[D]Encoder only vs encoder-decoder vs decoder only

I understand that encoder only models (like BERT etc) are mainly for learning representations of words taking context on both sides. What I’m confused about is why you would need decoder only vs encoder-decoder models. GPT and Bloom are decoder only while I think T5 is enc-dec, I’m not sure why you would use one vs the other. Intuitively enc-dec model has more parameters and should be better at tasks where you have both complex text input and output, like …

bert bloom context decoder encoder encoder-decoder etc gpt machinelearning think words