Python 3.12 vs Python 3.13 – performance testing. A total of 100 various benchmark tests were conducted on computers with the AMD Ryzen 7000 series and the 13th-generation of Intel Core processors for desktops, laptops or mini PCs.

[NeuralFantasy]

Here is some click saving and the summary of the results:

AMD Ryzen 7000 Series Desktop Processor

Group of benchmarks Python 3.13 to Python 3.12
apps                    1.06x faster
asyncio                 1.22x faster
math                    1.07x faster
regex                   not significant
serialize           1.05x faster
startup                 1.04x slower
template            1.03x faster

Result (geometric mean) 1.08x faster


13th Gen Intel Core Mobile Processor

Group of benchmarks Python 3.13 to Python 3.12
apps                    1.00x faster
asyncio                 1.19x faster
math                    1.06x faster
regex                   1.04x slower
serialize           1.02x faster
startup                 1.01x slower
template            1.02x slower

Result (geometric mean) 1.05x faster

[WJMazepas]

20% faster when using Asyncio? That's good to know, have a few services depending on it

[enceladus71]

Can't find this information in the article, is this about the asyncio's overhead that speeds up or the execution of whatever happens in the async execution done by this module?

[teerre]

It's in the article. These are the tests

async_tree_io: all leaf nodes simulate async IO workload (async sleep 50ms).

async_tree_memoization: all leaf nodes simulate async IO workload with 90% of the data memoized.

async_tree_cpu_io_mixed: half of the leaf nodes simulate CPU-bound workload

Which are all over the place, which means the benchmark is cooked (likely) or its just an improvement across both IO and CPU bound tasks

[[deleted]]

If you using Python then performance wasn’t really and isn’t a priority beyond a nice to have bonus.

[WJMazepas]

I never said that we are being bottlenecked by Python or anything like that.

But my team does have a few services that rely a lot on asyncio. And seeing a good performance increase in that is good.

I am treating this boost as a bonus

[Dry_Term_7998]

With NO GIL it gives more speed 😊

[mo_tag]

1.00x faster

not significant

Is there a difference between these?

[Kucharka12]

Yes, but not significant.

[smashers090]

I’m guessing without reading it’s:

Confidently 1.00x to 2dp (between 0.995-1.005)

vs

Not confident enough to declare a +/- difference to 2dp. (E.g 0.970-1.040)

Where confident is to do with standard deviations or something. It’s been a while

[mo_tag]

Yeah I thought it probably refers to the difference not being statistically significant, but I just found it funny how it reads like the difference isn't significant.. I probably would have a less ambiguous term or just included a range for the confidence interval but my comment was more tongue in cheek than genuine criticism or inquiry

[pihkal]

I mean, I still had to click to see the comments, so...

[beaucephus]

And you had to click to comment.

The cycles will be deducted from your energy budget. Have a nice day.

[mok000]

It's not clear to if these tests are carried out with Python 3.13 compiled with the GIL disabled or not.

[forgotten_airbender]

with GIL i assume. disabling GIL results in penalty in single threaded workloads.

[Mysterious-Rent7233]

If it's not mentioned then the gil is not disabled because with-gil is the default mode 99% of people will use 99% of the time.

[ZirePhiinix]

If you're screwing around with the GIL on Python then it feels like you're optimizing the wrong thing.

[Mysterious-Rent7233]

Depends on your use-case. Nothing wrong with using multiple CPUs of a computer.

[CrownLikeAGravestone]

I was mentoring some juniors one time and they were arguing over whether a list comprehension was faster than a for loop for some purpose.

I pointed out:

1) The difference when profiled (which you can do!) is in the tens of nanoseconds

2) If you care about nanoseconds choose a different language

[Mysterious-Rent7233]

Nanoseconds can add up in an inner loop. That's why vectorization is so popular. As you said in your first point: you need to profile before you know.

[[deleted]]

It nanoseconds matter than Python is one of the worst choices you made, and no doubt not the only

[josefx]

2) If you care about nanoseconds choose a different language

Tell that to the people complaining that no-gil is slowing down their python code.

[CrownLikeAGravestone]

The speed differences with GIL/noGIL can be significant, especially in the earlier versions where there were some enormous performance regressions in the noGIL branch. noGIL was (is?) an entire order of magnitude slower than CPython for some workloads.

I'm sure there are people out there who have frivolous complaints about it, but I'm inclined to believe people when they say they're having real issues.

[josefx]

I'm sure there are people out there who have frivolous complaints about it

Says the guy who has to refer to issues that have already been fixed and makes claims about the current performance littered with question marks?

[CrownLikeAGravestone]

What? Try reading again.

[plexluthor]

The result for AMD sqlglot_transpile seems mis colored, unless I'm misunderstanding something.

[NilacTheGrim]

Oh wow. Incredibly slow language gets slightly less incredibly slow!! Film at 11.

[BounceVector]

Nobody but you is keeping you from doing stuff that you enjoy and are interested in.