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

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Authors: Tejal Patwardhan, Rachel Dias, Elizabeth Proehl, Grace Kim, Michele Wang, Olivia Watkins, Sim´on Posada Fishman, Marwan Aljubeh, Phoebe Thacker, Laurance Fauconnet, Natalie S. Kim, Patrick Chao
+7 more Samuel Miserendino, Gildas Chabot, David Li, Michael Sharman, Alexandra Barr, Amelia Glaese, Jerry Tworek
Cited by: 6

Abstract

We introduce GDPval, a benchmark evaluating AI model capabilities on realworld economically valuable tasks. GDPval covers the majority of U.S. Bureau of Labor Statistics 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 at evals.openai.com to facilitate future research in understanding real-world model capabilities.

Fields of Study & Subjects

Explainable Artificial Intelligence (XAI) Ethics and Social Impacts of AI Machine Learning and Data Classification

Publication details

Journal / source
SuperIntelligence - Robotics - Safety & Alignment
Publisher
Carlson Research LLC
Volume
2
Issue
4
Publication date
21 January 2026