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There is no fixed price for one AI agent run. A run may contain several model requests, so estimate its model cost by adding the input, cached-input, output and billed reasoning-token charges for every request, then add any separately metered tool charges. That gives you a provider-usage estimate—not necessarily the full cost of running an application.
How to calculate an agent run’s cost
Use the actual usage for the completed run and the rates for the exact model, token category and service tier:
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Run cost = input charges + cached-input charges + output and billed reasoning charges + separately metered tool charges
For each model request, multiply the billable token count in each category by its applicable rate, then sum the requests. If the provider includes reasoning tokens in another billed category, follow that provider’s pricing rules rather than counting them twice. The OpenAI Agents SDK aggregates usage across model calls in a run and also exposes per-request usage entries that can help explain the total: OpenAI Agents SDK usage documentation.
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This calculation covers metered provider usage. Hosting, storage, orchestration subscriptions, negotiated rates and staff time may also affect an application’s economics; there is no single all-in deployment figure established here.
Worked example: Gemini API token charges
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 would have this model-token subtotal:
| Category | Calculation | Charge |
|---|---|---|
| Input | 100,000 ÷ 1,000,000 × $0.30 | $0.030 |
| Output | 10,000 ÷ 1,000,000 × $2.50 | $0.025 |
| Model-token subtotal | $0.030 + $0.025 | $0.055 |
This is arithmetic from Google’s published standard rates, not the measured price of a tested run. It excludes any separately applicable tools. Google says agent usage includes standard model charges for input, output and intermediate reasoning tokens during agentic loops, plus applicable tool charges. Check the current Gemini API pricing before budgeting; rates and tool schedules can change.
Why one run can contain many billable requests
An agent may ask the model what to do, call a tool, receive the tool’s result and ask the model again. Those are separate model requests whose usage must be included in the run total. Tool use can raise the bill in two distinct ways:
- More model tokens: Tool definitions and the content sent to or returned from tools can be included in model context.
- Separate tool fees: Some providers meter services such as web search in addition to model tokens. Anthropic says tool-use charges include input tokens, including the tools parameter, and generated output; some server-side tools, such as web search, also have usage-based pricing. See Anthropic’s pricing documentation.
Google likewise publishes separate rates for grounding and other tools. Do not assume that all tools are free, billed per call, or billed in the same way: check the provider’s schedule for each tool you use.
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Measure a real run instead of estimating from the answer
Visible response length is not a reliable substitute for usage records: it does not show every request, cached input or other billed token category. Capture provider usage data or SDK telemetry for representative completed runs, then reconcile it with the provider’s billing records. OpenAI’s guidance describes using API responses and the Usage Dashboard to inspect token counts and activity: OpenAI token-usage guidance.
Record these details for each run:
- Model identity and applicable rate tier
- Number of model requests
- Input, cached-input, output and reasoning-token counts where available
- Tool calls and any separately reported tool usage
- Region or endpoint, if it changes the rate
- Total billed amount for the completed task
The Agents SDK provides aggregate run usage and request_usage_entries for per-request breakdowns. The aggregate is useful for a run total; the entries help identify which requests or stages account for it.
Compare the cost of the same task, not just token rates
A lower price per million input tokens does not guarantee a cheaper completed task. Models can differ in tokenization, the number of requests they need, and how much output or reasoning they generate. Compare providers on the same representative task, and consider cost alongside quality and latency. OpenAI discusses these factors in its production best practices.
For a useful comparison, hold the task and evaluation criteria constant, and track the model and rate tier, token categories, request count, tool usage, region or endpoint, and total bill. Include cached-token and reasoning-token treatment wherever the provider reports it. Verify current rates directly: Anthropic documents a 1.1× multiplier for certain US-only inference settings on newer models, illustrating how service configuration can affect the rate.
How much can run costs vary?
Costs can vary even when the nominal task is the same, because an agent may take a different path, make more requests or consume more tokens. A 2026 arXiv preprint on agentic coding tasks reports up to 30-fold variation in total tokens across runs of the same task. It also reports 1,000 times more token consumption for agentic tasks than for code reasoning and code chat in its benchmark comparisons. These are findings within that paper’s studied setting, not universal multipliers or a forecast for an arbitrary agent: 2026 preprint on token consumption in agentic coding tasks.
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