Comparison
Multi-Head Attention vs Transformer
Multi-Head Attention and Transformer are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Multi-Head Attention
Multi-Head Attention comes up when the question is fundamentally about architecture.
GPT-4 reportedly uses ~96 heads per attention layer.
When you would reach for Transformer
Default choice for any sequence task in 2026: text, code, audio, even protein sequences.
GPT-4: decoder-only transformer.
Frequently asked
What is the difference between Multi-Head Attention and Transformer?
Multi-Head Attention: Multi-head attention runs several attention operations in parallel, each with its own learned projection, then concatenates the results. This lets the model attend to different kinds of relationships at once. Transformer: The transformer is the neural network architecture behind virtually every modern large language model. It uses self-attention to model relationships between all positions in a sequence in parallel.
When should I use Multi-Head Attention vs Transformer?
Multi-Head Attention is the right concept when you are focused on architecture. Default choice for any sequence task in 2026: text, code, audio, even protein sequences.
Are Multi-Head Attention and Transformer the same thing?
No. Multi-Head Attention is architecture; Transformer is architecture. They are related but address different parts of the AI stack.