That is at least partly the case. I maintain msgspec[1], another Python JSON validation library. Pydantic V1 was ~100x slower at encoding/decoding/validating JSON than msgspec, which was more a testament to Pydantic's performance issues than msgspec's speed. Pydantic V2 is definitely faster than V1, but it's still ~10x slower than msgspec, and up to 2x slower than other pure-python implementations like mashumaro.
Recent benchmark here: https://gist.github.com/jcrist/d62f450594164d284fbea957fd48b...
Eeh come on, I think it's a bit unfair to compare, because msgspec doesn't support regular python union types… which are the number 1 source of slowness… at least in my real world use case of the thing. I've got hundreds of classes with abundant nesting and unions.
In pydantic v2 they did the same thing i've been doing in typedload for a few versions already: check the field annotated with a Literal and directly pick the correct type, rather than do try and error. So now the speed for unions has become better.
Even so, for being binary vs pure python, I'd have expected much more.
I re-do the benchmarks of typedload when I make a release. The benchmarks will be updated when the next release happens.
I will not do a new release because you need new benchmarks after 3 days. You are free to include benchmarks on your own website (but we both know you won't do that).
This is because of how my whole setup works, requiring a git tag and a finished CHANGELOG. Running the command to regenerate the website would cause documentation from the master branch to be published.
The benchmarks will be here, as usual. https://ltworf.github.io/typedload/performance.html
I run them just getting the latest available version. But since I can't time travel, I can't get versions from the future to appease you, sorry.
I just ran them locally (like you could do by yourself) https://news.ycombinator.com/item?id=36644818
I maintain typedload (a similar project, that I started before pydantic's first release) and pydantic 2 somehow still manages to be slower than a pure python library that got no funding to improve performances.