Why seq2seq models are superior to simple LSTMs?
Encoder-decoder architectures are not simply "more powerful" than a mere LSTM. 
LSTMs can't be used (in their standard configuration) for general sequence transduction tasks. On the other hand, encoder-decoder architectures are conditional autoregressive models, that is, they generate a sequence element by element conditioning on another sequence. This difference is what justifies the rather different use cases for simple LSTMs and encoder-decoder architectures.

Among LSTM-based encoder-decoder architectures, we should distinguish different types by the way information is passed from the encoder to the decoder. The simplest form is to simply pass the last hidden state of the encoder LSTM to the first decoder LSTM; this implies that all the information from the input sequence is "compressed" into a fixed-length vector, which is known to be an information bottleneck. More complex forms include using attention mechanisms, where the encoder hidden states at each time step are combined into a different previous context vector for each decoder LSTM; here there is no bottleneck, and normally their results are much better.