# What is the advantage of using RNN with fixed timestep length over Neural Network?

More often than not, I see RNNs being used with fixed length timesteps. So what is the difference between the following two networks?

1. RNN with timestep length of 3 over sequence Xt.
2. NN with inputs x(t-2), x(t-1), x(t)

They both get 3 timesteps of sequence at each turn. Thus, (I know I am wrong but) these two networks have the same capability. They both use previous 3 samples to predict the next sample. What is the difference, then?