Big O is not the only notation used to describe algorithm complexity. There are theta and omega as well. In each (O, theta, omega), there are big and little notations.

As I read from online sources:

f(x) = O(g(x)) (big-oh) means that the growth rate of f(x) is asymptotically less than or equal to to the growth rate of g(x).

f(x) = Ω(g(x)) (big-omega) means that the growth rate of f(x) is asymptotically greater than or equal to the growth rate of g(x)

f(x) = o(g(x)) (small-oh) means that the growth rate of f(x) is asymptotically less than the growth rate of g(x).

f(x) = ω(g(x)) (small-omega) means that the growth rate of f(x) is asymptotically greater than the growth rate of g(x)

f(x) = Θ(g(x)) (theta) means that the growth rate of f(x) is asymptotically equal to the growth rate of g(x)