Chaotic is different from stochastic. Stochastic means there are randomness involved in the evolution of the state. 2-body and 3-body problems are deterministic, not stochastic. The state always evolve the same given the exact same initial condition.

But for 3-body problem, it's chaotic. If you don't have exact values for the initial condition, the error became exponentially large as time go by, so after a certain amount of time the state became essentially unpredictable (but there are special exceptions). If you do have exact values for initial condition, then you can make arbitrarily accurate prediction for arbitrary long period of time, but you will need to perform a lot more calculations compare to non-chaotic case to control this exponentially growing error (you always acquire error due to numerical imprecision). Chaotic implies a few properties. One is the butterfly effect, as I mentioned above. Another one is mixing: it's not merely that you can't predict precisely if time is long enough and you don't have the exact initial condition, you can't even make a vague estimate that carry any useful information at all.

Why is 2-body problem not chaotic? Essentially, it has too few variables compared to the amount of symmetry. It's known that if you have at most 2 free variables you can't be chaotic. A 2-body problem has 12 variables (position and velocity for each body), but standard physics 10 symmetries gives you 10 constant of motions (center of mass, linear momentum, angular momentum, energy) so the problem is reduced to 2 dimensions. Actually, there is a special 11th constant of motion specific to this problem: the LRL vector, but only its direction on the plane of motion matter, because the plane of motion and length is determined by angular momentum and energy, so the problem is reduced to just 1 dimension. 1 dimensional system is very easily solvable explicitly.

For 3-body problems, you start with 18 variables, but you only have the usual 10 symmetry. It's proven that there are no other algebraic constant of motions, so at most you can reduce this to an 8 dimensional problem using this technique. Actually showing that this problem is chaotic (and hence you really can't reduce further) is harder.