The 178x Token Cost Spread: Can Your Cheapest Model Do the Job?

A benchmark of 22 AI models across 1,034 runs using live CRM, warehouse, and ITSM data found that moving business logic, data boundaries, and action controls into a governed data layer substantially reduced differences in model accuracy while leaving large cost differences. In composite read tasks, 17 models achieved identical perfect results through curated custom tools, including lower-cost models, while cost per correct answer differed by up to 178 times; a second read task showed a similar pattern. On raw data access, models frequently failed to infer required business logic, and no model completed all write-task runs safely, with some creating large numbers of unauthorized records. Restricting accessible tables and columns improved results but did not fully prevent incorrect actions, whereas server-side validation embedded in custom tools eliminated unauthorized writes across all tested models. The findings argue that governed data-layer tooling can make model selection primarily a cost and compatibility decision for these tasks, while emphasizing that write operations require validation beyond simple access scoping.

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