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What counts as one agent task’s cost?
An agent task can trigger several model requests, tool calls, nested agent work, handoffs, retries, or context compaction. The run’s visible answer may represent only the last step. OpenAI’s Agents SDK documentation says, “The Agents SDK automatically tracks token usage for every run,” and describes both run-level totals and request-level usage, including compaction usage.
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For a given provider and endpoint, estimate the billable cost as:
Task cost = Σ (usage in each token category × that category’s rate) + separately billed tool or modality charges
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Include all billed requests attributable to the task, including retries and nested calls. Keep uncached input separate from cached input if they have different rates. Use provider-reported usage fields when available; character counts are not a reliable substitute for actual token usage.
Which usage and rates should you track?
Token categories do not necessarily share one price. Depending on the model and endpoint, input, cached input, output, and some reasoning or cache operations may be priced differently. Check the current official rate card for the exact model and endpoint rather than applying one blended rate to every token. OpenAI’s pricing page is one example of a provider’s official rate information; rates and product terms can change.
Some charges are not text-token charges at all. Add billed tool use, grounding, or modality usage—such as applicable audio or image processing—where your implementation uses them. Consult the relevant provider’s billing documentation for those charges; do not assume the token calculation covers everything.
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Listed rates alone cannot predict a task’s cost. Different tasks can generate different amounts of input, output, and reasoning, and different models may tokenize the same material differently. A model with a lower listed token rate can still cost more for a task if it uses more tokens, requests, or retries. Test representative work and calculate from the observed usage mix.
How to estimate cost per task
- Define representative task types. Sample the workflows your agent actually handles, including routine work and more demanding cases. A single easy task is not a dependable forecast for a varied workload.
- Capture each run’s usage. Record the task’s total input and output tokens, cached-input and reasoning usage where reported, and the usage for every model request. Note the request count, retries, nested calls, and any compaction.
- Apply the rates for the exact configuration. Use the applicable model and endpoint rates for each token category, then add separately billed tools or modalities. Keep the rate’s unit convention consistent—for example, if rates are per million tokens, convert usage to that unit before multiplying.
- Record the outcome alongside the cost. For each task, note whether it succeeded and assess its quality. Compare cost per successful task, not just average spend or cost per attempt; a cheap run that frequently fails may be a poor configuration.
- Review the spread, not only the average. Look at typical runs and unusually expensive ones. A few long, retry-heavy, or tool-intensive runs may matter even when most tasks are inexpensive.
Repeat the sample after changing the model, prompt, reasoning effort, context size, caching, number of agent steps, retry policy, or tools. Recheck rates as well: a forecast based on old pricing or a different endpoint may no longer apply.
How to track costs across a run
Capture both totals and request-level detail
Run totals answer “What did this task cost?” Request-level entries help answer “Where did the cost come from?” Keep both. Per-request records can expose repeated calls or an expensive stage hidden inside an otherwise ordinary-looking run. If you use the OpenAI Agents SDK, its usage documentation describes aggregate and request-level usage access, including compaction.
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Inspect traces and provider reports
Use traces or equivalent observability to examine agent and subagent activity, then connect those records to your own task or workflow identifiers. Provider dashboards and exports can help attribute usage by project where supported. OpenAI’s usage guidance describes project filters and exports and notes that usage data is not combined across organizations.
For useful accounting, retain enough context to explain a cost change: task type, model and endpoint, request count, token-category usage, retries, tool or modality charges, and outcome. Apply appropriate access controls to logs because prompts and traces may contain sensitive information.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which controls prevent runaway spend?
Different controls solve different problems. A request-size limit constrains an individual request, a rate limit governs how quickly requests can be made, and a spend limit constrains expenditure. None is a substitute for the others. Set controls at the provider level where available, and add application-level checks when you need a per-task or per-customer budget.
- Set a task budget: Track cumulative usage during the run and stop, request approval, or switch to a defined fallback when the task reaches its budget.
- Limit runaway loops: Bound the number of agent steps and retries, and make repeated failures visible rather than allowing indefinite retries.
- Constrain oversized requests: Set appropriate input or request-size limits for the workflow, while preserving the context needed to complete it.
- Use rate limits for traffic control: They can restrict request speed, but do not by themselves cap total spend over time.
- Set provider spend controls: Use workspace, project, or role-based controls where your provider and plan support them. Anthropic’s enterprise administration guidance describes spend caps and role-based controls; availability depends on the product and account.
- Review alerts and outliers: Watch usage by workflow and investigate sudden increases in request count, token volume, retries, or tool activity.
How to compare agent designs fairly
When comparing models or implementations, use the same representative tasks and evaluate the whole task rather than the price of an isolated call. Track:
- Cost per successful task, alongside quality and completion rate.
- Total input and output tokens per run, including cached-input and reasoning-token mix where available.
- Model request count, nested calls, and retries.
- Separately billed tools, grounding, or modality usage.
- Latency, when it affects the product or user experience.
Do not choose a design based only on the lowest listed token rate or lowest raw spend. A design that needs more attempts, produces weaker results, or fails more often can be more expensive in practical terms. Re-evaluate after pricing or implementation changes.
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