Using Haversine distance increases it to only 13ms. I'm not sure why your Python implementation is so slow? In the real world, things like haversine are implemented as C extensions, so I wrote one for Ruby as a comparison and it runs in 3.8ms for 16k points. https://gist.github.com/jamatthews/d910a2b39c87a871264dd31d1...
The Ruby GIS libaries already have C implementations for haversine but I just wanted to show how easy it is to hook out to a C function for math heavy stuff.
It was slow because at first I put the import inside the function. Moving it out improved the python performance to 20ms (as I mentioned in another comment).
The javascript version runs in 15ms.
> In the real world, things like haversine are implemented as C extensions
Having to write C-extensions is, well a point of friction, and adds complexity.
If you just used a native language to begin with, the simple straight forward code would work just fine.
C extensions are a core part of Ruby. A big chunk of the std lib is implemented in C. You shouldn't be afraid of writing a tiny bit of C. Ruby and Python are scripting languages. They're literally designed for scripting another language.
The JIT for Ruby 3 is well underway, so in a bit it'll be a complete non-issue anyway.