Title: GDPval: Evaluating AI Model Performance on Real-World Economically Valuable Tasks
Primary area: datasets and benchmarks
Keywords: benchmark, real-world tasks, RL environments, model evaluation, reinforcement learning, AI impacts, dataset, evals, benchmarks, multi-modal, computer use, agents, long-horizon tasks, AI, artificial intelligence, ML, machine learning, deep learning, LLMs, language models

Abstract: We introduce GDPval, an evaluation assessing AI model capabilities on real-world 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 has been 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 expert workflows. 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.

TL;DR: We introduce GDPval, a benchmark assessing AI model capabilities on real-world economically valuable tasks.