AI Agent Cost Calculator: Estimate LLM Loop & Token Spend

Model price table

Illustrative sample prices per 1M tokens. Edit to match current pricing from your provider.

Model	Input $/1M	Output $/1M

Loop simulator

Set your own values or load a worked preset.

System + tool prompt tokens

Avg output tokens / step

Steps per run

Extra context growth / step (tool results)

History mode

Runs per experiment

Experiments per day

Context limit (tokens)

Live results

Cost per run, per day, and per 30-day month for each model.

Model	Per run	Per day	Per month

Context growth per stepExceeds context limit

Monthly cost comparison

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How this is calculated

context(step i) = prompt + (i-1) x (output + growth)
  sliding window: history capped at window size
input tokens per run = sum of context over all steps
output tokens per run = steps x avg output
cost per run = input x in$/1M + output x out$/1M
per day = per run x runs x experiments
per month = per day x 30

FAQ

Why do agent loops get expensive?

Each step of an agent loop resends the system prompt, tool schemas, and the growing conversation history as input tokens. An 8-step run does not cost 8x one call; it costs more, because later steps carry the accumulated context of every earlier step.

How does context growth multiply cost?

If each step adds output plus tool results to history, input tokens grow roughly quadratically with step count under full history. A sliding window caps that growth at the window size, trading recall for a bounded per-step cost.