Cut Your AI Coding Bill in Half (2026): Prompt Caching & Model Routing

AI coding can cost real money — teams report monthly API bills from $1,600 to $5,000. Most of that spend is not the code the model writes; it is the context you resend on every request: your system prompt, conversation history, and reused files. The fixes are simple, and you can model them with our AI Cost Calculator.

1. Cache the stable prefix

System prompts, project rules (your CLAUDE.md), and fixed reference files rarely change between calls. Providers bill cached tokens at roughly 10% of the normal input rate. Turn on prompt caching and the bulk of your context cost collapses. In the calculator above, toggle "Use prompt caching" to see the savings on your own numbers.

2. Route by difficulty

You do not need the most expensive model for every task. Use a flagship model (Claude Sonnet 5, GPT-5.5) for hard architecture and debugging, and a cheap model (Claude Haiku 4.5, Gemini 3.5 Flash) for boilerplate and refactors. Tiered routing commonly cuts spend 40–60%. Compare options in our LLM Model Compare tool.

3. Shrink the context you resend

Context bloat is the silent budget killer: every extra kilobyte is re-billed on every call. Prefer targeted diffs over full-file rewrites, start a fresh session for a new task, and drop stale history. One team cut per-request tokens 83% just by sending patches instead of whole files.

4. Cap the loop

If a task is not solved in 15–25 turns, more turns will not help — you are in a loop. Stop, change approach, or split the task. Unbounded agent loops are where bills quietly explode.

Estimate before you build

Plug your tokens and volume into the AI Cost Calculator to see exactly how much each lever saves before you commit a budget.

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