--- title: How to estimate what a coding agent run will cost before you start it slug: how-to-estimate-what-a-coding-agent-run-will-cost-before-you-start-it published_at: 2026-10-14 13:00:00 +0000 updated_at: 2026-10-06 21:14:11 +0000 summary: Most of an agent's bill is reading, not writing. A back-of-the-envelope formula for estimating a run before you start it, and six habits that keep the number small. tags: [ai, llm, coding-agents] author: CJ Avilla url: https://www.cjav.dev/articles/how-to-estimate-what-a-coding-agent-run-will-cost-before-you-start-it type: article --- # How to estimate what a coding agent run will cost before you start it *Published: October 14, 2026* *Tags: ai, llm, coding-agents* A coding agent's bill rarely comes from the code it writes. It comes from what the agent reads, and from how many times it reads it again. That's why "rename one variable" can cost more than "write a new module." The rename sends the agent into a large repo to find every use. The module mostly needs the agent to write. Here's how I estimate a run before I start it, and the habits that keep the number small. ## The shape of the bill Every model call bills two things: input tokens (everything the model reads on that turn) and output tokens (everything it writes back). Output tokens cost more each, often several times more. But an agent sends far more input than it gets back. An agent run is a loop. On each turn the model gets: - the system prompt and tool definitions, - the whole conversation so far, including every file it has opened and every command output it has seen, - your latest message or the last tool result. The conversation grows each turn, and each turn sends all of it again. So input cost grows faster than the number of turns: turn 40 resends what turns 1 through 39 collected. ## A back-of-the-envelope formula You don't need exact numbers, only the right order of magnitude. ``` input tokens ≈ turns × average context size output tokens ≈ turns × average reply size cost ≈ input tokens × input price + output tokens × output price ``` Average context size is roughly half of where the context ends up, because it starts small and grows. A rough rule: one line of code is about 10 tokens, so a 400-line file is about 4,000 tokens. **An example with round numbers.** Use your provider's real prices; these are only for illustration. Say input costs $3 per million tokens and output $15 per million. A small task: 15 turns, context growing to 40,000 tokens (average 20,000), replies averaging 500 tokens. - Input: 15 × 20,000 = 300,000 tokens → about $0.90 - Output: 15 × 500 = 7,500 tokens → about $0.11 About a dollar, and nearly all of it is reading. An overnight run on a flaky test: 400 turns, context that keeps getting compacted back to around 150,000 tokens (average 100,000), replies averaging 800 tokens. - Input: 400 × 100,000 = 40 million tokens → about $120 - Output: 400 × 800 = 320,000 tokens → about $4.80 The second run isn't 120 times bigger because the work is 120 times harder. It's bigger because the agent kept reading. **Caching changes this a lot.** Most providers now cache the start of the prompt when it hasn't changed between turns and charge much less for those cached tokens. If your agent supports it (most do, automatically), the real bill for long runs can be a fraction of the formula. Treat the formula as an upper bound and check your usage page for the real number on a run or two. ## Six habits that keep the number small **1. Point at the file, not the repo.** "Fix the null check in `billing/invoice.ts`" costs a fraction of "fix the billing bug." The agent doesn't have to search, and search results stay in the context for the rest of the session. **2. One task per session.** A fresh session starts with a small context. A session that already fixed three unrelated things carries all three into the fourth. When you finish a task, start over. **3. Cap the turns.** Most agents let you set a maximum number of turns or a budget. For anything that runs while you're away, set one. An agent stuck retrying a flaky test will keep going as long as you let it. **4. Keep big outputs out of the context.** Test runners, build logs and `git diff` on a large change can each put tens of thousands of tokens into the conversation. Ask the agent to run only the failing test, or to show just the failing lines. **5. Use the smaller model for the small jobs.** Renames, formatting and "add a test like this one" don't need the largest model. Most tools let you switch per session. **6. Watch the first long run.** The first time you hand an agent a big task, keep the usage page open. Ten minutes of watching tells you more about your real cost per hour than any formula. ## When the bill is worth it None of this is an argument against long runs. An overnight run that lands a working migration is cheap next to a week of someone's time. The point is to decide that before the run, not after you read the bill. I estimate the cost, set a budget a bit above it, and let it go. If it hits the cap, that usually means the task was unclear, not that the budget was too low. --- *I sell a few shirts about this at [The Paperclip Factory](https://thepaperclipfactory.com/collections/gifts-for-ai-engineers?utm_source=cjav.dev&utm_medium=blog&utm_campaign=agent-run-cost), mostly for people who have opened their usage page and needed a minute.* --- [Back to all articles](https://www.cjav.dev/articles) --- ## Hey Agent, thanks for visiting! 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