Every developer who has integrated an AI agent into their terminal knows the sudden anxiety of the token burn. You start a session to fix a simple bug, but as the agent reads five different files, runs three test suites, and iterates on a solution, the context window swells. Suddenly, the latency increases, and the cost per turn spikes. This is the inherent tension of agentic workflows: the more the AI knows about your codebase, the more expensive it becomes to tell it the next thing. To solve this, Anthropic has implemented a sophisticated token economy within Claude Code that relies heavily on prompt caching and strategic context isolation.

The Economics of Prefill and Decode

To understand how Claude Code manages costs, one must first look at the underlying mechanics of LLM inference. The process is split into two distinct phases: prefill and decode. The prefill stage is where the model reads the entire prompt and context to understand the request. The decode stage is where the model generates the response, producing tokens one by one. Because the decode phase requires the GPU to remain occupied for a longer duration to generate sequential output, output tokens are priced significantly higher—approximately five times the cost of input tokens.

Claude Code mitigates the cost of the prefill stage through prompt caching. This technology allows the server to preserve the state of a previously processed token sequence, meaning the model does not have to re-calculate the same context every time a new message is sent. The pricing for this is asymmetrical. Reading data from the cache costs only 0.1 times the standard input rate, while writing a new sequence to the cache can cost up to 2 times the standard rate. For a long-running session, this creates a massive efficiency gain; once the codebase and system prompts are cached, every subsequent turn in the conversation becomes an order of magnitude cheaper.

To prevent the context from exploding during command execution, Claude Code employs a strict threshold via the `BASH_MAX_OUTPUT_LENGTH` setting. If the output of a terminal command exceeds 30,000 characters, the agent does not dump the entire text into the conversation. Instead, it saves the full output to a file and inserts only a brief preview and the file path into the context. This ensures that a single verbose log file does not consume the entire token budget for the rest of the session.

The Fragility of the Cache and Context Debt

While caching offers a path to affordability, it introduces a rigid structural requirement: the prefix must match perfectly. Claude Code organizes its requests in a specific hierarchy: tool definitions, followed by the system prompt, and finally the conversation history, which includes the `CLAUDE.md` file. If any element at the beginning of this chain changes, the entire subsequent cache is invalidated. This means that a seemingly minor adjustment, such as changing the `/effort` setting or switching the underlying model mid-session, wipes the cache and forces the system to re-prefill the entire context at full price.

This leads to the problem of context debt. Every file the model reads and every command output it processes accumulates in the session history. Even with the 0.1x cache read discount, the sheer volume of data occupies the model's context window, which can eventually degrade reasoning performance or increase the baseline cost of every turn. A common pitfall occurs when a developer allows a test suite to output 400 lines of success messages; while this is under the 30,000-character limit, it still adds unnecessary weight to every subsequent request.

Anthropic addresses this through the use of sub-agents. Unlike the main session, a sub-agent operates in a completely isolated context window. It possesses its own system prompt, tools, and `CLAUDE.md` but does not inherit the main session's conversation history. When a sub-agent is tasked with a heavy-lift job—such as analyzing a massive log file—it processes all the noise internally. Once the task is complete, it returns only the final answer to the main session, and all the intermediate tokens and tool outputs are discarded. This prevents the main session from becoming contaminated with transient data.

For the developer, the most effective way to manage these costs is through precision. Vague requests like "the tests are failing" force the agent to perform expensive discovery cycles using `grep` or recursive file reads, which bloat the context. In contrast, specifying a path, such as "fix the failing test in @utils.test.ts," bypasses the exploration phase and minimizes token consumption. Furthermore, tools like `/clear` and `/compact` allow users to manually prune the context. While `/clear` wipes the slate for a new task, `/compact` compresses the existing history to remove redundancies. Users should also be cautious with the `/loop` function; since it sends the entire session history with every iteration and the cache expires after one hour of inactivity, running loops in a dedicated terminal session is the most cost-effective approach.

Efficient AI engineering is no longer just about the quality of the prompt, but about the surgical management of the context window.