1000+ times
• NumPy operations (when the stars align and nothing crashes)
• Scientific computing with predictable computation patterns
• Basically only code that looks like C but written in Python

What Gets Slower (Most Things):

• Django views because database I/O dominates anyway
• REST APIs processing JSON because the JIT overhead isn't worth it
• Any code that calls into C libraries frequently
• Web applications that jump between different request handlers

The JIT needs to watch your code run hundreds of times before it figures out the hot paths, then compile optimized versions. Most web apps never run the same code path enough times for this to matter. It's solving a problem that only scientific computing has.

Mobile Development: Still a Mess

iOS and Android support exists but the experience is so bad you'll question your life choices:

Build Hell: Cross-compiling Python for mobile uses custom toolchains that break constantly with cryptic error messages. I spent way too long getting a "Hello World" app to compile because of Xcode version conflicts. The build process is fragile and documentation is outdated.

Debugging? What Debugging?: No debugger integration with Xcode or Android Studio. You're back to print statement debugging like it's 1995. Memory profiling doesn't work with embedded interpreters, so good luck finding memory leaks. Stack traces are useless when your app crashes - you get <main>:1 in <module> and that's it.

App Store Rejection Roulette: Apple hates interpreted languages and will reject your app for "executing downloaded code" even when you're not. Static bundling is required but increases app size by 50MB minimum. Google Play has similar restrictions that aren't well documented.

Performance That Makes Users Cry: Python on mobile is slow as molasses. UI animations stutter, battery drain is insane, and users will uninstall your app thinking it's broken. Native apps start in 0.5 seconds, Python apps take 3-4 seconds just to show the first screen.

Migration Strategy: How Not to Break Production

Upgrading from Python 3.12? Here's how to do it without getting paged at 3am:

Test Your Dependencies First: Python 3.13 removed deprecated APIs that a bunch of your dependencies probably still use. I've seen imp.find_module(), unittest.TestCase.assertEquals(), and other "stable" APIs disappear. Run your tests before changing anything in production. Better yet, test in a staging environment that actually matches production.

Stay Away from Experimental Features: Don't enable free-threading or JIT in production. I don't care how cool they sound, they're not ready. Free-threading will crash your app with segfaults and JIT will make most workloads slower. Stick to boring Python 3.13 with the GIL enabled.

Check Every Package with C Code: Go through your requirements.txt and check every package that has compiled extensions. Most don't support free-threading yet and some break in subtle ways even with regular Python 3.13. The compatibility tracker helps but isn't complete.

Benchmark Your Actual Workload: Don't trust generic benchmarks. Python 3.13 can be slower than 3.12 for some applications due to internal changes. Test with realistic data and traffic patterns, not toy examples.

My advice? Upgrade to stable Python 3.13, run your tests for a week, then wait 6 months before even thinking about experimental features. The ecosystem needs time to catch up, and you don't want to be debugging segfaults in production.