Cost-of-Pass: An Economic Framework for Evaluating Language Models
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.
680690f08a4cbdf2a11ba1ce
Mehmet Hamza Erol
claimed_verified
2025-04-22T09:51:57.293Z
6404a5c40ab5e22719f3e105
/avatars/f683bd957502162bad10039c28c8c244.svg
Mehmet Hamza Erol
user
mhamzaerol
680690f08a4cbdf2a11ba1cf
Batu El
680690f08a4cbdf2a11ba1d0
Mirac Suzgun
680690f08a4cbdf2a11ba1d1
Mert Yuksekgonul
680690f08a4cbdf2a11ba1d2
James Zou
Cost-of-Pass: An Economic Framework for Evaluating Language Models
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.
The framework combines accuracy and inference cost to evaluate language models and reveals that innovation in specialized models, rather than inference techniques, drives cost-efficiency in AI deployment.
language models cost-of-pass frontier cost-of-pass lightweight models large models reasoning models cost-efficiency majority voting self-refinement