What are Open Source Multimodal Models?
Open source multimodal models are advanced AI systems that can process and understand multiple types of data simultaneously—including text, images, videos, and documents. These Vision-Language Models (VLMs) combine natural language processing with computer vision to perform complex reasoning tasks across different modalities. They enable developers and researchers to build applications that can analyze visual content, understand spatial relationships, process long documents, and act as visual agents. This technology democratizes access to powerful multimodal AI capabilities, fostering innovation and collaboration in fields ranging from scientific research to commercial applications.
GLM-4.5V
GLM-4.5V is the latest generation vision-language model released by Zhipu AI, built upon the flagship GLM-4.5-Air with 106B total parameters and 12B active parameters. It utilizes a Mixture-of-Experts (MoE) architecture for superior performance at lower inference cost. The model introduces 3D Rotated Positional Encoding (3D-RoPE), significantly enhancing perception and reasoning abilities for 3D spatial relationships, and achieves state-of-the-art performance among open-source models on 41 public multimodal benchmarks.
GLM-4.5V: State-of-the-Art Multimodal Reasoning
GLM-4.5V represents the cutting edge of vision-language models with its innovative MoE architecture and 3D-RoPE technology. Through optimization across pre-training, supervised fine-tuning, and reinforcement learning phases, the model excels at processing diverse visual content including images, videos, and long documents. Its 'Thinking Mode' switch allows users to balance between quick responses and deep reasoning, making it versatile for both efficiency-focused and analysis-heavy applications. With 66K context length and superior performance on 41 benchmarks, it sets the standard for open-source multimodal AI.
Pros
- State-of-the-art performance on 41 multimodal benchmarks.
- Innovative 3D-RoPE for enhanced spatial reasoning.
- Efficient MoE architecture with 12B active parameters.
Cons
- Higher computational requirements due to 106B total parameters.
- More expensive inference costs compared to smaller models.
Why We Love It
- It combines cutting-edge MoE architecture with 3D spatial reasoning capabilities, delivering unmatched performance across diverse multimodal tasks while maintaining efficiency through its innovative design.
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. Built on GLM-4-9B-0414, it introduces a 'thinking paradigm' and leverages Reinforcement Learning with Curriculum Sampling (RLCS). As a 9B-parameter model, it achieves state-of-the-art performance comparable to much larger 72B models, excelling in STEM problem-solving, video understanding, and long document analysis with 4K image resolution support.
GLM-4.1V-9B-Thinking: Efficient Multimodal Reasoning
GLM-4.1V-9B-Thinking demonstrates that smaller models can achieve exceptional performance through innovative training approaches. Its 'thinking paradigm' and RLCS methodology enable it to compete with models four times its size, making it incredibly efficient for resource-conscious deployments. The model handles diverse tasks including complex STEM problems, video analysis, and document understanding while supporting 4K images with arbitrary aspect ratios. With 66K context length and competitive pricing on SiliconFlow, it offers an excellent balance of capability and efficiency.
Pros
- Matches 72B model performance with only 9B parameters.
- Innovative 'thinking paradigm' for enhanced reasoning.
- Excellent STEM problem-solving capabilities.
Cons
- Smaller parameter count may limit some complex tasks.
- May require more sophisticated prompting for optimal results.
Why We Love It
- It proves that innovative training methods can make smaller models punch above their weight, delivering exceptional multimodal reasoning at a fraction of the computational cost.
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 that can reason and dynamically direct tools, capable of computer and phone use. The model can accurately localize objects, generate structured outputs for data like invoices and tables, with enhanced mathematical and problem-solving abilities through reinforcement learning.
Qwen2.5-VL-32B-Instruct: Advanced Visual Agent
Qwen2.5-VL-32B-Instruct excels as a visual agent capable of sophisticated reasoning and tool direction. Beyond standard image recognition, it specializes in structured data extraction from invoices, tables, and complex documents. Its ability to act as a computer and phone interface agent, combined with precise object localization and layout analysis, makes it ideal for automation and productivity applications. With 131K context length and enhanced mathematical capabilities through reinforcement learning, it represents a significant advancement in practical multimodal AI applications.
Pros
- Advanced visual agent capabilities for tool direction.
- Excellent structured data extraction from documents.
- Capable of computer and phone interface automation.
Cons
- Mid-range parameter count may limit some complex reasoning.
- Balanced pricing on SiliconFlow reflects computational demands.
Why We Love It
- It transforms multimodal AI from passive analysis to active agent capabilities, enabling automation and structured data processing that bridges the gap between AI and practical applications.
Multimodal AI Model Comparison
In this table, we compare 2025's leading open source multimodal models, each with unique strengths. GLM-4.5V offers state-of-the-art performance with advanced 3D reasoning, GLM-4.1V-9B-Thinking provides exceptional efficiency with innovative thinking paradigms, while Qwen2.5-VL-32B-Instruct excels as a visual agent for practical applications. This comparison helps you choose the right model for your specific multimodal AI needs.
| Number | Model | Developer | Subtype | SiliconFlow Pricing | Core Strength |
|---|---|---|---|---|---|
| 1 | GLM-4.5V | zai | Vision-Language Model | $0.14 input / $0.86 output per M tokens | State-of-the-art 3D reasoning |
| 2 | GLM-4.1V-9B-Thinking | THUDM | Vision-Language Model | $0.035 input / $0.14 output per M tokens | Efficient thinking paradigm |
| 3 | Qwen2.5-VL-32B-Instruct | Qwen2.5 | Vision-Language Model | $0.27 per M tokens | Advanced visual agent |
Frequently Asked Questions
Our top three picks for 2025 are GLM-4.5V, GLM-4.1V-9B-Thinking, and Qwen2.5-VL-32B-Instruct. Each of these models stood out for their innovation, performance, and unique approach to solving challenges in multimodal reasoning, visual understanding, and practical agent applications.
For maximum performance and 3D reasoning, GLM-4.5V is the top choice with state-of-the-art benchmark results. For cost-effective deployment with strong reasoning, GLM-4.1V-9B-Thinking offers exceptional value. For visual agent applications and structured data extraction, Qwen2.5-VL-32B-Instruct provides the most practical capabilities.