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To measure AI model cost per completed task, add up the spend for every attempt in a representative workload—including failed attempts, retries, and fallback calls—then divide by the number of tasks that meet a defined acceptance test. Report that figure alongside success rate, workload coverage, quality, and latency; a cheap result on a small subset of solvable tasks is not evidence that a model can replace a more capable one.

Define what counts as a completed task

Choose a unit of work and an observable pass-or-fail condition before measuring cost. A task might be complete when its tests pass, a support ticket is closed, or a data operation returns the correct row count. A response being generated is not, by itself, proof of useful completion.

For workflows where partial completion has value, report that separately from full acceptance. Do not quietly count partial results as successes: that changes the denominator and makes comparisons misleading.

Set the cost boundary

For an API-cost figure, count every billable model request made for the task, including retries and fallback-model calls. Failed attempts remain in the cost total even though they do not count as accepted completions.

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A fully loaded operating-cost measure can also include tools and retrieval, evaluator or guardrail model calls, material infrastructure, and required human review or correction. Label the scope clearly: API spend and fully loaded workflow cost answer different questions.

Calculate token-based request cost

For providers that bill by token category, calculate the cost of each request using its applicable rates, then sum the requests across the complete task workflow. Anthropic’s platform guidance, for example, distinguishes uncached input, cache writes and reads, and output; its pricing documentation describes the relevant categories, while its Usage and Cost API documentation covers aggregate usage reporting. Rates and billing rules vary by model and can change, so use the current provider schedule and actual usage records rather than treating an illustrative calculation as a quote.

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Run a representative comparison

  1. Build the task set. Sample real or realistic production work in proportions that resemble actual traffic. Include the task types and difficulty levels that matter to the intended use.
  2. Hold the evaluation constant. Compare candidate models on the same tasks, acceptance checks, routing rules, and relevant quality threshold. Record model versions and pricing assumptions.
  3. Repeat stochastic tasks. Run multiple trials where outputs or tool choices can vary. Keep failure reasons and traces so that a single lucky run does not stand in for typical behavior.
  4. Segment when needed. Break out results by task type or difficulty if different task mixes would distort the overall average. Keep a blended figure as well if it represents the real workload.

Use executable checks where possible—for example, whether tests pass or whether the expected state changed. NVIDIA’s agent evaluation guidance describes verification approaches; if an LLM judge is used, validate its scores against human ratings on a sample.

Calculate cost per accepted completion

For a defined cohort, use:

Cost per accepted completion = total spend across all attempts ÷ number of accepted completions

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Also report the total task count and success rate, so readers can see how often the model met the acceptance condition. Average cost per attempt divided by success rate can approximate cost per accepted completion only when both figures come from the same representative run population and retry policy.

Keep the unit cost with the other measures that explain its meaning:

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  • Success rate and coverage: how often the model completes the whole evaluated workload, not only the subset it can solve.
  • Quality and verification: whether the acceptance check catches unacceptable results and whether partial outcomes are distinguished.
  • Consistency: how much results vary across repeat trials.
  • Latency and work performed: elapsed time, steps per success, and relevant tool calls. Count tool calls separately from conversational turns when that distinction matters.
  • Cost scope and workload mix: whether the number is API-only or fully loaded, and which task mix produced it.
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Interpret the result without overclaiming

Cost per attempt and token price are not substitutes for cost per accepted completion. A low-priced attempt may require more requests, retries, fallbacks, or human correction. On the other hand, the lowest cost per success does not show that the model covers enough of the workload to be a viable replacement. Compare coverage and quality next to unit economics.

A July 2026 benchmark by Arize AI and Fireworks illustrates why the denominator matters. It reported 2,400 runs across 40 Terminal-Bench tasks, 10 models, and six trials per task-model combination, with $626 in API spend for that setup. The authors reported a roughly ±6 percentage-point 95% confidence interval around pass rate: sufficient, in their study, to rank cost per success with confidence, but not to reliably distinguish close neighboring models. Their figures describe that benchmark, its harness, model versions, and study-time prices—not a general production estimate or current price quote.

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In that same study, gpt-oss-120b had a reported 33% pass rate and $0.054 cost per successful task, while GPT-5.5 had a 67% pass rate and $0.636 per successful task. Those results show the trade-off in that particular task set: a lower unit cost among successes can come with substantially lower coverage. They are not universal model rankings. See the Arize AI and Fireworks benchmark report for its conditions and results.

Use failure traces to improve the workflow

Inspect traces and failure reasons for expensive loops, unnecessary retries, malformed responses, and escalation causes. Change routing or workflow design only when you can rerun the same evaluation with the same acceptance checks; otherwise, an apparent cost improvement may simply reflect a different task mix or a looser success threshold. Observability software can help organize traces, but it is optional—the measurement method works with any setup that records task outcomes and costs.

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