Ultimate Guide - The Top LLMs for Document Q&A in 2026

What are LLMs for Document Q&A?

LLMs for document Q&A are specialized large language models designed to understand, analyze, and answer questions about documents. These models combine natural language processing with document comprehension capabilities, allowing them to parse complex document structures, extract relevant information, and provide accurate answers to user queries. They can handle various document formats including PDFs, images, charts, tables, and long-form text, making them essential tools for businesses, researchers, and organizations that need to efficiently process and query large volumes of document-based information.

Qwen2.5-VL-72B-Instruct

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.

Qwen2.5-VL-72B-Instruct: Premier Document Analysis Powerhouse

Qwen2.5-VL-72B-Instruct is a state-of-the-art vision-language model with 72 billion parameters, specifically designed for comprehensive document understanding and analysis. The model excels in analyzing texts, charts, and layouts within images, making it perfect for complex document Q&A tasks. With its 131K context length, it can process extensive documents while maintaining accuracy. The model demonstrates excellent performance across various benchmarks including image, video, and agent tasks, and supports structured outputs for scanned data like invoices and forms.

Pros

• Exceptional document and visual understanding with 72B parameters.

• 131K context length for processing extensive documents.

• Structured output generation for invoices and forms.

Cons

• Higher computational requirements due to large parameter size.

• More expensive than smaller alternatives.

Why We Love It

• It combines powerful vision-language capabilities with document-specific optimizations, making it the ideal choice for enterprise-grade document Q&A applications.

GLM-4.5V

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. 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.

GLM-4.5V: Efficient Multimodal Document Processor

GLM-4.5V is a cutting-edge vision-language model with 106B total parameters and 12B active parameters, utilizing a Mixture-of-Experts architecture for optimal efficiency. The model introduces innovations like 3D Rotated Positional Encoding (3D-RoPE), significantly enhancing its perception and reasoning abilities for document analysis. With its 'Thinking Mode' switch, users can choose between quick responses and deep reasoning, making it versatile for various document Q&A scenarios. The model achieves state-of-the-art performance on 41 multimodal benchmarks while maintaining cost-effectiveness.

Pros

• MoE architecture provides superior performance at lower cost.

• Flexible 'Thinking Mode' for balancing speed and accuracy.

• State-of-the-art performance on 41 multimodal benchmarks.

Cons

• Smaller context window compared to some alternatives.

• Requires understanding of thinking vs. non-thinking modes.

Why We Love It

• It offers the perfect balance of performance and efficiency for document Q&A, with innovative features like flexible reasoning modes that adapt to different use cases.

DeepSeek-R1

DeepSeek-R1-0528 is a reasoning model powered by reinforcement learning (RL) that addresses the issues of repetition and readability. Prior to RL, DeepSeek-R1 incorporated cold-start data to further optimize its reasoning performance. It achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks, and through carefully designed training methods, it has enhanced overall effectiveness.

DeepSeek-R1: Advanced Reasoning for Complex Documents

DeepSeek-R1 is a sophisticated reasoning model with 671B parameters using a Mixture-of-Experts architecture, specifically optimized for complex reasoning tasks. With its 164K context length, it can handle extensive document analysis while maintaining high accuracy. The model is powered by reinforcement learning and achieves performance comparable to OpenAI-o1 in reasoning tasks. Its advanced reasoning capabilities make it exceptionally suited for complex document Q&A scenarios that require deep understanding and logical inference.

Pros

• Massive 671B parameter model with advanced reasoning.

• 164K context length for comprehensive document analysis.

• Performance comparable to OpenAI-o1 in reasoning tasks.

Cons

• High computational requirements and cost.

• Longer inference times due to complex reasoning processes.

Why We Love It

• It delivers unmatched reasoning capabilities for the most complex document analysis tasks, making it ideal for research and enterprise applications requiring deep document understanding.

LLM Comparison for Document Q&A

In this table, we compare 2026's leading LLMs for document Q&A, each with unique strengths. For comprehensive visual document analysis, Qwen2.5-VL-72B-Instruct provides exceptional capabilities. For efficient multimodal processing, GLM-4.5V offers optimal performance-to-cost ratio. For complex reasoning tasks, DeepSeek-R1 delivers unparalleled analytical depth. This comparison helps you choose the right model for your specific document Q&A requirements.

Number | Model | Developer | Subtype | Pricing (SiliconFlow) | Core Strength
1 | Qwen2.5-VL-72B-Instruct | Qwen2.5 | Vision-Language Model | $0.59/ M Tokens | Comprehensive document analysis
2 | GLM-4.5V | zai | Vision-Language Model | $0.14-$0.86/ M Tokens | Efficient multimodal processing
3 | DeepSeek-R1 | deepseek-ai | Reasoning Model | $0.5-$2.18/ M Tokens | Advanced reasoning capabilities

Frequently Asked Questions

Which LLMs made it into our top three picks for document Q&A?

Our top three picks for 2026 are Qwen2.5-VL-72B-Instruct, GLM-4.5V, and DeepSeek-R1. Each of these models stood out for their exceptional document understanding capabilities, advanced reasoning abilities, and unique approaches to processing various document formats and answering complex questions.

What criteria did we use when ranking these document Q&A models?

We evaluated each model based on several key factors: performance on document understanding benchmarks, ability to process various document formats (PDFs, images, charts, tables), context length for handling long documents, reasoning capabilities for complex queries, accuracy in information extraction, and cost-effectiveness for deployment.

Why did we select these models as the best for document Q&A in 2026?

These models were chosen because they represent the cutting edge of document understanding technology. They demonstrate significant advancements in visual comprehension (Qwen2.5-VL-72B), efficient multimodal processing (GLM-4.5V), and complex reasoning (DeepSeek-R1), pushing the boundaries of what can be achieved in document Q&A applications.

Which models are the best for different types of document Q&A tasks?

Our analysis shows different leaders for specific needs. Qwen2.5-VL-72B-Instruct excels at comprehensive visual document analysis including charts and forms. GLM-4.5V is ideal for cost-effective multimodal document processing with flexible reasoning modes. DeepSeek-R1 is best for complex reasoning tasks requiring deep document understanding and logical inference.