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There is no fixed price for one AI agent run. A run may contain several model requests, tool calls and intermediate steps, so estimate it by adding the charges for every request and any separately billed tools—not by pricing only the final answer.
How to calculate the cost of an agent run
For a metered API, calculate each model request using the rate for its model and token category, then add any separately metered tool charges:
Run cost = input charges + cached-input charges + output and billed-reasoning charges + separately metered tool charges
Not every provider exposes or bills every category separately. Use the categories and rates that apply to the exact model, endpoint and service tier. OpenAI’s guidance explains that model and token category affect price, and that a lower per-token rate does not necessarily make a completed task cheaper: OpenAI token and usage guidance.
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Why one run can include several charges
An agent commonly asks a model what to do, makes a tool call, receives the result and asks the model to continue. Each model request contributes its own usage to the run total. OpenAI’s Agents SDK provides aggregate usage for a run and per-request entries, including requests that result in tool calls or handoffs: Agents SDK usage documentation.
Tools can affect the bill in two ways: their descriptions and returned content may consume model tokens, and some tools carry an independent usage fee. Anthropic says tool-use pricing includes input tokens (including the tools parameter) and generated output, with additional usage-based pricing for some server-side tools such as web search. Google also lists separate charges for tools such as grounding. Check the applicable provider schedule rather than assuming all tool calls are free or priced alike: Anthropic pricing and Google Gemini API pricing.
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OpenAI says the Agents API adds no separate fee for using the API itself; customers pay for the tokens and tools their agents use, according to its announcement: OpenAI Agents API announcement. That statement does not establish the full cost of operating an application, which can also involve hosting, storage, orchestration subscriptions, negotiated rates and staff time.
A worked example using published rates
Google’s pricing table lists standard Gemini 3.5 Flash-Lite text input at $0.30 per million tokens and output at $2.50 per million tokens. At those listed rates, a hypothetical run using 100,000 input tokens and 10,000 output tokens has this model-token subtotal:
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| Category | Calculation | Cost |
|---|---|---|
| Input | 100,000 ÷ 1,000,000 × $0.30 | $0.030 |
| Output | 10,000 ÷ 1,000,000 × $2.50 | $0.025 |
| Model-token subtotal | Input plus output | $0.055 |
This is arithmetic from Google’s published standard rates, accessed October 5, 2026; it is not the measured cost of a tested run. It excludes any applicable tool charges. Google says agent usage includes standard model charges for input, output and intermediate reasoning tokens during agentic loops, plus applicable tool charges. An actual agent may use more or fewer tokens than this example, and rates can change: Google Gemini API pricing.
How to measure your own runs
Use provider usage data or SDK telemetry for completed runs, not the visible length of the final response. Record the information needed to reproduce and explain the bill:
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- Model identity, endpoint or region, and service tier.
- Number of model requests in the run.
- Input and output tokens for each request, plus cached and reasoning-token details where available.
- Tool calls and their separately billed usage, if any.
- Total usage and cost for the completed task.
The Agents SDK’s aggregate run usage is useful for totals; its per-request entries help identify which steps drove usage. OpenAI also points to API response data and the Usage Dashboard for checking token counts and activity: OpenAI usage guidance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare costs between providers
Run the same representative task and compare the resulting task bill alongside quality and latency. Token prices alone are not enough: different models can tokenize the same material differently and generate different amounts of reasoning or output. Include these factors in the comparison:
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- Input, cached-input, output and billed-reasoning rates for the exact models.
- Tokens and number of requests used to complete the same task.
- Tool and search charges, including tool data passed back into model requests.
- Region, endpoint and service-tier modifiers.
- Measured total cost, quality and latency for representative completed runs.
Pricing modifiers can matter: Anthropic documents a 1.1× multiplier for certain US-only inference settings on newer models. Confirm the current rate and tool schedule before budgeting, since published pricing can change: Anthropic pricing.
A 2026 arXiv preprint studying agentic coding tasks reports up to a 30-fold variation in total tokens across runs of the same task, and 1,000 times more token consumption for agentic tasks than for code reasoning and code chat in its benchmark comparisons. Those figures describe that paper’s setting, not a universal multiplier or a forecast for any particular agent: 2026 arXiv preprint on token consumption in agentic coding tasks.
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