What are Multimodal Models for Document Analysis?
Multimodal models for document analysis are specialized Vision-Language Models (VLMs) that combine natural language processing with computer vision to understand and analyze complex documents. These models can process diverse visual content including text, charts, tables, diagrams, and layouts within documents, extracting structured information and providing intelligent insights. They excel at tasks like invoice processing, form understanding, chart analysis, and converting visual documents into actionable data, making them essential tools for businesses seeking to automate document workflows and enhance information extraction capabilities.
GLM-4.5V
GLM-4.5V is the latest generation vision-language model released by Zhipu AI, featuring 106B total parameters and 12B active parameters with a Mixture-of-Experts (MoE) architecture. The model excels at processing diverse visual content including long documents, achieving state-of-the-art performance on 41 public multimodal benchmarks. It features innovative 3D Rotated Positional Encoding (3D-RoPE) and a 'Thinking Mode' switch for flexible reasoning approaches.
GLM-4.5V: Premium Document Analysis Powerhouse
GLM-4.5V represents the cutting edge of document analysis with its 106B parameter MoE architecture delivering superior performance at lower inference costs. The model processes complex documents, images, videos, and long-form content with exceptional accuracy. Its 3D-RoPE innovation enhances spatial relationship understanding, crucial for document layout analysis. The flexible 'Thinking Mode' allows users to balance speed and deep reasoning, making it ideal for both quick document processing and complex analytical tasks requiring detailed comprehension.
Pros
- State-of-the-art performance on 41 multimodal benchmarks.
- MoE architecture provides superior efficiency and cost-effectiveness.
- Advanced 3D spatial relationship understanding for complex layouts.
Cons
- Higher output pricing due to advanced capabilities.
- Large model size may require significant computational resources.
Why We Love It
- It delivers unmatched document analysis capabilities with flexible reasoning modes, making it perfect for enterprise-grade document processing workflows.
GLM-4.1V-9B-Thinking
GLM-4.1V-9B-Thinking is an open-source Vision-Language Model jointly released by Zhipu AI and Tsinghua University's KEG lab. This 9B-parameter model introduces a 'thinking paradigm' with Reinforcement Learning and achieves performance comparable to much larger 72B models. It excels in long document understanding and can handle images up to 4K resolution with arbitrary aspect ratios.
GLM-4.1V-9B-Thinking: Efficient Document Reasoning Champion
GLM-4.1V-9B-Thinking revolutionizes document analysis by delivering exceptional performance in a compact 9B-parameter package. The model's innovative 'thinking paradigm' enhanced through Reinforcement Learning with Curriculum Sampling (RLCS) enables sophisticated reasoning on complex documents. Despite its smaller size, it matches or surpasses larger 72B models on 18 benchmarks, making it ideal for long document understanding, STEM problem-solving, and high-resolution document processing up to 4K with flexible aspect ratios.
Pros
- Outstanding performance-to-size ratio competing with 72B models.
- Advanced 'thinking paradigm' for complex document reasoning.
- Supports 4K resolution documents with arbitrary aspect ratios.
Cons
- Smaller parameter count than premium alternatives.
- May require fine-tuning for highly specialized document types.
Why We Love It
- It offers exceptional document analysis performance in a compact, cost-effective package that rivals much larger models through innovative thinking paradigms.
Qwen2.5-VL-32B-Instruct
Qwen2.5-VL-32B-Instruct is a multimodal large language model from the Qwen team, highly capable of analyzing texts, charts, icons, graphics, and layouts within images. It acts as a visual agent with tool reasoning capabilities and can accurately localize objects, generate structured outputs for invoices and tables, with enhanced mathematical and problem-solving abilities through reinforcement learning.
Qwen2.5-VL-32B-Instruct: Structured Document Processing Expert
Qwen2.5-VL-32B-Instruct specializes in comprehensive document analysis with exceptional capabilities in text recognition, chart interpretation, and layout understanding. The model excels at generating structured outputs from complex documents like invoices and tables, making it invaluable for business process automation. Enhanced through reinforcement learning, it offers superior mathematical reasoning and problem-solving abilities, while its visual agent capabilities enable dynamic tool interaction and precise object localization within documents.
Pros
- Excellent at structured output generation for invoices and tables.
- Advanced chart, icon, and graphics analysis capabilities.
- Visual agent functionality with tool reasoning.
Cons
- Shorter context length compared to some alternatives.
- Equal input and output pricing may be less cost-effective for read-heavy tasks.
Why We Love It
- It excels at converting complex visual documents into structured, actionable data, making it perfect for business automation and document processing workflows.
Document Analysis Model Comparison
In this table, we compare 2025's leading multimodal models for document analysis, each with unique strengths for processing complex visual documents. GLM-4.5V offers premium capabilities with flexible reasoning modes, GLM-4.1V-9B-Thinking provides exceptional efficiency and thinking paradigms, while Qwen2.5-VL-32B-Instruct specializes in structured output generation. This comparison helps you choose the right model for your document analysis requirements and budget.
| Number | Model | Developer | Subtype | SiliconFlow Pricing | Core Strength |
|---|---|---|---|---|---|
| 1 | GLM-4.5V | Zhipu AI | Vision-Language Model | $0.14-$0.86/M Tokens | Premium multimodal performance |
| 2 | GLM-4.1V-9B-Thinking | THUDM | Vision-Language Model | $0.035-$0.14/M Tokens | Efficient thinking paradigms |
| 3 | Qwen2.5-VL-32B-Instruct | Qwen2.5 | Vision-Language Model | $0.27/M Tokens | Structured output generation |
Frequently Asked Questions
Our top three picks for document analysis in 2025 are GLM-4.5V, GLM-4.1V-9B-Thinking, and Qwen2.5-VL-32B-Instruct. Each model excelled in different aspects of document processing, from premium multimodal performance to efficient reasoning and structured output generation.
GLM-4.5V is best for comprehensive, high-accuracy document analysis requiring flexible reasoning. GLM-4.1V-9B-Thinking excels at cost-effective long document processing with advanced thinking capabilities. Qwen2.5-VL-32B-Instruct is ideal for structured output generation from invoices, tables, and forms requiring precise data extraction.