Enterprise AI Analysis
Reasoning Models Ace the CFA Exams
Advanced AI reasoning models have demonstrated unprecedented performance on Chartered Financial Analyst (CFA) exams, surpassing previous LLM benchmarks and achieving near-perfect scores in foundational and application-based assessments. This marks a significant leap in AI's capability for complex financial analysis.
Our comprehensive evaluation across all three CFA levels reveals that state-of-the-art models like Gemini 3.0 Pro, GPT-5, and Gemini 2.5 Pro now consistently meet and exceed passing thresholds, indicating a new era for AI in professional financial tasks.
Executive Impact: Setting New Benchmarks in Financial AI
The latest generation of reasoning models has not only passed the rigorous CFA exams but has achieved top-tier performance across all levels, showcasing their readiness for high-stakes financial applications. This opens new avenues for AI integration in investment analysis and portfolio management.
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Gemini 3.0 Pro Level I
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GPT-5 Level II Performance
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Gemini 2.5 Pro Level III MCQ
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Gemini 3.0 Pro Level III CRQ
Deep Analysis & Enterprise Applications
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Robust Evaluation Framework
Our study utilizes a comprehensive dataset of 980 mock CFA exam questions across three levels, updated to the 2024 and 2025 curriculum. This ensures relevance and guards against data contamination, providing a valid reproduction baseline for evaluating advanced reasoning models.
The evaluation includes Zero-Shot (ZS) and Chain-of-Thought (CoT) prompting strategies, with CRQ responses graded by an automated LLM evaluator (o4-mini) using standardized rubrics. This meticulous approach allows for consistent comparison and a deep understanding of model capabilities.
Breakthrough Performance
The latest generation of AI reasoning models, including Gemini 3.0 Pro and GPT-5, has achieved unprecedented success on all three levels of the CFA exams. This marks a pivotal moment, as LLMs can now reliably pass these high-stakes financial assessments.
Specifically, Gemini 3.0 Pro achieved a record 97.6% on Level I, while GPT-5 led Level II with 94.3%. For Level III, Gemini 2.5 Pro scored 86.4% on MCQs, and Gemini 3.0 Pro achieved 92.0% on CRQs, demonstrating advanced synthesis capabilities.
Challenges and Future Directions
While impressive, the study acknowledges limitations. The Level III dataset relies on third-party mock exams, potentially differing in complexity from official exams. Future work should prioritize official materials for maximum representativeness.
Automated CRQ grading, while scalable, introduces potential measurement error due to LLMs' verbosity bias and occasional logical inconsistencies. Human-verified ground truth by CFA charterholders is crucial for future validation. The risk of training data contamination, though mitigated by proprietary and new datasets, remains an inherent challenge in LLM evaluation.
Common Error Patterns
Analysis of model errors reveals persistent challenges despite overall high performance. Common error types include Concept Misapplication (incorrectly selecting between related propositions), Rule Application Errors (misapplying ethical standards to case vignettes), Misinterpretation of Evidence (incorrectly flagging normal activities as problems), and Calculation Errors (using incorrect base values).
Notably, Ethical and Professional Standards remain a persistent challenge across all models, exhibiting the highest relative error rates on Level II (~17-21% for top models). This suggests areas for further refinement in AI's nuanced ethical reasoning.
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Reasoning Models Cleared All Three CFA Levels
| Model Generation | MCQ Performance with CoT | CRQ Performance with CoT |
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| Earlier LLMs (e.g., GPT-4, ChatGPT) |
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| State-of-the-Art Reasoning Models (e.g., Gemini 3.0 Pro, GPT-5) |
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| Model | Level III MCQ Accuracy | Level III CRQ Accuracy |
|---|---|---|
| Gemini 2.5 Pro |
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| Gemini 3.0 Pro |
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~17-21%
Highest Relative Error Rate on Ethical Standards (Level II)
CFA Exam Structure & Progression
Level I: Foundational Knowledge (MCQs)
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Level II: Application & Analysis (Vignettes)
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Level III: Synthesis & Portfolio Construction (CRQs)
Case Study: Automated Grading Challenges
When evaluating Constructed-Response Questions (CRQs) using automated LLM graders like o4-mini, a "verbosity bias" can emerge. This means longer, more comprehensive-sounding responses might be favored, even if they lack precise technical accuracy or subtle logical consistency compared to human expert judgment.
Impact: While scalable, automated scoring may not fully penalize nuanced errors, potentially inflating scores for verbose answers. This highlights the need for human-verified ground truth to fully validate AI performance on open-ended tasks.
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