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Agent cost planning

AI Agent Cost Calculator

Estimate the API cost of multi-step agents, tool-using workflows, research loops, and automated task runners.

Start estimatingView models

Scenario assumptions

Start with these editable numeric assumptions, then adjust the calculator fields to match your own traffic and token usage.

Tasks per month

1,000

User or scheduled tasks handled by the agent.

Steps per task

6

Average model calls for planning, tool use, review, and final output.

Input tokens per step

1,800

Instructions, tool results, memory, and task context.

Output tokens per step

900

Intermediate reasoning summaries, tool instructions, and final response content.

AI agent model and usage

Choose a model and edit the numeric assumptions for this AI agent scenario.
AnthropicClaude Opus 4.1

Input price

$15.00 / 1M tokens

Output price

$75.00 / 1M tokens

AnthropicClaude Opus 4.1Deprecated
Official source

Verified Aug 10, 2026. Estimates vary by usage and provider pricing conditions.

Usage assumptions

Estimate traffic and token usage for an average request.
Active seats, customers, or internal users.
Average AI calls per user each day.
Prompt, history, and retrieved context per request.
Generated answer length; SaaS founders should test long replies.
Reusable cached prompt or context tokens per request.
Use 30 for always-on products or fewer for batch jobs.

Estimated results

Run the calculator to see projected cost and usage volume.

Enter your usage details, then select Calculate estimate to see your projected cost.

Estimated cost = input usage cost + output usage cost + supported optional charges.

How to read the output

Use the result as a planning estimate, then validate against provider pricing and real usage once the workflow is live.

Cost per task

Agent costs often multiply because one task can require several model calls instead of a single response.

Monthly automation spend

Use monthly and yearly estimates to decide whether tasks need cheaper routing, limits, or caching.

Step count sensitivity

Compare simple and complex task paths because retries and tool loops can materially change cost.

Models to consider

These options come from the central model data and are suggestions to evaluate, not claims that one model is best for every workload.

Anthropic

Claude Opus 4.1

Reasoning-capable option to evaluate for multi-step tasks.

Input $15.00 / 1M, output $75.00 / 1M.

Open model

Anthropic

Claude Opus 4

Reasoning-capable option to evaluate for multi-step tasks.

Input $15.00 / 1M, output $75.00 / 1M.

Open model

Anthropic

Claude Sonnet 5

Reasoning-capable option to evaluate for multi-step tasks.

Input $2.00 / 1M, output $10.00 / 1M.

Open model

Anthropic

Claude Sonnet 4.6

Reasoning-capable option to evaluate for multi-step tasks.

Input $3.00 / 1M, output $15.00 / 1M.

Open model

Related planning pages

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AI Usage Calculator

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AI agent FAQ

Short answers for estimating this scenario without sharing private prompts, documents, or customer data.

Why do AI agents cost more than simple chatbots?

Agents can make multiple model calls per task for planning, tool use, retries, validation, and final output.

How do I estimate steps per task?

Map the common path for one task, count expected model calls, then add a small allowance for retries or review steps.

Can I reduce agent API cost?

Often yes. Route simple steps to cheaper models, cap loops, shorten tool output, and cache repeated context when appropriate.