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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsA model’s price per token tells you what each unit costs—not what it costs to get useful work accepted. To compare models economically, run them on the same representative tasks, define what counts as acceptable before testing, and divide total measured inference spend by the number of tasks that pass. Report the acceptance rate and service performance beside that figure; a low cost per accepted result is not useful if too few results pass or the system responds too slowly.
What “cost per completed work” should mean
Use an operational definition of completion that fits the job. A task may count as accepted if its answer matches a key, its code passes specified tests, or a reviewer approves the result against a stated rubric. There is no universal acceptance test: what is good enough depends on the application. NVIDIA’s NIM LLM Benchmarking overview likewise says cost measurement should be based on reaching accuracy acceptable for the use case.
For an API-based comparison, calculate:
Inference spend per accepted completion = total measured inference spend ÷ number of accepted tasks
Also report completion rate = accepted tasks ÷ total attempts. The denominator in the first formula is tasks that passed your stated acceptance test—not merely responses returned by the model. If no task passes, report that the candidate produced no accepted work in the sample; do not invent a finite cost per accepted completion.
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This measure is not automatically the full cost of delivering work. If you include human review, corrections, rework, or incident costs, show those as separate components and explain how you counted them. Keep raw API inference charges separate from self-hosted infrastructure costs unless you clearly define and consistently apply a broader cost boundary.
Why token prices alone can mislead
Actual spend depends on the billable work consumed: input, cached input, reasoning, and output tokens, as well as retries and any fallback calls. Two models with identical rates can incur different costs when one uses more tokens. A lower rate can also be outweighed by longer responses, additional reasoning, repeated attempts, or a lower acceptance rate.
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Microsoft Foundry describes its cost benchmark as measuring actual cost on quality-benchmark datasets rather than estimating it from token pricing alone. Its methodology accounts for input, reasoning, and output token consumption and configured reasoning effort. Microsoft Foundry’s benchmark documentation also cautions that standardized benchmark conditions may not match a real workload.
Artificial Analysis provides another example of a task-based measure: its cost per task uses actual token consumption weighted across the tasks in its Intelligence Index. The organization notes that longer answers and reasoning usage raise task cost even when token rates are unchanged. That metric is specific to its benchmark workload and weighting, so it is not a prediction of what a particular production application will cost. See the Artificial Analysis Intelligence Index methodology.
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How to run a fair comparison
- Choose representative work. Build a sample from actual or realistically representative tasks, with enough variety to reflect the expected task mix. Give every candidate the same tasks and distribution.
- Set the acceptance rule in advance. Use deterministic checks where suitable, such as an answer key or test suite. For work that cannot be checked mechanically, use a defined rubric and, where practical, blinded human review. Decide how partial credit, invalid outputs, tool failures, and human corrections will count.
- Hold the workflow constant. Keep system instructions, context and retrieval, tools, output constraints, model settings, retry policy, provider or endpoint, and relevant region the same where possible. If the service is not deterministic, record its configuration and run repeated trials.
- Record actual usage and spend. Capture billable input, cached-input, reasoning, and output usage, along with retries and fallback calls. Match the usage to the applicable rates on the measurement date. For self-hosted systems, state the cost boundary rather than blending infrastructure and API charges without explanation.
- Calculate spend per accepted result and completion rate. Apply the formulas above to the full sample. Keep the number of attempts and the number accepted visible so readers can see how much work the model failed to complete to the required standard.
- Measure speed and capacity separately. Record end-to-end latency and time to first token, with relevant percentiles. For scaled services, measure throughput under stated concurrency and load; a single-request result does not establish performance under traffic.
- Publish the comparison conditions. Include the model and version, provider or endpoint, region, task set and mix, acceptance rule, settings, pricing basis and date, token accounting, cache treatment, retry policy, and measurement window.
Read cost alongside quality and service performance
A useful comparison keeps distinct dimensions visible rather than collapsing them into one score:
| Dimension | What to report | Why it matters |
|---|---|---|
| Accepted-work cost | Total measured inference spend divided by tasks passing the stated acceptance test | Reflects actual usage and failed attempts better than a rate card alone. |
| Completion quality | Acceptance rule and pass rate | A low average cost does not help if too few outputs meet the required standard. |
| Responsiveness | End-to-end latency, time to first token, and relevant percentiles | Interactive workflows may require both a quick start and a timely finish. |
| Capacity | Throughput at stated concurrency and load | Single-request speed does not show how a service behaves at production traffic levels. |
| Reproducibility | Task mix, prompts, settings, endpoint conditions, and dated pricing | Results are local to the workload and service configuration tested. |
| Operational fit | Relevant safety checks, data handling, availability, and deployment constraints | Cost and quality do not by themselves establish production suitability. |
These are separate decision axes, not interchangeable measures. A team can set minimum requirements for acceptance rate, latency, throughput, or operational controls, then compare cost among candidates that meet them. Microsoft Foundry separates quality, safety, performance, and cost benchmarks and recommends scenario-specific comparisons over relying only on a general index. NVIDIA also distinguishes performance benchmarking from load testing and notes that benchmarking tools do not always define terms consistently.
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Keep benchmark results in their proper scope
Public benchmarks can show how a particular measurement method works, but their results belong to their stated datasets and conditions. Artificial Analysis’s weighted Intelligence Index workload is not every team’s workload. Microsoft documents a performance setup of 14 days, 24 trials per day, and 336 runs; those figures describe Microsoft’s benchmark setup, not a universal sample-size rule.
Microsoft also describes standardized assumptions that can include synthetic prompts, fixed token ratios, a single region, and sequential requests. Real workloads may differ in prompt lengths, concurrency, geography, caching, and request patterns. Use public results as context, not as a substitute for testing the work your team needs done.
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Make results repeatable and useful later
Model versions, endpoint behavior, and provider rates change. Date every comparison and preserve enough detail for another person to understand or repeat it. At minimum, record:
- Model name and version, provider or endpoint, and region.
- Task sample, task mix, prompts, and acceptance criteria.
- Settings, tools, context, retrieval, retry policy, and any fallback behavior.
- Input, cached-input, reasoning, and output usage; treatment of failed calls and retries.
- Price schedule and measurement date, plus the cost boundary used.
- Accepted-task count, total attempts, latency measures, throughput conditions, and measurement window.
A dated result answers a bounded question: what did this model and endpoint cost to produce accepted work on this task set, under these conditions, at these prices? It does not establish a permanent ranking or a universal cost for other applications.
Quick Recap
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