AI Agent Cost Estimator

A single chat reply is cheap. An agent that takes 8 steps, calls 3 tools, and re-reads a growing context on every turn is not. Model your real workflow below.

Steps per task (avg LLM calls to finish one task)

Context overhead multiplier

Avg input tokens per step

Avg output tokens per step

Tasks completed per day

Provider	Model	Cost / task	Est. monthly cost

Why the overhead multiplier matters

Most agent frameworks re-send the full running context (system prompt, prior tool outputs, conversation history) on every single step. A task that "should" cost X often costs 1.5–2.5x that because each of the 8 steps is re-reading everything that came before it, not just its own slice. The multiplier field above lets you approximate that without hand-modeling every step's exact context size. Start at 1.5–2x for typical ReAct-style agents; go higher for long research/browsing agents that accumulate large tool outputs.