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The AI Productivity Index (APEX)

Author

Listed:
  • Bertie Vidgen
  • Abby Fennelly
  • Evan Pinnix
  • Julien Benchek
  • Daniyal Khan
  • Zach Richards
  • Austin Bridges
  • Calix Huang
  • Ben Hunsberger
  • Isaac Robinson
  • Akul Datta
  • Chirag Mahapatra
  • Dominic Barton
  • Cass R. Sunstein
  • Eric Topol
  • Brendan Foody
  • Osvald Nitski

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.

Suggested Citation

  • Bertie Vidgen & Abby Fennelly & Evan Pinnix & Julien Benchek & Daniyal Khan & Zach Richards & Austin Bridges & Calix Huang & Ben Hunsberger & Isaac Robinson & Akul Datta & Chirag Mahapatra & Dominic B, 2025. "The AI Productivity Index (APEX)," Papers 2509.25721, arXiv.org, revised Dec 2025.
    • Handle: RePEc:arx:papers:2509.25721
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    File URL: http://arxiv.org/pdf/2509.25721
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