With the rapid development of large language model (LLM) technology, a new star has emerged in the document parsing field—MonkeyOCR. This lightweight document parsing model has quickly become the focus of industry attention due to its outstanding performance and efficient processing speed.

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MonkeyOCR: Small Model, Big Power

MonkeyOCR demonstrates impressive performance with its lightweight architecture of only 3 billion parameters in English document parsing tasks. According to recent discussions on social media, MonkeyOCR surpasses heavyweight models like Gemini2.5Pro and Qwen2.5-VL-72B in multiple document parsing tasks, showing a significant average performance improvement. Particularly in complex document types, MonkeyOCR stands out, improving formula parsing by 15.0%, table parsing by 8.6%, and achieving an overall average improvement of 5.1% across nine document types. These results have made the industry take notice of the potential of lightweight models.

Parsing Speed: New Benchmark for Efficiency

Beyond performance breakthroughs, MonkeyOCR also leads in processing speed. Social media data shows that its parsing speed for multi-page documents reaches 0.84 pages per second, far exceeding MinerU’s 0.65 pages per second and Qwen2.5-VL-7B’s 0.12 pages per second. This speed advantage makes MonkeyOCR more competitive when handling large-scale document tasks, especially suitable for enterprise-level applications requiring quick responses.

Structure-Recognition-Relationship Triplet Paradigm

The core innovation of MonkeyOCR lies in its adoption of the "structure-recognition-relationship" triplet paradigm. This unique design allows the model to more accurately understand structured information in documents, achieving efficient parsing from text to tables and even complex formulas. Technical discussions on social media point out that this paradigm not only improves parsing accuracy but also significantly reduces computational resource requirements, making it possible for small and medium-sized enterprises to deploy AI document parsing solutions.

Industry Impact: Opening a New Chapter in Document Parsing

The emergence of MonkeyOCR not only showcases the immense potential of LLMs in the document parsing field but also sets a new technical benchmark for the industry. Its lightweight and efficient characteristics reduce the cost threshold for businesses applying AI technologies while providing more flexible options for academic research and commercial applications. AIbase believes that MonkeyOCR's success may encourage more developers to explore the application of lightweight models in vertical fields, potentially triggering a new wave of technological innovation in the document parsing domain.

Although MonkeyOCR currently excels mainly in English document parsing, discussions on social media are already looking forward to further optimizations in multilingual support and more complex scenarios. AIbase will continue to monitor MonkeyOCR's subsequent developments and its influence in the global AI ecosystem.

Paper: https://arxiv.org/abs/2506.05218