AI Agent Cost Calculator

An agent doesn’t make one API call — it loops, re-sending a growing context on every step. Model your loop below to see what one autonomous task really costs, then watch routing the routine steps to a cheaper model cut the bill.

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Your agent loop

Loop iterations (plan / act / verify…) per task.
System prompt + tools + first input.
Tokens appended each step (history, tool output).
Model’s response length per step.
Across your whole product.
Used for the reasoning-heavy steps.
Tool calls, formatting, simple decisions.
In most agent loops only a minority of steps need the big model. Smart routing sends the rest to the cheap one.
0
Tokens / task
0
API calls / month
$0
All-frontier / mo
$0
Saved / mo by routing
Every step on the frontier model
$0
per month
Per task$0
Per day$0
⚡ Smart routing (the QADIR way)
$0
per month
Per task$0
Per day$0
Provider Model In $/1M Out $/1M Cost / task Cost / month

“Cost / task” runs the whole loop on a single model so you can see how much the model choice alone matters. Prices per 1M tokens, standard (non-cached, non-batch) tier, verified June 2026. Token figures are estimates; exact counts depend on each model’s tokenizer. Confirm live rates: OpenAI, Anthropic, Google, DeepSeek. Tip: prompt caching can cut input cost up to ~90% on the repeated prefix — huge for agents, since the system prompt and tools repeat every step.

Your agent shouldn’t pay frontier prices for routine steps

You just watched routing cut an agent’s bill without touching quality on the steps that matter. QADIR OS does this automatically on every step of every loop — cheapest model that clears the bar, frontier model only when the task demands it. Opus-grade output at Haiku-grade cost.

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Why agent runs cost what they cost

Why is running an AI agent so much more expensive than a single API call?

An agent doesn’t answer once — it loops: perceive, plan, act, verify, reflect. Each step is a separate API call, and every step re-sends the accumulated context: the system prompt, the tools, and the full history so far. So one task is often 5 to 30 calls whose input grows on every step. With the defaults above, the last step alone carries several times the tokens of the first — which is why agent costs scale far faster than a one-shot chat completion.

How do I reduce AI agent costs?

The biggest lever is routing: send the routine steps — tool calls, formatting, simple decisions — to a cheap, fast model, and reserve a frontier model only for the genuinely hard reasoning. Caching the static prompt prefix and trimming history help too. Routing alone usually cuts agent spend 40–80% with no quality loss on the steps that never needed the big model in the first place.

What is LLM model routing?

Routing automatically picks the cheapest model that still clears the quality bar for each individual step, instead of sending everything to one expensive model. Because most steps in an agent loop are routine, routing them to a cheaper model captures most of the savings while keeping frontier quality exactly where it matters. The slider above lets you see the trade-off for your own loop.

Is my data sent to a server?

No. This calculator runs entirely in your browser. Nothing you type is uploaded, logged, or stored anywhere.