Not necessarily the most representative benchmark, but:

    >>> from sympy import *
    >>> from time import clock
    >>> var('x y z t')
    (x, y, z, t)
    >>> f = poly(1 + x + y + z + t)
    >>> p = f ** 20
    >>> t1=clock(); q=p*(p+1); clock()-t1
    53.828634

    julia> using Nemo
    julia> Rx,x=PolynomialRing(ZZ,"x");
    julia> Ry,y=PolynomialRing(Rx,"y");
    julia> Rz,z=PolynomialRing(Ry,"z");
    julia> Rt,t=PolynomialRing(Rz,"t");
    julia> p = (1+x+y+z+t)^20;
    julia> @time p*(p+1);
    1.593379 seconds (129.24 k allocations: 6.758 MB, 0.82% gc time)

Edit: oops, I forgot a variable which made the benchmark totally unfair!

> Since Julia is about speed

Is it ? At least that's not the part that excites me most, although its a nice by product to have. Its a nice flexible language with good syntax and a good set of features. I still dislike 1 based indexing though.

They have a benchmarks page where it compares quite favorably to SageMath. I don't know but I'd kinda assume that Sage uses Sympy under the hood.