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Reasoning Models Ace the CFA Exams

Author

Listed:
  • Jaisal Patel
  • Yunzhe Chen
  • Kaiwen He
  • Keyi Wang
  • David Li
  • Kairong Xiao
  • Xiao-Yang Liu

Abstract

Previous research has reported that large language models (LLMs) demonstrate poor performance on the Chartered Financial Analyst (CFA) exams. However, recent reasoning models have achieved strong results on graduate-level academic and professional examinations across various disciplines. In this paper, we evaluate state-of-the-art reasoning models on a set of mock CFA exams consisting of 980 questions across three Level I exams, two Level II exams, and three Level III exams. Using the same pass/fail criteria from prior studies, we find that most models clear all three levels. The models that pass, ordered by overall performance, are Gemini 3.0 Pro, Gemini 2.5 Pro, GPT-5, Grok 4, Claude Opus 4.1, and DeepSeek-V3.1. Specifically, Gemini 3.0 Pro achieves a record score of 97.6% on Level I. Performance is also strong on Level II, led by GPT-5 at 94.3%. On Level III, Gemini 2.5 Pro attains the highest score with 86.4% on multiple-choice questions while Gemini 3.0 Pro achieves 92.0% on constructed-response questions.

Suggested Citation

  • Jaisal Patel & Yunzhe Chen & Kaiwen He & Keyi Wang & David Li & Kairong Xiao & Xiao-Yang Liu, 2025. "Reasoning Models Ace the CFA Exams," Papers 2512.08270, arXiv.org.
    • Handle: RePEc:arx:papers:2512.08270
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    References listed on IDEAS

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    1. Xiao-Yang Liu & Guoxuan Wang & Hongyang Yang & Daochen Zha, 2023. "FinGPT: Democratizing Internet-scale Data for Financial Large Language Models," Papers 2307.10485, arXiv.org, revised Nov 2023.
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