# AI Task Cost

> Source: https://aiwiki.ai/wiki/ai_task_cost
> Updated: 2026-11-03
> Categories: AI Benchmarks, AI Compute
> License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/) - attribute to "AI Wiki (aiwiki.ai)"
> Cite as: AI Wiki. "AI Task Cost." aiwiki.ai, 3 Nov 2026. https://aiwiki.ai/wiki/ai_task_cost
> From AI Wiki (https://aiwiki.ai), the free encyclopedia of artificial intelligence. Reuse freely with attribution.

**AI task cost** measures the spending associated with executing a defined task using an [AI system](https://aiwiki.ai/wiki/artificial_intelligence). It can be expressed per attempt, completed workflow or accepted output. The denominator matters: paying less for an attempt does not necessarily mean paying less for an output that meets the task's requirements. Benchmark protocols distinguish workload, quality and performance conditions when measuring systems.[1][2]

## Cost per attempt and cost per accepted task

For a batch of tasks, cost per attempt is total included spending divided by attempted tasks. Cost per accepted task is total included spending divided by outputs that satisfy a stated acceptance criterion. Failed attempts remain in the cost numerator. If no output meets the criterion, there is no finite observed cost per accepted task for that batch.

For example, consider an illustrative evaluation of 100 tasks with $20 of included spending. If 80 outputs are accepted, cost per attempt is $0.20 and cost per accepted task is $0.25. A second system that spends $15 but produces only 50 accepted outputs has a lower cost per attempt, $0.15, and a higher cost per accepted task, $0.30. These are hypothetical arithmetic examples, not measurements of named products.

The task and acceptance rule need to be fixed before the result can support a comparison. A coding task might require tests to pass; a document-processing task might require designated fields to be extracted correctly. A result must identify the actual evaluation used rather than equating a syntactically valid response with success.

## What belongs in the cost record?

A workload record can distinguish usage charges from the resources needed to operate the service. NVIDIA's inference-cost methodology connects performance measurements and system sizing with ownership or hosting costs.[3]

| Cost component | Required accounting choice |
| --- | --- |
| Input and output tokens | Use the charged token categories and the endpoint's rate |
| Cached input | State the cache policy, hit share and any additional recorded fees |
| Reasoning tokens | Include charged reasoning usage under the endpoint's rules |
| Retries and failed attempts | Retain their spending in the evaluation total |
| Tools | Include measured search, execution, retrieval or other paid operations |
| Hosted hardware | Include the billed configuration and paid idle time |
| Owned hardware | State depreciation or another cost-allocation method |
| Human review | State whether review time is measured and included |

An estimate that omits some components can still be useful if the scope is explicit. It should not be labeled a full deployment cost when it includes only token charges or GPU rental. [Inference economics](https://aiwiki.ai/wiki/inference_economics) covers the connection between hardware access and delivered work.

## Published benchmark costs

Artificial Analysis defines its cost per task as a weighted average over tasks in its Intelligence Index, using the token prices and token consumption associated with that workload. This is a cost of running the evaluation, not a claim that every attempted task succeeds. Its methodology also distinguishes native model tokens from standardized token measurements.[4]

That definition should remain attached to a quoted result. Changing the model version, reasoning setting, endpoint, cache treatment or evaluation suite can change the number. Two results called "cost per task" should not be combined unless their task populations and accounting rules are compatible.

## Latency, throughput and goodput

Task success can have a time requirement as well as a content requirement. GuideLLM defines goodput using requests that satisfy every configured latency objective. It also distinguishes that quantity from throughput, which includes completed requests that miss those objectives.[5]

Latency goodput does not itself establish semantic correctness. A fast response can be wrong; an accurate response can arrive after an application's deadline. A report should say whether its acceptance rule combines these conditions or measures them separately.

For a serving system, record whether throughput is measured during active processing or over the complete wall-clock evaluation. Applying an additional idle-time factor to a measurement that already includes idle time would count the loss twice. The same issue applies to a failure adjustment when failed requests are already present in the measured denominator.[3][5]

## A reproducible comparison record

MLPerf's policies make scenario definitions, consistent system configuration and reproducibility explicit parts of inference benchmarking.[1] A cost comparison can preserve the corresponding evidence in a compact record:

| Field | Example of what to record |
| --- | --- |
| Workload | Dataset or task definition and version |
| Model | Exact checkpoint or API model version |
| Serving configuration | Engine version, precision, hardware and GPU count |
| Inputs and outputs | Length distribution and tokenizer |
| Load | Concurrency, request rate and evaluation duration |
| Acceptance | Quality test, latency thresholds and retry limit |
| Observations | Total spend, attempts, accepted outputs and errors |
| Dates | Evaluation date, price effective date and review date |

These details allow another evaluator to identify a relevant difference rather than treating an aggregate score as a universal ranking. They also separate observed measurements from hypothetical scenarios. An [LLM ranking](https://aiwiki.ai/wiki/llm_rankings) can help select candidates, but the final cost comparison still depends on the intended workload.

## References

1. MLCommons. ["MLPerf Inference Rules."](https://github.com/mlcommons/inference_policies/blob/master/inference_rules.adoc) Scenario, reproducibility and system rules, accessed November 3, 2026.
2. vLLM Project. ["Benchmark Suites."](https://docs.vllm.ai/en/latest/benchmarking/) Documentation, accessed November 3, 2026.
3. Vinh Nguyen and Sergio Perez. ["LLM Inference Benchmarking: How Much Does Your LLM Inference Cost?"](https://developer.nvidia.com/blog/llm-inference-benchmarking-how-much-does-your-llm-inference-cost/) NVIDIA Technical Blog, June 18, 2025.
4. Artificial Analysis. ["Artificial Analysis Benchmarking Methodology."](https://artificialanalysis.ai/methodology) Cost per task and token definitions, accessed November 3, 2026.
5. vLLM Project. ["Goodput."](https://vllm-project.github.io/guidellm/main/reference/guidellm/schemas/benchmark/goodput/) GuideLLM documentation, accessed November 3, 2026.