AI Agent Cost

AI Agent Cost Calculator for Tool-Use Workloads

Estimate cost per agent run — including planner calls, tool-use loops and retries. See where multi-step agents burn tokens and how to bring the bill down.

  • Cost per run — LLM calls + tool overhead
  • Monthly projection across your run volume
  • Cheapest-model swap for the planner loop
Calculate Agent Cost

AI Cost Calculator

Instant monthly cost estimate based on average input/output pricing.

1 Select model
5,000,000
3 Estimated monthly cost
~$23.75/ mo

Based on average pricing — input $2.50 / output $10.00 per 1M tokens.

Potential monthly savings
~$22.00
~$264 / year on the table
Alternative model detected
OpenAI GPT-4o mini
Comparable tier · 94% lower output cost
Efficiency gap
94%
vs. cheaper frontier in your quality tier
Optimization intelligence · Locked

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Why AI agents cost more than single-shot LLMs

An agent is not one LLM call — it's a loop. The planner reasons about the goal, picks a tool, reads the tool result, decides the next step, and repeats until it answers. Every hop pays for the full context: system prompt, tool schemas, chat history and all prior tool outputs. That accumulation is why an agent that "feels" the same size as a chat call can be 5–20× more expensive per run.

How to reduce agent cost

  1. Cap max steps. Most workflows stabilize within 3–5 tool calls; hard-limit the loop.
  2. Split planner vs. writer. Cheap model plans and calls tools; frontier model composes the final answer.
  3. Trim tool schemas. Verbose JSON schemas quietly inflate every prompt in the loop.
  4. Cache tool outputs. Deterministic tools (search, lookup) should hit a cache inside a session.

Frequently asked questions

What drives the cost of an AI agent?+

Agent cost is dominated by three things: the number of LLM calls per run (planner + tool-use loops), the size of each prompt (system prompt, tool schemas, chat history) and the output tokens generated at each step. Multi-step agents typically make 3–8 LLM calls per user request, so per-run cost can be 5–20× a single chat completion.

How do I estimate cost per agent run?+

Multiply average input tokens per step by the number of steps, add expected output tokens per step, then apply the model's per-token price. Include tool-call overhead — each tool response gets fed back into the next LLM call, so tokens accumulate across the loop.

How can I cut agent costs without breaking quality?+

Shrink the system prompt and tool schemas, cap the max steps, route planning to a cheaper model and reserve the frontier model for final answers, and cache tool outputs when the same query recurs inside a session.

Which models are cheapest for agent workloads?+

For tool-use loops, small models with strong function-calling — GPT-4o mini, Claude Haiku, Gemini Flash — are usually the best price/quality trade-off. Reserve GPT-4o, Claude Sonnet or Gemini Pro for the final synthesis step where reasoning matters.

Is the agent cost calculator free?+

Yes. The calculator and baseline estimate are free. Upgrade to unlock the full optimization report — cheapest-model swap, savings breakdown and scenario simulation.

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