Prompt cache hit — nullvuild

A prompt cache hit is a request event where a model provider can reuse a previously processed prompt prefix instead of processing the same prefix from scratch.

The practical meaning is simple: repeated instructions, examples, tool definitions, schemas, and long background sections may become cheaper or faster when they stay identical across requests. OpenAI documents automatic prompt caching for recent models and says cache hits depend on exact prefix matches. Anthropic documents cache breakpoints and explains that cache reads are useful when a stable prefix is reused.

A cache hit is not a memory feature. It does not mean the model remembers a user across unrelated sessions. It also does not change the answer by itself. The model still generates the final output for the current request; the cache affects the cost and latency of processing repeated input.

A useful note should record provider, model, prompt length, static prefix, dynamic tail, cached token count if available, latency, and price tier. Without those fields, teams may guess that caching is helping when the request shape is actually changing too often.

The boundary is important. Prompt caching is most useful when the same long prefix appears again. It is weak when every request begins with a timestamp, user-specific text, random examples, or reordered tool definitions. Good prompt design keeps stable material first and changing material later.

Prompt cache hit ai api cost note 2026 06 25 i

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