The AI Productivity Index (APEX)

Bertie Vidgen, Abby Fennelly, Evan Pinnix, Julien Benchek, Daniyal Khan, Zach Richards, Austin Bridges, Calix Huang, Kanishka Sahu, Abhishek Kottamasu, Bo Ma, Ben Hunsberger, Isaac Robinson, Akul Datta, Chirag Mahapatra, Dominic Barton, Cass R. Sunstein, Eric Topol, Brendan Foody and Osvald Nitski

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Abstract: We present an extended version of the AI Productivity Index (APEX-v1-extended), a benchmark for assessing whether frontier models are capable of performing economically valuable tasks in four jobs: investment banking associate, management consultant, big law associate, and primary care physician (MD). This technical report details the extensions to APEX-v1, including an increase in the held-out evaluation set from n = 50 to n = 100 cases per job (n = 400 total) and updates to the grading methodology. We present a new leaderboard, where GPT5 (Thinking = High) remains the top performing model with a score of 67.0%. APEX-v1-extended shows that frontier models still have substantial limitations when performing typical professional tasks. To support further research, we are open sourcing n = 25 non-benchmark example cases per role (n = 100 total) along with our evaluation harness.

Date: 2025-09, Revised 2025-12
New Economics Papers: this item is included in nep-ain, nep-cmp and nep-mac
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