Comparison
Context Window vs Prompt Caching
Context Window and Prompt Caching 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 Prompt Caching
Whenever a prefix is reused across calls and exceeds ~1K tokens. The break-even point is low; the upside is large.
A long-context RAG app caches the system prompt + few-shot examples; per-call latency drops from 6s to 1.5s, cost drops ~80%.
Frequently asked
What is the difference between Context Window and Prompt Caching?
Context Window: The context window is the maximum number of tokens an LLM can consider in a single call — prompt plus generated output combined. Prompt Caching: Prompt caching stores the KV-cache state of a long prefix (system prompt, large document, tool definitions) so subsequent calls that reuse it skip the prefill compute — cutting TTFT and cost by 50-90%.
When should I use Context Window vs Prompt Caching?
Context Window is the right concept when you are focused on inference. Whenever a prefix is reused across calls and exceeds ~1K tokens. The break-even point is low; the upside is large.
Are Context Window and Prompt Caching the same thing?
No. Context Window is inference; Prompt Caching is inference. They are related but address different parts of the AI stack.