Agent development is early. Builders iterate on architectures, prompts, tools, memory, and execution loops, and experimentation gets expensive fast because context re-sends every step. Model the cost of your loops here, entirely in your browser.
Worked examples that populate all fields. Assumptions shown below.
Sample prices are illustrative. Edit to match current provider pricing.
| Model | Input $/1M tok | Output $/1M tok |
|---|
No models yet. Add a model row to see costs.
Negative values are clamped to zero automatically.
Cost per run, per day (runs × experiments), and per month (30 days).
| Model | Per run | Per day | Per month |
|---|
context(step s) = base + s × (output + growth) [full history] context(step s) = base + min(window, s × (output + growth)) [sliding window] input tokens per run = Σ context(s) for s = 0..steps-1 output tokens per run = steps × output cost per run = input×(in$/1M) + output×(out$/1M) per day = cost × runs × experiments; per month = per day × 30
Each step re-sends the entire conversation plus tool results as input tokens. A 10-step loop does not cost 10× one call — the growing prompt makes input cost roughly quadratic in steps under full history.
Every extra token added per step (tool outputs, retrieved memory) is paid again on every subsequent step. Sliding windows or summarization cap that accumulation and flatten the cost curve.