What are Open Source LLMs for Document Screening?
Open source LLMs for document screening are specialized large language models designed to analyze, understand, and extract information from various document formats including text documents, PDFs, scanned images, tables, charts, and forms. These vision-language models combine advanced natural language processing with optical character recognition (OCR) and visual understanding capabilities to process complex document layouts, extract structured data, identify key information, and automate document review workflows. They enable developers and organizations to build intelligent document processing systems that can handle tasks like invoice processing, contract analysis, form extraction, compliance screening, and automated document classification with unprecedented accuracy and efficiency.
GLM-4.5V
GLM-4.5V is the latest generation vision-language model (VLM) released by Zhipu AI, built on a Mixture-of-Experts architecture with 106B total parameters and 12B active parameters. The model excels at processing diverse visual content including images, videos, and long documents, with innovations like 3D-RoPE significantly enhancing its perception and reasoning abilities. It features a 'Thinking Mode' switch for flexible responses and achieves state-of-the-art performance among open-source models of its scale on 41 public multimodal benchmarks.
GLM-4.5V: Advanced Multimodal Document Understanding
GLM-4.5V is the latest generation vision-language model (VLM) released by Zhipu AI. The model is built upon the flagship text model GLM-4.5-Air, which has 106B total parameters and 12B active parameters, and it utilizes a Mixture-of-Experts (MoE) architecture to achieve superior performance at a lower inference cost. Technically, GLM-4.5V follows the lineage of GLM-4.1V-Thinking and introduces innovations like 3D Rotated Positional Encoding (3D-RoPE), significantly enhancing its perception and reasoning abilities for 3D spatial relationships. Through optimization across pre-training, supervised fine-tuning, and reinforcement learning phases, the model is capable of processing diverse visual content such as images, videos, and long documents, achieving state-of-the-art performance among open-source models of its scale on 41 public multimodal benchmarks. Additionally, the model features a 'Thinking Mode' switch, allowing users to flexibly choose between quick responses and deep reasoning to balance efficiency and effectiveness. On SiliconFlow, pricing is $0.86/M output tokens and $0.14/M input tokens.
Pros
- Exceptional long document understanding capabilities with 66K context length.
- Innovative 3D-RoPE enhances spatial relationship perception.
- Thinking Mode enables deep reasoning for complex document analysis.
Cons
- Smaller context window compared to some newer models.
- May require expertise to optimize Thinking Mode usage.
Why We Love It
- It combines powerful document understanding with flexible reasoning modes, making it ideal for complex document screening tasks that require both speed and deep analysis.
Qwen2.5-VL-72B-Instruct
Qwen2.5-VL-72B-Instruct is a vision-language model in the Qwen2.5 series with 72B parameters and 131K context length. It demonstrates exceptional visual understanding capabilities, recognizing common objects while analyzing texts, charts, and layouts in images. The model functions as a visual agent capable of reasoning and dynamically directing tools, comprehends videos over 1 hour long, accurately localizes objects in images, and supports structured outputs for scanned data like invoices and forms.
Qwen2.5-VL-72B-Instruct: Comprehensive Document Processing Powerhouse
Qwen2.5-VL is a vision-language model in the Qwen2.5 series that shows significant enhancements in several aspects: it has strong visual understanding capabilities, recognizing common objects while analyzing texts, charts, and layouts in images; it functions as a visual agent capable of reasoning and dynamically directing tools; it can comprehend videos over 1 hour long and capture key events; it accurately localizes objects in images by generating bounding boxes or points; and it supports structured outputs for scanned data like invoices and forms. The model demonstrates excellent performance across various benchmarks including image, video, and agent tasks. With 72B parameters and 131K context length, it provides comprehensive document understanding and extraction capabilities. On SiliconFlow, pricing is $0.59/M output tokens and $0.59/M input tokens.
Pros
- Large 131K context window handles extensive documents.
- Superior text, chart, and layout analysis within documents.
- Structured output support for invoices, forms, and tables.
Cons
- Higher computational requirements due to 72B parameters.
- Higher pricing compared to smaller models.
Why We Love It
- It excels at extracting structured data from complex documents and supports comprehensive visual understanding, making it perfect for enterprise-scale document screening applications.
DeepSeek-VL2
DeepSeek-VL2 is a mixed-expert (MoE) vision-language model with 27B total parameters and only 4.5B active parameters, employing a sparse-activated MoE architecture for superior efficiency. The model excels in visual question answering, optical character recognition, document/table/chart understanding, and visual grounding. It demonstrates competitive or state-of-the-art performance using fewer active parameters than comparable models, making it highly cost-effective for document screening applications.
DeepSeek-VL2: Efficient Document Intelligence
DeepSeek-VL2 is a mixed-expert (MoE) vision-language model developed based on DeepSeekMoE-27B, employing a sparse-activated MoE architecture to achieve superior performance with only 4.5B active parameters. The model excels in various tasks including visual question answering, optical character recognition, document/table/chart understanding, and visual grounding. Compared to existing open-source dense models and MoE-based models, it demonstrates competitive or state-of-the-art performance using the same or fewer active parameters. This makes it exceptionally efficient for document screening tasks where OCR accuracy and document structure understanding are critical. The model's efficient architecture enables faster inference times while maintaining high accuracy across diverse document types. On SiliconFlow, pricing is $0.15/M output tokens and $0.15/M input tokens.
Pros
- Highly efficient with only 4.5B active parameters.
- Excellent OCR and document understanding capabilities.
- Superior document, table, and chart comprehension.
Cons
- Smaller 4K context window limits long document processing.
- May not handle extremely complex multi-page documents as effectively.
Why We Love It
- It delivers exceptional OCR and document understanding performance at a fraction of the computational cost, making it the ideal choice for high-volume document screening applications.
Document Screening LLM Comparison
In this table, we compare 2025's leading open source LLMs for document screening, each with unique strengths. GLM-4.5V offers flexible thinking modes for deep document analysis, Qwen2.5-VL-72B-Instruct provides comprehensive structured data extraction with the largest context window, and DeepSeek-VL2 delivers exceptional OCR and document understanding with remarkable efficiency. This side-by-side view helps you choose the right model for your specific document screening needs.
| Number | Model | Developer | Subtype | SiliconFlow Pricing | Core Strength |
|---|---|---|---|---|---|
| 1 | GLM-4.5V | zai | Vision-Language Model | $0.86/$0.14 per M tokens | Thinking Mode for complex analysis |
| 2 | Qwen2.5-VL-72B-Instruct | Qwen2.5 | Vision-Language Model | $0.59/$0.59 per M tokens | 131K context & structured outputs |
| 3 | DeepSeek-VL2 | deepseek-ai | Vision-Language Model | $0.15/$0.15 per M tokens | Superior OCR efficiency |
Frequently Asked Questions
Our top three picks for document screening in 2025 are GLM-4.5V, Qwen2.5-VL-72B-Instruct, and DeepSeek-VL2. Each of these vision-language models stood out for their exceptional document understanding capabilities, OCR performance, and ability to extract structured information from complex document formats including invoices, forms, tables, and charts.
For complex document analysis requiring deep reasoning and context understanding, GLM-4.5V with its Thinking Mode is ideal. For enterprise-scale document processing with structured data extraction from invoices, forms, and tables, Qwen2.5-VL-72B-Instruct with its 131K context window is the top choice. For high-volume, cost-effective document screening where OCR accuracy is critical, DeepSeek-VL2 offers the best balance of performance and efficiency with its sparse MoE architecture and competitive pricing on SiliconFlow.