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The AI Productivity Index (APEX)

Author

Listed:
  • Bertie Vidgen
  • Abby Fennelly
  • Evan Pinnix
  • Chirag Mahapatra
  • Zach Richards
  • Austin Bridges
  • Calix Huang
  • Ben Hunsberger
  • Fez Zafar
  • Brendan Foody
  • Dominic Barton
  • Cass R. Sunstein
  • Eric Topol
  • Osvald Nitski

Abstract

We introduce the first version of the AI Productivity Index (APEX), a benchmark for assessing whether frontier AI models can perform knowledge work with high economic value. APEX addresses one of the largest inefficiencies in AI research: outside of coding, benchmarks often fail to test economically relevant capabilities. APEX-v1.0 contains 200 test cases and covers four domains: investment banking, management consulting, law, and primary medical care. It was built in three steps. First, we sourced experts with top-tier experience e.g., investment bankers from Goldman Sachs. Second, experts created prompts that reflect high-value tasks in their day-to-day work. Third, experts created rubrics for evaluating model responses. We evaluate 23 frontier models on APEX-v1.0 using an LM judge. GPT 5 (Thinking = High) achieves the highest mean score (64.2%), followed by Grok 4 (61.3%) and Gemini 2.5 Flash (Thinking = On) (60.4%). Qwen 3 235B is the best performing open-source model and seventh best overall. There is a large gap between the performance of even the best models and human experts, highlighting the need for better measurement of models' ability to produce economically valuable work.

Suggested Citation

  • Bertie Vidgen & Abby Fennelly & Evan Pinnix & Chirag Mahapatra & Zach Richards & Austin Bridges & Calix Huang & Ben Hunsberger & Fez Zafar & Brendan Foody & Dominic Barton & Cass R. Sunstein & Eric To, 2025. "The AI Productivity Index (APEX)," Papers 2509.25721, arXiv.org, revised Oct 2025.
  • Handle: RePEc:arx:papers:2509.25721
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