What are Multimodal AI Chat and Vision Models?
Multimodal AI chat and vision models are advanced Vision-Language Models (VLMs) that combine natural language understanding with sophisticated visual processing capabilities. These models can analyze images, videos, documents, charts, and other visual content while engaging in conversational interactions. Using deep learning architectures like Mixture-of-Experts (MoE) and advanced reasoning paradigms, they translate visual information into meaningful dialogue and insights. This technology enables developers to create applications that can see, understand, and discuss visual content, democratizing access to powerful multimodal AI tools for everything from document analysis to visual assistance and educational applications.
GLM-4.5V
GLM-4.5V is the latest generation vision-language model (VLM) released by Zhipu AI. Built upon the flagship text model GLM-4.5-Air with 106B total parameters and 12B active parameters, it utilizes a Mixture-of-Experts (MoE) architecture to achieve superior performance at a lower inference cost. The model introduces innovations like 3D Rotated Positional Encoding (3D-RoPE), significantly enhancing its perception and reasoning abilities for 3D spatial relationships, and features a 'Thinking Mode' switch for flexible reasoning depth.
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 introduces innovations like 3D Rotated Positional Encoding (3D-RoPE), significantly enhancing its perception and reasoning abilities for 3D spatial relationships. 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.
Pros
- State-of-the-art performance on 41 multimodal benchmarks.
- Efficient MoE architecture with 106B total, 12B active parameters.
- Advanced 3D spatial reasoning with 3D-RoPE encoding.
Cons
- Higher output pricing compared to smaller models.
- May require more computational resources for optimal performance.
Why We Love It
- It combines cutting-edge multimodal capabilities with efficient MoE architecture, delivering state-of-the-art performance across diverse visual understanding tasks with flexible reasoning modes.
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: Compact Powerhouse with Advanced Reasoning
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 STEM problem-solving, video understanding, and long document understanding, handling images with resolutions up to 4K and arbitrary aspect ratios.
Pros
- Exceptional performance-to-size ratio with only 9B parameters.
- Advanced 'thinking paradigm' with RLCS training.
- Handles 4K resolution images with arbitrary aspect ratios.
Cons
- Smaller parameter count may limit complex reasoning in some scenarios.
- Being open-source may require more technical setup expertise.
Why We Love It
- It delivers remarkable multimodal reasoning performance in a compact 9B parameter package, making advanced vision-language capabilities accessible without massive computational requirements.
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 excels at 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, with accurate object localization and structured output generation for data like invoices and tables.
Qwen2.5-VL-32B-Instruct: Advanced Visual Agent with Tool Integration
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
- Exceptional visual agent capabilities for computer and phone use.
- Advanced object localization and structured data extraction.
- Extensive 131K context length for long document processing.
Cons
- Higher computational requirements with 32B parameters.
- Equal input and output pricing may be costly for extensive use.
Why We Love It
- It excels as a visual agent with advanced tool integration capabilities, making it perfect for practical applications requiring document analysis, object localization, and structured data extraction.
Multimodal AI Model Comparison
In this table, we compare 2025's leading multimodal AI models for chat and vision, each with unique strengths. For cutting-edge performance, GLM-4.5V offers state-of-the-art capabilities with efficient MoE architecture. For compact efficiency, GLM-4.1V-9B-Thinking provides remarkable reasoning in a smaller package, while Qwen2.5-VL-32B-Instruct excels as a visual agent with advanced tool integration. This side-by-side view helps you choose the right multimodal model for your specific chat and vision applications.
| Number | Model | Developer | Subtype | SiliconFlow Pricing | Core Strength |
|---|---|---|---|---|---|
| 1 | GLM-4.5V | zai | Vision-Language Model | $0.14-$0.86/M Tokens | State-of-the-art multimodal performance |
| 2 | GLM-4.1V-9B-Thinking | THUDM | Vision-Language Model | $0.035-$0.14/M Tokens | Compact powerhouse with advanced reasoning |
| 3 | Qwen2.5-VL-32B-Instruct | Qwen2.5 | Vision-Language Model | $0.27/M Tokens | Advanced visual agent with tool integration |
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 vision-language models stood out for their innovation, performance, and unique approach to solving challenges in multimodal chat and vision understanding applications.
Our in-depth analysis shows different leaders for various needs. GLM-4.5V is the top choice for state-of-the-art performance across diverse multimodal benchmarks with flexible thinking modes. GLM-4.1V-9B-Thinking is best for users who need advanced reasoning capabilities in a compact, cost-effective model. Qwen2.5-VL-32B-Instruct excels for applications requiring visual agents, document analysis, and structured data extraction.