Encoder blocks primarily affect language understanding and contextual representation. Increasing encoder depth, attention heads, or FFN size generally improves semantic understanding but increases latency and cost. Decoder blocks primarily affect text generation quality and reasoning. Increasing decoder depth improves generation capabilities, while removing cross-attention converts a sequence-to-sequence model into a decoder-only model like GPT. The overall impact is a trade-off among model quality, latency, memory consumption, and training cost.