What does the GIL mean for Python concurrency now that free-threaded builds exist?

Explains conventional CPython thread execution and the optional free-threaded runtime boundary.

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In a conventional CPython build, the global interpreter lock allows only one thread at a time to execute Python bytecode in a process. Threads can still overlap blocking I/O, and extension code may release the GIL. Since Python 3.13, optional free-threaded builds can disable the GIL and run Python threads in parallel, but that is not the default deployment assumption and package compatibility still matters.

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Explains conventional CPython thread execution and the optional free-threaded runtime boundary.

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Explains conventional CPython thread execution and the optional free-threaded runtime boundary.
In a conventional CPython build, the global interpreter lock allows only one thread at a time to execute Python bytecode in a process. Threads can still overlap blocking I/O, and extension code may release the GIL. Since Python 3.13, optional free-threaded builds can disable the GIL and run Python threads in parallel, but that is not the default deployment assumption and package compatibility still matters.
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The GIL does not make compound application operations thread-safe. Context switches, I/O, and extension behavior can interleave shared-state access, and free-threaded builds make accidental assumptions even more dangerous. Code using threads still needs explicit ownership or synchronization. For CPU work I verify the interpreter build and whether the library releases the GIL before predicting speedup.

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Saying the GIL prevents all races
Assuming every Python install is free-threaded
Expecting threads to speed every CPU workload

What does the GIL mean for Python concurrency now that free-threaded builds exist?

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