Prompt caching is everything ―― Claude Codeの課金構造をサクッと理解This article explains how prompt caching is the central mechanism behind Claude…

Prompt caching has quietly become one of the most consequential factors in the cost of running Claude Code, Anthropic's terminal-based agentic coding assistant. For developers who use the tool intensively, understanding how caching interacts with token billing is often the difference between a predictable monthly bill and an unexpectedly large one, which is why it deserves closer attention than most usage guides give it.

At its core, prompt caching addresses a structural inefficiency in how large language models handle repeated context. Every time an API request is made, the model normally reprocesses the full prompt from scratch, including system instructions, tool definitions, and any accumulated conversation history. In an agentic coding session, that context can be enormous and largely unchanged from one turn to the next. Caching lets the provider store the processed representation of a prompt prefix so that later requests can reuse it rather than recomputing it.

The economics are what make this important. Anthropic's pricing separates tokens into several categories: standard input tokens, cache write tokens, and cache read tokens. Writing to the cache typically costs more than a normal input token, commonly around 25 percent more for the default five-minute cache window, because the system must process and store the content. Reading from the cache, however, is dramatically cheaper, often priced at roughly one-tenth of the standard input rate. Output tokens are billed separately and are unaffected by caching. There is also a longer one-hour cache option, which appears to carry a higher write premium in exchange for a longer time-to-live.

This asymmetry explains why prompt caching sits at the center of Claude Code's billing structure. A coding session repeatedly sends a large, stable block of context—the system prompt, tool schemas, files that have been read, and prior exchanges—on top of a small amount of new input. Without caching, that entire block would be billed at full input rates on every turn. With caching, the bulk of it is written once and then read back at a fraction of the cost for the duration of the cache window. Because interactive coding tends to produce frequent, closely spaced requests, cache hits are common, and the savings compound over the length of a session.

Understanding this behavior has practical consequences. Actions that invalidate or fail to reuse the cache tend to be the expensive ones. Long pauses that let the cache expire, edits that change an early part of the context and therefore break the reusable prefix, or workflows that repeatedly restructure the beginning of a prompt can all force fresh cache writes. Keeping stable content at the front of the context and volatile content toward the end is likely to improve hit rates, since caching generally works on shared prefixes.

It helps to place this in the broader landscape. Prompt caching is not unique to Anthropic; OpenAI and Google have introduced comparable mechanisms, and the general trend across providers is toward pricing that rewards reusing context rather than resending it. Claude Code competes with tools such as GitHub Copilot, Cursor, Aider, and OpenAI's Codex-style agents, many of which grapple with the same cost pressure from large, repeated context windows. For subscription-based access through Claude's Pro and Max plans, the caching mechanics are handled behind the scenes, but for developers working directly against the API or through pay-as-you-go billing, they translate directly into money.

The prerequisite concept worth emphasizing is that tokens, not requests, are the unit of cost, and that not all tokens are priced equally. Once that distinction is clear, the value of caching becomes intuitive: the goal is to maximize inexpensive cache reads while minimizing full-price input processing and unnecessary cache writes. Anthropic exposes cache-related token counts in its API responses, so usage can be measured rather than guessed.

For teams budgeting around agentic coding, the takeaway is that cost optimization is less about limiting how much you use Claude Code and more about structuring interactions so the cache does as much work as possible. Treating prompt caching as a first-class part of the workflow, rather than an implementation detail, appears to be the most reliable path to keeping API spending in check.