Cost-of-Pass: An Economic Framework for Evaluating Language Models
Authors: name: Mehmet Hamza Erol, name: Batu El, name: Mirac Suzgun, name: Mert Yuksekgonul, name: James Zou

The widespread adoption of AI systems in the economy hinges on their ability
to generate economic value that outweighs their inference costs. Evaluating
this tradeoff requires metrics that account for both performance and costs. We
propose a framework grounded in production theory for evaluating language
models by combining accuracy and inference cost. We introduce "cost-of-pass",
the expected monetary cost of generating a correct solution. We then define the
"frontier cost-of-pass" as the minimum cost-of-pass achievable across available
models or the "human-expert, using the approximate cost of hiring an expert.
Our analysis reveals distinct economic insights. First, lightweight models are
most cost-effective for basic quantitative tasks, large models for
knowledge-intensive ones, and reasoning models for complex quantitative
problems, despite higher per-token costs. Second, tracking this frontier
cost-of-pass over the past year reveals significant progress, particularly for
complex quantitative tasks where the cost has roughly halved every few months.
Third, to trace key innovations driving this progress, we examine
counterfactual frontiers: estimates of cost-efficiency without specific model
classes. We find that innovations in lightweight, large, and reasoning models
have been essential for pushing the frontier in basic quantitative,
knowledge-intensive, and complex quantitative tasks, respectively. Finally, we
assess the cost-reductions afforded by common inference-time techniques like
majority voting and self-refinement, finding that their marginal accuracy gains
rarely justify their costs. Our findings underscore that complementary
model-level innovations are the primary drivers of cost-efficiency, and our
economic framework provides a principled tool for measuring this progress and
guiding deployment.