The Global Interpreter Lock is not simply gone from every Python installation. CPython supports free-threaded builds in which the GIL is disabled, and Python 3.14 officially supports free-threaded Python. The default build and the free-threaded build remain different choices.
Free threading can let Python threads use multiple CPU cores for workloads designed for parallel execution. But it does not automatically make every program faster. The official documentation notes additional overhead, increased memory use, thread-safety concerns, and the possibility that an extension module will re-enable the GIL when it is not ready for free threading.
Start with measurement. Identify a CPU-bound workload with enough parallel work, test it on the default build, then test a free-threaded build. Check every native dependency, especially numerical, database, and imaging packages. Run race-sensitive tests and look for code that relied accidentally on the old GIL’s behavior.
Use explicit locks and thread-safe design rather than relying on internal behavior of built-in containers. For I/O-bound work, asynchronous programming or ordinary threads may already be enough. For CPU-heavy work, multiprocessing, native extensions, or specialized libraries may remain better choices.
The GIL update expands Python’s options; it does not remove engineering judgment. Treat free threading as a workload-specific optimization and verify the complete dependency stack.