NLP

ToltIQ Study Compares Top AI Models for PE Due Diligence

ToltIQ Study Compares Top AI Models for PE Due Diligence

Comprehensive evaluation of leading AI models demonstrates significant performance differences in financial analysis capabilities

ToltIQ, the leading AI-powered platform for private markets due diligence, today released findings from a comprehensive study evaluating the performance of three major large language models (LLMs) for private equity workflows. The research is part of ToltIQ’s ongoing rigorous assessment process for evaluating LLMs as models rapidly evolve and new releases become available. The study benchmarked Anthropic’s Claude 4 Sonnet and OpenAI’s ChatGPT 4.1, both currently available to ToltIQ clients as well as Google’s Gemini 2.5 Pro Preview that is currently being evaluated for potential availability through the ToltIQ platform.

Key Findings:

The study revealed distinct performance characteristics across models, with each demonstrating particular strengths for different use cases:

  • Claude 4 Sonnet excelled in analytical depth and reasoning, providing detailed financial analysis with precise data usage and logical reasoning chains. While generation times were longer, it delivered higher information density with more concise outputs.
  • ChatGPT 4.1 demonstrated the fastest response times with reliable accuracy and well-structured outputs, making it highly effective for scenarios requiring rapid, comprehensive information gathering with clear presentation.
  • Gemini 2.5 Pro Preview showed the highest source utilization rates and broadest document coverage, though it struggled with relevance and specificity in some scenarios, often producing verbose responses.

Across the comprehensive evaluation framework, Claude 4 Sonnet achieved the highest overall qualitative score (8.02/10), followed by ChatGPT 4.1 (6.62/10) and Gemini 2.5 Pro Preview (5.81/10). The research measured models across quantitative metrics including response time, source utilization, and citation accuracy, alongside qualitative assessments of relevance, accuracy, reasoning, problem-solving capability, industry knowledge, and overall usability.

“This rigorous evaluation directly informs our platform’s model selection and validates our commitment to offering investment professionals choice among the most capable AI tools available,” said Ed Brandman, CEO and Founder of ToltIQ. “Different models excel in different scenarios, and our clients benefit from having access to multiple options. ChatGPT 4.1’s speed and reliability make it excellent for rapid information gathering, while Claude 4 Sonnet’s analytical depth serves complex reasoning tasks. We continue evaluating new models like Gemini to ensure our platform provides the best tools for each specific use case.”

Alfast Bermudez, AI Researcher and study author, noted: “The results demonstrate that raw citation volume doesn’t translate to better due diligence outcomes. Claude’s ability to synthesize information, identify data limitations, and provide structured analytical reasoning makes it function almost like a skilled research analyst. This capability is crucial for private equity professionals who need to quickly assess complex investment opportunities.”

Methodology

The study evaluated model performance across 16 use cases representing core due diligence scenarios, including financial analysis, market assessment, ESG evaluation, and product economics. Each model analyzed a comprehensive virtual deal room (VDR) using Amazon.com, Inc. as a representative case study. Amazon was selected due to its extensive publicly available documentation, which provides the depth and complexity of materials that challenge models to identify relevant information from large data sets—similar to what private markets professionals encounter in confidential deal processes.

The evaluation methodology utilized ToltIQ’s proprietary platform architecture, enabling models to process large document sets representative of real-world due diligence scenarios. Performance was measured through both automated quantitative analysis and human expert evaluation across industry-relevant criteria.

The full study is available at www.toltiq.com/insights.

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