CPython vs. PyPy: Which Python runtime has the better JIT?

"PyPy, an alternative runtime for Python, uses a specially created JIT compiler to yield potentially massive speedups over CPython, the conventional Python runtime. But PyPy's exemplary performance has often come at the cost of compatibility with the rest of the Python ecosystem, particularly C extensions. And while those issues are improving, the PyPy runtime itself often lags in keeping up to date with the latest Python releases."

"On a Ryzen 5 3600 with six cores, Python 3.14 takes about 9 seconds to run this benchmark. But PyPy chews through it in around 0.2 seconds. This also isn't the kind of workload that benefits from Python's JIT, at least not yet. With the JIT enabled in 3.14, the time drops only slightly, to around 8 seconds. But what happens if we use a multi-threaded version of the same code,"

PyPy delivers massive speedups on arithmetic-heavy benchmarks, running the cited loop in about 0.2 seconds versus CPython 3.14 at roughly 9 seconds on a Ryzen 5 3600. CPython's native JIT in 3.14 reduces that particular workload only slightly to about 8 seconds. PyPy's high performance often comes with trade-offs in C-extension compatibility and slower adoption of the newest Python releases. CPython's new native JIT and an alternative no-GIL build provide meaningful improvements in some workloads and enable full multithreading, narrowing the gap depending on workload characteristics.

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