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Comment by sgarland | Hacker News Reader

I feel like (by which I mean, in my tests for personal projects) performance peaked with 3.11, and has been going downhill since.

For example, 3.12 changed the byte code list comprehensions use, citing faster performance. In my tests, this is only true with small lists, and as soon as you get to a large number of items (~10K), it falls off a cliff. While large lists are probably not the norm, to have that completely ignored is maddening.

The 3.13 REPL is nice, I’ll give them that.

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The new generic syntax is great as well, the debugging improvements are handy and I do appreciate the new @deprecated. But 3.13 really shines, once again, with the improvement of error messages.

Now when you are a professional dev, you see them less and less, because you make less mistakes, but also because you heavily use tooling to prevent errors or diagnose them.

But if you observe beginners with modern versions of Python, it's quite magical really. The autonomy and confidence they get from those are huge.

The energy that Pablo Galindo Salgado injected into the culture of good error message has been a boon to the language.

I updated the benchmark [1] that I wrote to someone who responded to you to include a even/odd filter and take the minimum time of 100 runs. Python 3.13 is much faster for small lists, and a tiny bit faster for larger lists. The times are the time taken for iterating per entry in the list comprehension.

Running on Python 3.12.7 (main, Oct 2 2024, 15:45:55) [Clang 18.1.8 ] 16.69ns on N=100 18.84ns on N=1_000 15.88ns on N=10_000 14.26ns on N=100_000 14.70ns on N=1_000_000

Running on Python 3.13.0 (main, Oct 16 2024, 08:05:40) [Clang 18.1.8 ] 9.54ns on N=100 17.88ns on N=1_000 15.81ns on N=10_000 14.14ns on N=100_000 14.46ns on N=1_000_000

[1] https://gist.github.com/thomasaarholt/2e4d42cbf3f0de60c811bb...

For simple loops, yes, I also found that 3.13 is roughly equivalent to 3.12. Both are much slower than 3.11, though [0] – roughly 6-9% for lists, and in one weird edge case, 57% slower for array.array, but only on MacOS (I only tested ARM, no idea on x86). Linux was about the same as for lists.

[0]: https://github.com/python/cpython/issues/123540

Thank you for sharing! I am convinced! Completely agree that this is a problem!

Do you have some microbenchmarks for this? Would be a chance to use UV to setup Python 3.11 and Python 3.12 envs side by side.

https://github.com/astral-sh/uv

Great minds think alike. Here's one I just cooked up. Python 3.13 is faster, at least for the simplest possible list comprehension with no filters. N is the length of the list comprehension. Repeated 100 times for each test and showing the average. Run on a MacBook Air M3.

https://gist.github.com/thomasaarholt/2e4d42cbf3f0de60c811bb...

  Running on Python 3.12.7 (main, Oct  2 2024, 15:45:55) [Clang 18.1.8 ]
  11.32ns on N=100
  9.42ns on N=1000
  7.32ns on N=10000
  5.23ns on N=100000
  5.87ns on N=1000000
  
  Running on Python 3.13.0 (main, Oct 16 2024, 08:05:40) [Clang 18.1.8 ]
  7.30ns on N=100
  5.91ns on N=1000
  5.09ns on N=10000
  4.31ns on N=100000
  5.89ns on N=1000000

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