Julia is reasonably fast. I returned to a language comparison project specific to my math research, to see how I might do better. My agents and I studied the advice in the post and various more recent links from the comments, but we were already mostly on target and nothing left moved the needle.

My work is more combinatorial. Julia does excel at numerical computation. There's a tribal divide in math between people who can't go 30 seconds away from the real or complex numbers, and those whose tolerance is about that long. I try to keep an open mind, but I'm closer to the second camp. Julia is good enough to consider either way.

A development in recent months, AI can assist in general purpose Lean 4 programming, no longer getting confused by the dominant proof-oriented training corpus. If one is a functional programmer who believes that Haskell was on the right track, then Lean is the most interesting language choice for shaping one's thoughts. Benchmarks are inherently misleading if a better language makes it possible to express algorithms out of reach of more primitive languages.

https://github.com/Syzygies/Compare

            C++  100    13.08s  ±0.08s
           Rust   99    13.16s  ±0.02s
          Julia   90    14.54s  ±0.01s
             F#   90    14.54s  ±0.04s
  Kotlin-native   88    14.79s  ±0.01s
         Kotlin   86    15.18s  ±0.01s
          Scala   79    16.50s  ±0.08s
   Scala-native   76    17.14s  ±0.02s
            Nim   65    20.17s  ±0.01s
          Swift   64    20.54s  ±0.04s
          Ocaml   52    25.38s  ±0.04s
           Chez   49    26.64s  ±0.02s
        Haskell   37    34.96s  ±0.06s
           Lean   29    45.39s  ±0.15s