What are Small Models for Document + Image Q&A?
Small models for document and image Q&A are compact vision-language models specialized in understanding and answering questions about visual content, including documents, charts, diagrams, and images. These efficient models combine visual comprehension with natural language processing to extract information, analyze layouts, interpret text within images, and provide accurate answers to user queries. With parameter counts between 7B-9B, they offer an optimal balance between performance and resource efficiency, making them ideal for deployment in resource-constrained environments while still delivering powerful multimodal reasoning capabilities for document understanding, visual question answering, and intelligent information extraction.
Qwen2.5-VL-7B-Instruct
Qwen2.5-VL is a new member of the Qwen series, equipped with powerful visual comprehension capabilities. It can analyze text, charts, and layouts within images, understand long videos, and capture events. It is capable of reasoning, manipulating tools, supporting multi-format object localization, and generating structured outputs. The model has been optimized for dynamic resolution and frame rate training in video understanding, and has improved the efficiency of the visual encoder.
Qwen2.5-VL-7B-Instruct: Powerful Visual Comprehension for Documents
Qwen2.5-VL-7B-Instruct is a compact yet powerful vision-language model from the Qwen series with 7 billion parameters. It excels at analyzing text, charts, and complex layouts within images, making it ideal for document Q&A applications. The model can interpret structured content, extract information from tables and diagrams, and provide accurate answers to visual queries. With an optimized visual encoder and support for 33K context length, it efficiently processes long documents and multi-page content. The model's ability to handle multi-format object localization and generate structured outputs makes it particularly effective for enterprise document processing and visual question answering tasks. SiliconFlow offers this model at $0.05 per million tokens for both input and output.
Pros
- Excellent text, chart, and layout analysis capabilities.
- Optimized visual encoder for efficient processing.
- Supports 33K context length for long documents.
Cons
- Smaller parameter count compared to larger VLMs.
- May require fine-tuning for highly specialized domains.
Why We Love It
- It delivers exceptional document understanding and visual comprehension in a compact 7B parameter model, perfect for efficient document Q&A deployment.
GLM-4.1V-9B-Thinking
GLM-4.1V-9B-Thinking is an open-source Vision-Language Model designed to advance general-purpose multimodal reasoning. It introduces a 'thinking paradigm' and leverages Reinforcement Learning with Curriculum Sampling to significantly enhance capabilities in complex tasks. The model achieves state-of-the-art performance among similar-sized models and excels in STEM problem-solving, video understanding, and long document understanding, handling images with resolutions up to 4K.
GLM-4.1V-9B-Thinking: Advanced Multimodal Reasoning for Complex Documents
GLM-4.1V-9B-Thinking is a breakthrough vision-language model jointly released by Zhipu AI and Tsinghua University's KEG lab, featuring 9 billion parameters and a unique 'thinking paradigm' for enhanced reasoning. This model excels at complex document understanding, STEM problem-solving within images, and long-form document analysis with its 66K context window. It can handle high-resolution images up to 4K with arbitrary aspect ratios, making it ideal for processing detailed documents, technical diagrams, and multi-page PDFs. The model's Reinforcement Learning with Curriculum Sampling (RLCS) training enables it to perform sophisticated reasoning over visual content, answering complex questions that require multi-step logic and visual comprehension. On SiliconFlow, it's priced at $0.035 per million input tokens and $0.14 per million output tokens.
Pros
- Advanced 'thinking paradigm' for complex reasoning.
- Supports 66K context length for extensive documents.
- Handles 4K resolution images with arbitrary aspect ratios.
Cons
- Higher output pricing at $0.14/M tokens on SiliconFlow.
- More computationally intensive than simpler models.
Why We Love It
- It brings enterprise-grade multimodal reasoning to a compact 9B model, excelling at complex document Q&A with advanced thinking capabilities.
GLM-4-9B-0414
GLM-4-9B-0414 is a small-sized model in the GLM series with 9 billion parameters. Despite its smaller scale, it demonstrates excellent capabilities in code generation, web design, SVG graphics generation, and search-based writing tasks. The model supports function calling features, allowing it to invoke external tools to extend its range of capabilities, and shows a good balance between efficiency and effectiveness in resource-constrained scenarios.
GLM-4-9B-0414: Efficient Multimodal Processing with Tool Integration
GLM-4-9B-0414 is a versatile 9 billion parameter model from the GLM series that offers excellent document understanding and question answering capabilities while maintaining lightweight deployment. While primarily known for code generation and web design, its multimodal comprehension makes it effective for document Q&A tasks, especially when combined with its function calling capabilities. The model can invoke external tools to enhance its document processing abilities, such as OCR engines or specialized parsers. With 33K context length support and competitive performance benchmarks, GLM-4-9B-0414 provides a cost-effective solution for organizations needing efficient document Q&A without the overhead of larger models. SiliconFlow offers this model at $0.086 per million tokens for both input and output.
Pros
- Function calling for extended tool integration.
- Excellent efficiency in resource-constrained scenarios.
- Supports 33K context length for long documents.
Cons
- Less specialized in vision tasks compared to dedicated VLMs.
- May not handle high-resolution images as effectively.
Why We Love It
- It provides a balanced, efficient solution for document Q&A with unique function calling capabilities to extend its reach through external tools.
Small Model Comparison for Document + Image Q&A
In this table, we compare 2025's leading small models for document and image Q&A, each with unique strengths. Qwen2.5-VL-7B-Instruct offers powerful visual comprehension at the lowest parameter count. GLM-4.1V-9B-Thinking provides advanced reasoning capabilities with extended context and 4K image support. GLM-4-9B-0414 delivers efficiency with tool integration. This side-by-side view helps you choose the right model for your specific document understanding and visual Q&A requirements.
| Number | Model | Developer | Subtype | SiliconFlow Pricing | Core Strength |
|---|---|---|---|---|---|
| 1 | Qwen2.5-VL-7B-Instruct | Qwen | Vision-Language Model | $0.05/M tokens | Document & chart analysis |
| 2 | GLM-4.1V-9B-Thinking | THUDM | Vision-Language Model | $0.035-$0.14/M tokens | Advanced multimodal reasoning |
| 3 | GLM-4-9B-0414 | THUDM | Multimodal Chat Model | $0.086/M tokens | Function calling & efficiency |
Frequently Asked Questions
Our top three picks for 2025 are Qwen2.5-VL-7B-Instruct, GLM-4.1V-9B-Thinking, and GLM-4-9B-0414. Each of these compact models (7B-9B parameters) stood out for their exceptional document understanding, visual comprehension, and efficient performance in answering questions about documents and images while maintaining cost-effectiveness and deployment flexibility.
For high-resolution document processing, GLM-4.1V-9B-Thinking is the top choice, capable of handling images up to 4K resolution with arbitrary aspect ratios and featuring a 66K context window for extensive documents. For optimized layout and chart analysis with excellent cost-effectiveness, Qwen2.5-VL-7B-Instruct is ideal, offering powerful visual comprehension at just $0.05 per million tokens on SiliconFlow. Both models excel at understanding complex document structures, tables, diagrams, and multi-page content.