I have similar timings (for a draft version of PR, see f.patch) as for the last comment, though the small-dens overhead seems to be bigger(~20%):
$ python3.10 -m timeit -r11 -s 'from fractions import Fraction as F' -s 'a=F(10,3)' -s 'b=F(6, 5)' 'a * b'
50000 loops, best of 11: 9.09 usec per loop
$ python3.10 -m timeit -r11 -s 'from patched import Fraction as F' -s 'a=F(10,3)' -s 'b=F(6, 5)' 'a * b'
20000 loops, best of 11: 11.2 usec per loop

On another hand, here are timings for bigger denominators:
$ python3.10 -m timeit -r11 -s 'from fractions import Fraction as F' -s 'import random' -s 'n = [random.randint(1, 1000000) for _ in range(1000)]' -s 'd = [random.randint(1, 1000000) for _ in range(1000)]' -s 'a=list(map(lambda x: F(*x), zip(n, d)))' 'sum(a)'
1 loop, best of 11: 257 msec per loop
$ ... from patched ...
10 loops, best of 11: 33.2 msec per loop

It's not so clear what "are very large" does mean, that could be defined here.  BTW, 10**6 denominators are (very!) small for mentioned above use case (CAS package).