Free tool / cost calculator

Multi-Agent Cost Estimator

Estimate the token and dollar cost of a multi-agent run, compare it with a single-agent baseline, and see how much coordination overhead changes the bill.

Run assumptions

Use realistic averages. For production, run this once with expected values and once with worst-case turns.

Single-agent baseline

Estimate the simplest one-agent run that attempts the same task.

The practical formula

A simple estimate is: agents times tokens per turn times turns, plus a coordination multiplier, divided by one million, times blended model price. The exact number will vary by provider and input/output mix, but the multiplier is the architectural signal: if the system costs four times more than a baseline, it needs measurable quality, coverage, or control gain.

Why does the calculator include coordination overhead?

Multi-agent systems spend tokens on delegation, summaries, synthesis, retries, judging, and trace repair. Those tokens are part of the architecture cost.

What model price should I enter?

Use a blended price per one million tokens for the model mix you expect. If input and output pricing differ, estimate the weighted average for your workload.

Is the single-agent baseline optional?

No. A multi-agent cost is only meaningful against the simplest baseline that can solve the same task. The baseline is your economic control group.

Does this include tool or infrastructure costs?

No. It estimates token spend only. Add search APIs, browser runs, storage, human review, and platform costs separately for production budgets.

Read next

Multi-agent cost guide

Learn the budget formula and when overhead is worth paying.

Cost blowup as failure mode

Understand why runaway tokens are an architecture risk.

Decision quiz

Check whether your task shape earns the extra spend.

Sources used on this page

Anthropic / 2025-06-13

How we built our multi-agent research system

Concrete pro case for breadth-first research, with reported 90.2% internal eval gain and about 15x chat token use.

Anthropic / 2024-12-19

Building effective agents

Defines workflows vs agents and recommends starting with the simplest solution that meets the task.

OpenAI / 2025

A practical guide to building agents

Recommends maximizing one agent first, then splitting for prompt complexity, tool overload, manager patterns, or handoffs.

Cognition / 2025-06-12

Don't Build Multi-Agents

Argues that parallel agents are fragile when context and implicit decisions are not shared thoroughly.