AI Agent Experiment Cost Calculator
Agent development is early. Every architecture, prompt, tool stack, memory system, and loop you test costs tokens. Model your experiment budget before the invoice models it for you. All math runs in your browser.
Model pricing
Illustrative sample prices. Edit to match current provider pricing.
| Model name | Input $/1M | Output $/1M |
|---|
No models yet. Add a model row to see cost estimates.
Loop simulator
Describe one agent run. Values below zero are clamped to zero.
Presets
Context growth per step
Input tokens the model reads at each step of a run.
Within context limitLive cost results
Monthly comparison
How this is calculated
Show formulas
history(i) = (i-1) * (avgOutput + contextGrowth) if sliding window: history(i) = min(history(i), windowSize) input(i) = systemTokens + history(i) inputTokens/run = sum over steps of input(i) outputTokens/run= steps * avgOutput cost/run = inputTokens * inPrice/1e6 + outputTokens * outPrice/1e6 cost/day = cost/run * runsPerExperiment * experimentsPerDay cost/month = cost/day * 30
FAQ
Why do agent loops get expensive?
Each step of a loop re-sends the entire conversation so far. A 20-step run does not cost 20 single calls; it costs 20 calls where each one carries all prior tool results and reasoning, so input tokens grow roughly quadratically with steps.
How does context growth multiply cost?
If each step adds tokens to history, step N reads everything from steps 1 through N-1. Doubling steps can quadruple input spend. Sliding windows or summarized memory cap that growth at a fixed window size.