How do Anthropic and OpenAI prompt-cache pricing models work and what counts as a cache hit?

**Prompt caching** lets a provider remember the work it did on the front of your prompt so it does not redo it next time. Internally this is the KV cache from the prefill phase, kept warm and reused. Anthropic charges cached input tokens at roughly 10 percent of the normal input rate. There is a one-time write surcharge (about 25 percent above input) the first time an entry is stored. The cache expires after about 5 minutes by default, with longer options available. OpenAI takes a different shape. It gives a flat 50 percent discount on cached input tokens, charges no explicit write fee, and turns caching on automatically once the prompt passes a length threshold. A cache hit is strict. The bytes of the cached prefix must exactly match a previous request. Change the system prompt, the tool schema, or even leading whitespace, and the hit breaks for everything after that point. The design rule follows directly: put long stable content (system prompt, documents, few-shot examples, tools) first, and the changing user query last. Then the largest part of every request reuses the cache.

**Prompt caching** stores the prefill KV state for a prompt prefix and replays it at a discount, so the model skips recomputing tokens it has already processed. The two providers price it differently. Anthropic charges cached reads at about 10 percent of the input rate. It adds a one-time write surcharge near 25 percent above input when a new entry is stored. The default cache lives about 5 minutes, with longer tiers available. OpenAI applies a flat 50 percent discount on cached input. It charges no separate write fee and caches automatically once a prompt clears a length threshold. A cache hit is a strict byte-equality check on the prefix. Any change above the dynamic suffix breaks it. That includes the system prompt, the tool schema, few-shot examples, even leading whitespace or JSON key order. There is no semantic matching. The architecture rule follows. Place the largest stable content at the front. Put the volatile user query last. Then the cacheable prefix dominates each request. Hit rate, not raw token count, drives per-call cost on agents, retrieval pipelines, and support bots that share long context across many turns.