Encoder-Decoder Architecture — AI Glossary

Reference details

Related terms

• Decoder-Only Architecture
• Encoder-Only Architecture
• Cross-Attention

Example: Bidirectional reading is the thing you give up

In an encoder-decoder, the input is read by a bidirectional encoder: every input token attends to every other, before any output exists. Translating a German sentence whose verb lands at the end, the encoder has that verb available while representing the first word. The decoder then generates while cross-attending to those finished representations. A decoder-only model reads the same input left-to-right under a causal mask, so the representation of the first word never sees the last. In practice scale has covered the gap for general generation — but the architectural difference is real, and it is why the encoder-decoder shape persists where input and output are distinct sequences.

Commonly confused with

Encoder-decoder is not the same as an encoder-only model. Encoder-only produces representations and cannot generate — it is what an embedding model or a classifier is built from. Encoder-decoder generates conditioned on a fully-read input; decoder-only generates conditioned on everything to its left, prompt and output alike.

When to use it

Reach for it when:

• Fixed transformations with a clear input and output sequence — translation, structured rewriting
• Where bidirectional input understanding matters more than open-ended generation
• Smaller task-specific models, where the shape is efficient for the job

Reach for something else when:

• General chat and agentic work, where the decoder-only ecosystem is far better served
• Anything needing in-context learning at scale — that is where decoder-only models concentrate
• Assuming cross-attention makes it immune to long-input degradation; it does not

Continue your research

Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to Encoder-Decoder Architecture — AI Glossary.

• Browse models
• Review providers
• Inspect benchmarks
• Explore workflows
• Learn AI concepts
• Read decision guides
• Review trust and methodology
• Start a decision path