Both process sequences, but LSTM does it one token at a time with hidden-state memory; Transformer processes the entire sequence in parallel with self-attention.

Comparison

Why Transformer wins

Two compounding reasons:

1. Parallel training on thousands of GPUs.
2. Attention is O(1) per query token to reach any earlier token (vs O(N) walks for LSTM).

When LSTM still wins

• Truly tiny / on-device models.
• Streaming under strict memory budgets (Transformers still need the full KV cache).

Analogy

LSTM is reading a book aloud, remembering only what came earlier; Transformer spreads the entire book on a table and points at relevant pages.

Interview tip: For scale and long context, Transformer wins. LSTMs haven’t disappeared; they still drive edge inference.