What are Multimodal AI Models?
Multimodal AI models are advanced vision-language models (VLMs) that can process and understand multiple types of input simultaneously, including text, images, videos, and documents. Using sophisticated deep learning architectures, they analyze visual content alongside textual information to perform complex reasoning, visual understanding, and content generation tasks. This technology allows developers and creators to build applications that can understand charts, solve visual problems, analyze documents, and act as visual agents with unprecedented capability. They foster collaboration, accelerate innovation, and democratize access to powerful multimodal intelligence, enabling a wide range of applications from educational tools to enterprise automation solutions.
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. 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.
GLM-4.5V: State-of-the-Art Multimodal Reasoning
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.
Pros
- State-of-the-art performance on 41 multimodal benchmarks.
- MoE architecture for superior performance at lower cost.
- 3D-RoPE for enhanced 3D spatial reasoning.
Cons
- Higher output price at $0.86/M tokens on SiliconFlow.
- Requires understanding of MoE architecture for optimization.
Why We Love It
- It combines cutting-edge multimodal reasoning with flexible thinking modes, achieving benchmark-leading performance while processing diverse visual content from images to videos and long documents.
GLM-4.1V-9B-Thinking
GLM-4.1V-9B-Thinking is an open-source Vision-Language Model (VLM) jointly released by Zhipu AI and Tsinghua University's KEG lab, designed to advance general-purpose multimodal reasoning. Built upon the GLM-4-9B-0414 foundation model, it introduces a 'thinking paradigm' and leverages Reinforcement Learning with Curriculum Sampling (RLCS) to significantly enhance its capabilities in complex tasks.
GLM-4.1V-9B-Thinking: Efficient Multimodal Reasoning Champion
GLM-4.1V-9B-Thinking is an open-source Vision-Language Model (VLM) jointly released by Zhipu AI and Tsinghua University's KEG lab, designed to advance general-purpose multimodal reasoning. Built upon the GLM-4-9B-0414 foundation model, it introduces a 'thinking paradigm' and leverages Reinforcement Learning with Curriculum Sampling (RLCS) to significantly enhance its capabilities in complex tasks. As a 9B-parameter model, it achieves state-of-the-art performance among models of a similar size, and its performance is comparable to or even surpasses the much larger 72B-parameter Qwen-2.5-VL-72B on 18 different benchmarks. The model excels in a diverse range of tasks, including STEM problem-solving, video understanding, and long document understanding, and it can handle images with resolutions up to 4K and arbitrary aspect ratios.
Pros
- Outperforms much larger 72B models on 18 benchmarks.
- Efficient 9B parameters for cost-effective deployment.
- Handles 4K resolution images with arbitrary aspect ratios.
Cons
- Smaller parameter count than flagship models.
- May require fine-tuning for specialized domains.
Why We Love It
- It delivers flagship-level performance at a fraction of the size and cost, punching well above its weight class with innovative thinking paradigms and reinforcement learning optimization.
Qwen2.5-VL-32B-Instruct
Qwen2.5-VL-32B-Instruct is a multimodal large language model released by the Qwen team, part of the Qwen2.5-VL series. This model is not only proficient in recognizing common objects but is 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.
Qwen2.5-VL-32B-Instruct: The Visual Agent Powerhouse
Qwen2.5-VL-32B-Instruct is a multimodal large language model released by the Qwen team, part of the Qwen2.5-VL series. This model is not only proficient in recognizing common objects but is 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. Additionally, the model can accurately localize objects in images, and generate structured outputs for data like invoices and tables. Compared to its predecessor Qwen2-VL, this version has enhanced mathematical and problem-solving abilities through reinforcement learning, with response styles adjusted to better align with human preferences.
Pros
- Acts as a visual agent for computer and phone control.
- Exceptional at analyzing charts, layouts, and documents.
- Generates structured outputs for invoices and tables.
Cons
- Mid-range parameter count compared to larger models.
- Equal input and output pricing structure.
Why We Love It
- It's a true visual agent that can control computers and phones while excelling at document analysis and structured data extraction, making it perfect for automation and enterprise applications.
Multimodal AI Model Comparison
In this table, we compare 2025's leading multimodal AI models, each with a unique strength. For state-of-the-art performance across diverse visual tasks, GLM-4.5V provides flagship-level capabilities with MoE efficiency. For cost-effective multimodal reasoning that rivals larger models, GLM-4.1V-9B-Thinking offers exceptional value. For visual agent capabilities and document understanding, Qwen2.5-VL-32B-Instruct excels. This side-by-side view helps you choose the right tool for your specific multimodal AI needs.
| Number | Model | Developer | Subtype | Pricing (SiliconFlow) | Core Strength |
|---|---|---|---|---|---|
| 1 | GLM-4.5V | Zhipu AI | Vision-Language Model | $0.14/M input, $0.86/M output | State-of-the-art multimodal reasoning |
| 2 | GLM-4.1V-9B-Thinking | THUDM / Zhipu AI | Vision-Language Model | $0.035/M input, $0.14/M output | Efficient performance rivaling 72B models |
| 3 | Qwen2.5-VL-32B-Instruct | Qwen | Vision-Language Model | $0.27/M tokens | Visual agent with document analysis |
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 vision-language tasks.
Our in-depth analysis shows several leaders for different needs. GLM-4.5V is the top choice for state-of-the-art performance across 41 multimodal benchmarks with flexible thinking modes. For budget-conscious deployments that still need flagship-level performance, GLM-4.1V-9B-Thinking delivers exceptional value, outperforming models three times its size. For visual agent capabilities and document analysis, Qwen2.5-VL-32B-Instruct excels with its ability to control computers and extract structured data.