"sys._jit.is_available(): Lets you know if the current build of Python has the JIT. Most binary builds of Python shipped will now have the JIT available, except the "free-threaded" or "no-GIL" builds of Python. sys._jit.is_enabled(): Lets you know if the JIT is currently enabled. It does not tell you if running code is currently being JITted, however."
"sys._jit.is_active(): Lets you know if the topmost Python stack frame is currently executing JITted code. However, this is not a reliable way to tell if your program is using the JIT, because you may end up executing this check in a "cold" (non-JITted) path. It's best to stick to performance measurements to see if the JIT is having any effect. For the most part, you will want to use sys._jit.is_enabled() to determine if the JIT is available and running, as it gives you the most useful information. Python code enhanced by the JIT Because the JIT is in its early stages, its behavior is still somewhat opaque. There's no end-user instrumentation for it yet, so there's no way to gather statistics about how the JIT handles a given piece of code. The only real way to assess the JIT's performance is to benchmark your code with and without the JIT."
Python 3.13 and above include a sys._jit namespace exposing three boolean utilities for inspecting JIT state. sys._jit.is_available() reports whether the current build includes the JIT; most binary builds include it except the "free-threaded" or "no-GIL" builds. sys._jit.is_enabled() reports whether the JIT is currently enabled. sys._jit.is_active() indicates whether the topmost Python frame is executing JITted code but can be misleading because checks can run on cold (non-JITted) paths. Rely on sys._jit.is_enabled() for availability and use benchmarking to measure JIT performance due to limited instrumentation.
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