How Cursor and coding agents cut AI costs by caching reusable prompt computations

Coding tools like Cursor can serve nearly all input tokens from cache, dramatically reducing processing costs during long sessions. These tools work by structuring prompts with static content — such as system instructions and file context — placed first, so the model's computed Key/Value states can be reused across conversation turns. Because large language models are stateless by default, they would otherwise reprocess the entire context from scratch on every request. Prompt caching short-circuits this by saving intermediate transformer computations from the prefill stage, meaning only newly added tokens need fresh processing. Most major AI providers now apply this optimization automatically, making extended agentic coding sessions faster and cheaper without requiring any action from the user.