Comparison
Context Window vs Sliding-Window Attention
Context Window and Sliding-Window Attention are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Context Window
Context Window comes up when the question is fundamentally about inference.
GPT-4o: 128K context.
When you would reach for Sliding-Window Attention
Sliding-Window Attention comes up when the question is fundamentally about architecture.
Mistral 7B: sliding window of 4096 over a 32K-trained context.
Frequently asked
What is the difference between Context Window and Sliding-Window Attention?
Context Window: The context window is the maximum number of tokens an LLM can consider in a single call — prompt plus generated output combined. Sliding-Window Attention: Sliding-window attention limits each token to attending only the most recent W tokens (e.g. 4K), making attention linear in sequence length. Mistral and Gemma use it.
When should I use Context Window vs Sliding-Window Attention?
Context Window is the right concept when you are focused on inference. Sliding-Window Attention applies when you are focused on architecture.
Are Context Window and Sliding-Window Attention the same thing?
No. Context Window is inference; Sliding-Window Attention is architecture. They are related but address different parts of the AI stack.