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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →To calculate an AI workflow’s true cost per successful task, add every cost incurred during the measurement period—including all model attempts, retries, tools, infrastructure, and required human review—then divide by the number of tasks that meet a written acceptance standard. Report the success rate and quality alongside that figure: otherwise, a workflow can appear cheaper simply because fewer of its outputs pass.
Define the outcome before counting costs
Start by deciding what counts as one logical task and what qualifies as success. “The model returned an answer” is not enough if the work must pass a check, resolve a case, or receive approval. Write an acceptance test that reviewers can apply consistently, such as required fields being valid, a test suite passing, or a support ticket remaining resolved.
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Keep that rule fixed when comparing workflows. If task types differ substantially in difficulty or value, calculate them separately rather than allowing an easy-task mix to make one option look artificially efficient.
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Use a complete cost formula
For a defined measurement period, calculate:
Fully loaded workflow cost = model and media charges across all attempts + tool, search, and retrieval charges + infrastructure and data-service costs + other direct workflow charges + required human review + relevant allocated shared-platform costs.
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Human review cost = review and correction hours × loaded hourly labor rate.
Cost per accepted task = fully loaded workflow cost ÷ number of tasks that meet the agreed success standard.
State the cost boundary you choose and use it consistently. Direct marginal costs may answer a model-selection question; an operational or business-case view may also include staff time and an allocation of shared platform expenses. Avoid counting the same shared expense twice. There is no universal labor rate or overhead allocation that applies to every organization.
Count attempts, retries, and failures
Attribute every attempt to the logical task that triggered it. Include billable retries, fallback calls, validation calls, and tool actions even when an earlier attempt failed or the final result was not accepted. A task that reaches its retry limit without passing the acceptance test adds cost to the numerator but not to the accepted-task denominator.
Do not assume that retries cost the same as the first call. They may carry a longer conversation history, use a different model, or trigger additional tools. Preserve usage for each attempt and price it using the model and rates that actually applied. Anthropic’s guidance, for example, describes pricing recorded token usage at the applicable input, output, and cache rates; other providers and deployments have their own rates and meters. Rates can change, so retain the dated price schedule or version used in the calculation. Anthropic’s cost guidance discusses pricing recorded usage and the limits of benchmark comparisons.
For provider-specific fallback behavior, Anthropic documents per-attempt billing and an API field, usage.iterations, that records attempt-level usage in its system. Treat that as platform-specific guidance, not a guarantee about every provider. Anthropic’s refusals and fallback documentation explains its behavior.
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Track enough detail to reconstruct the result
Assign each logical task an ID and retain a record of its full lifecycle. A useful record includes:
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- Task type, start and terminal status, and whether it passed the acceptance test.
- Each attempt’s provider, model, input and output usage, applicable rate basis, and charges.
- Retry reason, fallback route, tool or retrieval calls, and related costs.
- Infrastructure or data-service costs attributable to the workflow.
- Human review and correction time, reviewer role, and loaded hourly rate.
Instrumenting attempts separately matters when fallbacks use different models or prices. It also lets you distinguish a costly first attempt from spend concentrated in retries or unsuccessful paths.
Report cost with success and review burden
Cost per accepted task is the main outcome-based measure, but it is more useful when shown with supporting figures. For the same period, report total cost, task volume, accepted count, success rate, cost per attempt, and review burden. Calculate:
- Cost per attempt = fully loaded workflow cost ÷ total attempts.
- Success rate = accepted tasks ÷ total attempts.
- Review burden = review and correction minutes per attempted task and per accepted task, alongside the labor-rate assumption.
These measures help explain whether a result comes from expensive attempts, repeated attempts, substantial review, or a low acceptance rate. Include labor and fees for manual fallback or unresolved failures in total cost when they are part of the workflow, even though those cases do not count as accepted outcomes.
Example: calculate the denominator correctly
The AI Career Lab gives a one-month illustrative support-workflow example with 10,000 attempts: $6,000 in model and tool charges, $1,000 in retrieval and infrastructure, and $3,000 in required review. With 7,500 tickets resolved without reopening, the total is $10,000 and the cost is $10,000 ÷ 7,500, or about $1.33 per successful task. This is an example’s arithmetic, not an industry benchmark or a forecast for another workflow. The AI Career Lab guide provides the example.
Compare alternatives on the same work
Run candidate workflows on representative tasks using the same acceptance test, then compare cost alongside outcomes. A lower per-token price does not establish a lower cost per accepted result if it also brings more retries, review, or failures.
- Acceptance and quality: Share meeting the fixed test, plus error severity and any safety, fairness, or compliance requirements.
- Retry and fallback burden: Attempt counts, causes, fallback share, and spend on unsuccessful paths.
- Human effort: Review and correction time per attempt and accepted result, with the rate assumption.
- Latency and costly cases: Completion time and the cases where spend is concentrated.
Published benchmark results are specific to their workload and settings. CHAI’s Testing and Evaluation Framework presents a $0.228-per-task cost-of-pass benchmark from a 2025 supporting study, but says organizations should establish a local baseline outside a comparable setting and seek cost improvements without reducing safety, fairness, or compliant completion. It is not a universal target price. CHAI’s Testing and Evaluation Framework describes the metric and its context.
Anthropic’s 2026 platform guidance reports 88.6% solved at $0.54 per solved task versus 77.4% at $0.84 for a stated SWE-bench Pro subset, comparing Claude Fable 5.1 at low effort with Claude Sonnet 5 at default effort. The same guidance says two problems accounted for 43% of spend in a particular 20-problem WideSearch run. These results illustrate why outcomes and costly outliers matter, but they are specific to those models, settings, and benchmark tasks—not predictions for a business workflow. Anthropic’s guidance provides the benchmark examples.
Recalculate after material changes to the model, prompt, retry policy, tools, or acceptance standard. If the standard changes, mark the new measurement period clearly; a cost comparison is meaningful only when its success bar is comparable.
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