The crlibm code is terrible to read and hence maintain. I had considered including crlibm too during my work but decided against it because it would be a lot more work.

Also, crlibm functions are provably correctly rounded only for univariate transcendentals (thanks to the worst case paper I cited in the blog). There is no such proof available for bivariate functions, which is why computing at an arbitrary very large precision (the way glibc does) is the only way to increase the likelihood of correctly rounded results. IIRC crlibm does not even try for bivariates.

I believe some documentation on the crlibm website cites benchmark numbers where IIRC glibc performs better (although not by much) in the fast paths of most functions they mention but gets hit really badly in the slow path. Slow path inputs are usually quite rare, but if your application/benchmark hits them regularly enough (especially sice the slow path is a factor of 1000 times worse) then you'll obviously conclude that the performance sucks and that crlibm is better.