When processing complex documents and charts, Optical Character Recognition (OCR) is merely the first step. The critical factor for enterprise AI is not the surface accuracy of text extraction, but the accuracy of logical reasoning and information synthesis.
According to recent benchmark evaluations, VaultSage (leveraging our proprietary orchestration technology primarily powered by Gemini) outperforms competing models in deep multimodal understanding:
The data confirms that VaultSage delivers good accuracy in complex data synthesis and cross-disciplinary reasoning compared to GPT-5.5, Claude Opus 4.7, and Claude Sonnet 4.6.
Benchmark Method | Capability / Description | VaultSage | Claude Sonnet 4.6 | Claude Opus 4.7 | GPT-5.5 |
CharXiv Reasoning | Information synthesis from complex charts | 84.2% | 65.1% | 82.1% | 84.1% |
MMMU-Pro | Multimodal understanding and masining | 83.6% | 70.5% | 75.2% | 81.2% |
CharXiv Reasoning: A benchmark utilizing real academic charts from arXiv papers. It tests a model's ability to perform complex data synthesis, trend estimation, and logical deductions rather than just reading raw labels.
MMMU-Pro: An advanced, multi-discipline benchmark consisting of university-level problems. It requires multi-step reasoning, formula handling, and deep cross-modal understanding to solve highly complex tasks.
This table is more interesting as it uses a comparative approach to show which LLM's OCR will win in real-world 'head-to-head battles'. The OCR used by vaultsage.ai is better equipped to achieve correct results in the real world, rather than just in test environments.
Model | Win Rate |
VaultSage AI | 76.60% |
Gemini 2.5 Pro | 71.20% |
Opus 4.5 (Low) | 69.20% |
Sonnet 4.5 | 46.90% |
GPT-5.1 (Medium) | 59.00% |
Nanonets2-3B | 33.20% |
GPT-5.1 (Low) | 54.30% |
GPT-5.2 (Medium) | 52.10% |
DeepSeek OCR | 20.50% |
olmOCR 2 | 28.90% |
(Refer to OCR Arena 2026 for other models)