Poster Thu, Apr 23, 2026 • 11:15 AM – 1:45 PM PDT Pavilion 3 P3-#1819

GDPval: Evaluating AI Model Performance on Real-World Economically Valuable Tasks

Tejal Patwardhan ⋅ Rachel Dias ⋅ Elizabeth Proehl ⋅ Grace Kim ⋅ Michele Wang ⋅ Olivia Watkins ⋅ Simon Fishman ⋅ Marwan Aljubeh ⋅ Phoebe Thacker ⋅ Laurance Fauconnet ⋅ Natalie Kim ⋅ Samuel Miserendino ⋅ Gildas Chabot ⋅ David Li ⋅ Patrick Chao ⋅ Michael Sharman ⋅ Alexandra Barr ⋅ Amelia Glaese ⋅ Jerry Tworek

Abstract

We introduce GDPval, a benchmark evaluating AI model capabilities on real-world economically valuable knowledge-work tasks. GDPval covers the majority of Department of Labor O*NET Work Activities for 44 occupations across the top 9 sectors contributing to U.S. GDP (Gross Domestic Product). Tasks are constructed from the representative work of industry professionals with an average of 14 years of experience. We find that frontier model performance on GDPval is improving roughly linearly over time, and that the current best frontier models are approaching industry experts in deliverable quality. We analyze the potential for frontier models, when paired with human oversight, to perform GDPval tasks cheaper and faster than unaided experts. We also demonstrate that increased reasoning effort, increased task context, and increased scaffolding improves model performance on GDPval. Finally, we open-source a gold subset of 220 tasks and provide a public automated grading service to facilitate future research in understanding real-world model capabilities.