What are you basing this all on?
$ time (cat optimizer.bin | bzip2 > optimizer.bin.bz2)
real 0m4.352s
user 0m4.244s
sys 0m0.135s
$ time (cat optimizer.bin | zstd -19 > optimizer.bin.zst)
real 0m12.786s
user 0m28.457s
sys 0m0.237s
$ ls -lh optimizer.bin*
-rw-r--r-- 1 billy users 76M Oct 20 17:54 optimizer.bin
-rw-r--r-- 1 billy users 56M Oct 20 17:55 optimizer.bin.bz2
-rw-r--r-- 1 billy users 59M Oct 20 17:56 optimizer.bin.zst
$ time (cat stocks-part-2022-08.tar | bzip2 > stocks-part-2022-08.tar.bz2)
real 0m3.845s
user 0m3.788s
sys 0m0.103s
$ time (cat stocks-part-2022-08.tar | zstd -19 > stocks-part-2022-08.zst)
real 0m34.917s
user 1m12.811s
sys 0m0.211s
$ ls -lh stocks-part-2022-08.*
-rw-r--r-- 1 billy users 73M Oct 20 17:57 stocks-part-2022-08.tar
-rw-r--r-- 1 billy users 26M Oct 20 17:58 stocks-part-2022-08.tar.bz2
-rw-r--r-- 1 billy users 27M Oct 20 17:59 stocks-part-2022-08.zst
Are you looking at https://jdlm.info/articles/2017/05/01/compression-pareto-docker-gnuplot.html or something? I would expect Lempel-Ziv to perform phenomenally on genomic data because of how many widely separated repeated sequences the data will have... for that specific domain I could see zstd being a clear winner (super fast obviously and also happens to have the best compression, although check the not-starting-at-0 Y axis to put that in context).
I have literally never heard of someone claiming zstd was the best overall general purpose compression. Where are you getting this?