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I'm trying to build a 5 input-5 output model using LSTM, where all the outputs are the same features as the inputs, predicted in the future.

My question is: is it better to build 5 models, each with the same 5 inputs, but predicting just 1 of the 5 sequences at a time, or is it the same as building 1 model predicting all 5 sequences? In other words, is the accuracy per predicted sequence going to be higher with 5 separate models or will it be the same as 1 model with 5 outputs.

The reason for my confusion is that, in the case of the multiple output model, the hidden layer will be the same; so how would the algorithm go about optimizing the weights so as to minimize error for all output sequences?