What are Multimodal AI Models for Chat + Vision?
Multimodal AI models for chat and vision are advanced Vision-Language Models (VLMs) that can process and understand both text and visual content simultaneously. Using sophisticated deep learning architectures, they can analyze images, videos, documents, and charts while engaging in natural language conversations. This technology allows developers and creators to build applications that can reason about visual information, answer questions about images, extract structured data from documents, and act as visual agents. They foster collaboration, accelerate innovation, and democratize access to powerful multimodal tools, enabling a wide range of applications from document understanding to visual reasoning and computer vision tasks.
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. Technically, GLM-4.5V introduces innovations like 3D Rotated Positional Encoding (3D-RoPE), significantly enhancing its perception and reasoning abilities for 3D spatial relationships.
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 public multimodal benchmarks.
- MoE architecture with 106B total parameters for superior performance at lower cost.
- 3D-RoPE technology for enhanced 3D spatial reasoning.
Cons
- Higher output pricing at $0.86/M tokens on SiliconFlow.
- Larger model size may require more computational resources.
Why We Love It
- It delivers cutting-edge multimodal reasoning with innovative 3D spatial understanding and a flexible thinking mode that adapts to both quick responses and complex reasoning tasks.
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 Open-Source 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 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
- Exceptional performance-to-size ratio, matching 72B models.
- Excels at STEM problems, video understanding, and long documents.
- Handles 4K resolution images with arbitrary aspect ratios.
Cons
- Smaller 9B parameter size compared to flagship models.
- May not match the absolute peak performance of larger models.
Why We Love It
- It punches far above its weight class, delivering performance comparable to much larger models while being cost-effective and open-source with exceptional reasoning capabilities.
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: 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. With a 131K context length, it can process extensive visual and textual information.
Pros
- Acts as a visual agent capable of computer and phone use.
- Exceptional at analyzing charts, layouts, and structured data.
- Generates structured outputs for invoices and tables.
Cons
- Pricing at $0.27/M tokens for both input and output on SiliconFlow.
- May require more resources than smaller models.
Why We Love It
- It bridges the gap between visual understanding and action, functioning as a true visual agent that can interact with computers and extract structured data with human-aligned responses.
Multimodal AI Model Comparison
In this table, we compare 2025's leading multimodal AI models for chat and vision, each with a unique strength. For state-of-the-art reasoning with 3D spatial understanding, GLM-4.5V provides cutting-edge performance. For efficient open-source multimodal reasoning, GLM-4.1V-9B-Thinking offers exceptional value. For visual agent capabilities and structured data extraction, Qwen2.5-VL-32B-Instruct excels. This side-by-side view helps you choose the right tool for your specific multimodal AI application.
| Number | Model | Developer | Subtype | Pricing (SiliconFlow) | Core Strength |
|---|---|---|---|---|---|
| 1 | GLM-4.5V | zai | Chat + Vision | $0.14 input / $0.86 output per M tokens | State-of-the-art 3D spatial reasoning |
| 2 | GLM-4.1V-9B-Thinking | THUDM | Chat + Vision | $0.035 input / $0.14 output per M tokens | Efficient reasoning matching 72B models |
| 3 | Qwen2.5-VL-32B-Instruct | Qwen2.5 | Chat + Vision | $0.27 per M tokens | Visual agent with structured data extraction |
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 chat and vision tasks, from 3D spatial reasoning to visual agent capabilities.
Our in-depth analysis shows several leaders for different needs. GLM-4.5V is the top choice for advanced 3D spatial reasoning and complex multimodal tasks requiring deep thinking. For cost-effective deployment with strong reasoning capabilities, GLM-4.1V-9B-Thinking offers exceptional performance at 9B parameters. For visual agent applications, document understanding, and structured data extraction, Qwen2.5-VL-32B-Instruct excels with its 131K context length and tool-use capabilities.