So only 1 in 5 production environments is on 3.13. Why? The free-threaded build requires recompiling C extensions, and many CI/CD pipelines haven't updated their base Docker images.
It works. The nogil fork is officially dead. For CPU-bound threaded workloads (think web scraping, data processing, or pandas aggregations), teams are reporting 2x to 4x speedups without rewriting a single line of multiprocessing code. python 3.13 news today november 2025
You still need pip install numpy --config-settings=--disable-gil . Many C extensions took over a year to become thread-safe. As of today, the PyData stack (NumPy, Pandas, Scikit-learn) is 90% compatible with free-threading, but niche libraries like lxml and cryptography still require the GIL. So only 1 in 5 production environments is on 3
Use free-threading for new backend services. Wait for legacy data science environments. 2. The JIT Compiler: Warm-up is Over Python 3.13 introduced a copy-and-patch JIT (courtesy of brandtbucher). Back in October 2024, it gave a ~5-10% speed boost. One year later, with JIT optimizations tuned in 3.13.2 and 3.13.4? It works
— Your friendly local Pythonista