Scenario presets
Worked examples. Each preset fills every field with visible assumptions.
Model price table
Illustrative sample prices — edit to match current pricing from your provider.
| Model | Input $/1M tok | Output $/1M tok |
|---|
Loop simulator
Negative or empty values are clamped to valid minimums.
Context growth per step
Input tokens sent at each step of one run. Red line is your context limit.
Live results
| Model | Cost / run | Cost / day | Cost / month (30d) |
|---|
How this is calculated
Input tokens at step i: full history = prompt + (i-1) x (output + growth); sliding window = min(that, prompt + window). Cost per run = sum(input_i) x in$/1M + steps x output x out$/1M. Day = run x runs x experiments. Month = day x 30. All math runs in your browser; nothing is uploaded.
Monthly cost comparison
FAQ
Why do agent loops get expensive?
Every step re-sends the system prompt, tool schemas, and accumulated history as input tokens. An 8-step run does not cost 8x one call — it costs far more, because each step's input includes everything before it.
How does context growth multiply cost?
If context grows linearly per step, total input tokens grow quadratically with steps. Doubling steps roughly quadruples input spend on full-history agents. Sliding windows cap that growth at the window size.