Why AI Agents Cost More Than You Expect (and How to Cut the Bill)

Estimating the cost of an AI agent by multiplying one model call by the number of steps gives an answer that is far too low. An agent doesn't make independent calls. Each step sends the system prompt and tool definitions again, plus everything earlier steps produced: their outputs and the results of every tool they called. The context grows with every step, and so does the bill.

The math

For n steps with a fixed prompt S, and each step adding its output O and tool results R to the context, total input is n × S + (O + R) × n(n − 1) ÷ 2. The second term, the accumulated context, grows with the square of the number of steps.

Each task sends 57,600 input tokens, and 58% of them are accumulated context rather than the fixed prompt. The final step alone carries 11,400 tokens. With 10% of tasks retried, that's about $0.16 a task, or $161.92 a month for 1,000 tasks.

Why more steps cost disproportionately more

Double the agent to 16 steps and the cost per task doesn't double. It rises from about $0.16 to about $0.49, roughly three times as much, because every extra step carries all the context before it. Long-running agents are where budgets break.

Four ways to cut the bill

1. Trim tool results before feeding them back. Halving tool output from 800 to 400 tokens a step cuts this example from about $0.16 to about $0.14 a task, and the saving grows with the number of steps.
2. Use a cheaper model where it's good enough. The same agent on Claude Haiku 4.5 costs about $0.08 a task, half the Sonnet 5 figure. Many agents route routine steps to a small model and only hard ones to a large one.
3. Cache the fixed prompt. System prompts and tool definitions are identical on every step, and most providers bill cached input at a fraction of the normal rate.
4. Cap and summarise. Set a maximum number of steps, and replace older context with a short summary once it grows past a threshold.

Measure cost per completed task

The number that matters isn't price per token but cost per task that actually succeeds. A stronger model that finishes in 5 steps can cost less than a cheap one that takes 15, or fails and retries. Log steps, tokens and outcomes per task from the start, and compare models on that.

Tip: Reasoning or thinking tokens are billed as output. On agents that reason at every step, include them in output per step or the estimate will be low.