There is no meaning of this kind of comparison. It is evident that the localization will be more accurate than SLAM. Let's see why. SLAM is usually constructed by using probabilistic methods as follows
$$
p( x_{t}, m | z,u)
$$
One seeks to solve the aforementioned conditional probability where $x$ is the state vector, $m$ is the map, $z$ is the observations and $u$ is the control input. For localization, also by using probabilistic methods is as follows
$$
p( x_{t} | z,u, m)
$$
You will notice the $m$ in localization is provided which means the true location of the map is known with absolute certainty. This is not the case in SLAM in which we need to estimate the map $m$ which means the absolute certainty is not provided (i.e. impossible to access to the true location of the map). In SLAM, more work need to be done to filter this noise. As you can see, loosely speaking, localization is a simplified version of the SLAM where we assume the map is given. This is why the authors of "Probabilistic Robotics" started the discussion with localization before SLAM. That being said, SLAM is more realistic than localization.