Price per million tokens is easy to compare and difficult to use as a complete business decision. A model can be 30 percent cheaper on paper and still cost more when a sales coordinator must correct model numbers, units and certificate names in every translated product sheet.

An AWS benchmarking approach published on September 11 shifts the unit of comparison from token price to cost per correct answer, total agent trajectory cost and rubric-graded deliverable quality. That is a better fit for international sales work, where a useful output is rarely a single generation.

An inquiry workflow may read an email, identify the account, search a catalog, extract technical requirements, draft a response and wait for approval. A failure in any step can trigger another model call, another tool call or human rework. The first response can be inexpensive while the accepted deliverable is not.

Benchmark the work product, not a demo prompt

Choose three to five recurring tasks: product-page summaries, RFQ field extraction, quotation-gap lists, exhibition follow-up drafts and multilingual specification checks. Build a small set of de-identified real examples for each. Then define acceptance criteria that reflect the commercial risk: model identifiers remain unchanged, units are preserved, missing data is not guessed, claims cite approved material and price or delivery commitments are flagged for a person.

Record the total resources required to reach acceptance. Include input and output tokens, retrieval, tool calls, failed runs, review minutes and corrections caused downstream. A model with a higher call price but a strong first-pass acceptance rate can be less expensive than a cheaper model that needs three rounds of prompting.

Agent evaluation also needs the trajectory, not only the final paragraph. An agent may arrive at the right answer after opening the wrong customer record, reading an obsolete catalog or calling an unnecessary external tool. That is not a stable success. Keep the sequence of evidence, tool choices, decisions and stopping conditions so reviewers can distinguish a reliable process from a lucky outcome.

Route tasks instead of declaring one universal winner

Low-cost, low-latency models may be appropriate for simple classification and format conversion. Cross-document checks, complex product matching or multi-step tool use may justify a model with a higher acceptance rate. The useful architecture routes tasks according to risk and difficulty rather than assigning every job to the strongest or cheapest model.

A monthly scorecard can stay compact: first-pass acceptance, average retries, human review time and blended cost per accepted deliverable. If a new model raises token spend but sharply reduces review time, the same table will show the net effect. If a release does not improve the company's own test set, a new model name is not a reason to migrate.

The rubric matters as much as the cost calculation. A fluent email that changes a part number should fail. A concise answer that identifies three missing quotation inputs can pass. Quality should be defined by what the next person can safely do with the output, not by writing style alone.

Model procurement therefore becomes a workload question: which combination produces an accepted result most consistently under our documents, languages and rules? Once cost is attached to the approved outcome, AI selection moves from a vendor price sheet to an operating decision.

Refresh part of the evaluation set each month without discarding every long-term benchmark. Stable cases reveal regressions across model upgrades; recent products, buyer language and observed defects keep the test relevant. A benchmark made only of familiar, easy prompts can improve while live work gets worse.

Human review time can be valued in the calculation, but high-risk approval should not be treated as waste. Prices, certification claims, contracts and delivery commitments still require accountable confirmation. The economic goal is to remove repetitive correction so reviewers can focus on the decisions that carry commercial responsibility.

Teams should also publish uncertainty around the score. A model that wins by a small margin on twenty examples may not be meaningfully better. Repeating the run, inspecting failure types and testing across languages prevents a narrow average from becoming a company-wide purchasing rule.

Sources

Amazon Web Services, Beyond the price per token: Choosing the right OpenAI model on Amazon Bedrock for your workload, September 11, 2026.