What are Fastest Open Source Multimodal Models?
Fastest open source multimodal models are advanced vision-language models that can efficiently process and understand both visual and textual information simultaneously. These models combine computer vision and natural language processing capabilities to analyze images, videos, documents, and text with remarkable speed and accuracy. They enable developers to build applications that can understand visual content, answer questions about images, analyze documents, and perform complex reasoning tasks across multiple modalities—all while maintaining high inference speeds and cost-effectiveness for real-world deployment.
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, 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, with performance comparable to or even surpassing the much larger 72B-parameter models on 18 different benchmarks.
GLM-4.1V-9B-Thinking: Compact Powerhouse with Advanced Reasoning
GLM-4.1V-9B-Thinking is an open-source Vision-Language Model 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. 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 with a 66K context length.
Pros
- Compact 9B parameters with exceptional speed and efficiency.
- State-of-the-art performance comparable to much larger 72B models.
- Handles 4K images with arbitrary aspect ratios.
Cons
- Smaller parameter count may limit some complex reasoning tasks.
- Newer model with less extensive real-world testing.
Why We Love It
- It delivers exceptional performance with remarkable efficiency, proving that smaller models can compete with giants through innovative thinking paradigms and advanced training techniques.
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. The model can accurately localize objects in images and 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 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 and a massive 131K context length.
Pros
- Acts as a visual agent capable of computer and phone use.
- Exceptional 131K context length for extensive document processing.
- Advanced object localization and structured data extraction.
Cons
- Higher computational requirements with 32B parameters.
- More expensive inference costs compared to smaller models.
Why We Love It
- It combines powerful visual understanding with practical tool integration, making it perfect for real-world applications requiring both visual analysis and automated task execution.
GLM-4.5V
GLM-4.5V is the latest generation vision-language model released by Zhipu AI. Built upon the flagship text model GLM-4.5-Air, it has 106B total parameters and 12B active parameters, utilizing 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 response optimization.
GLM-4.5V: Next-Generation MoE Architecture with Thinking Mode
GLM-4.5V is the latest generation vision-language model 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.
Pros
- MoE architecture with only 12B active parameters for efficient inference.
- State-of-the-art performance on 41 public multimodal benchmarks.
- 3D-RoPE innovation for enhanced 3D spatial understanding.
Cons
- Large total parameter count (106B) may require significant storage.
- Complex MoE architecture may need specialized deployment expertise.
Why We Love It
- It represents the cutting edge of multimodal AI with its innovative MoE architecture, delivering flagship-level performance while maintaining inference efficiency through intelligent parameter activation.
Fastest Multimodal AI Model Comparison
In this table, we compare 2025's fastest open source multimodal models, each with unique strengths. For compact efficiency, GLM-4.1V-9B-Thinking provides exceptional performance in a small package. For advanced visual agent capabilities, Qwen2.5-VL-32B-Instruct offers unmatched tool integration and context length. For cutting-edge MoE architecture, GLM-4.5V delivers flagship performance with efficient inference. This side-by-side view helps you choose the right model for your specific multimodal AI requirements.
| Number | Model | Developer | Subtype | SiliconFlow Pricing | Core Strength |
|---|---|---|---|---|---|
| 1 | GLM-4.1V-9B-Thinking | THUDM | Vision-Language Model | $0.035/$0.14 per M tokens | Compact efficiency with advanced reasoning |
| 2 | Qwen2.5-VL-32B-Instruct | Qwen2.5 | Vision-Language Model | $0.27/$0.27 per M tokens | Visual agent with 131K context length |
| 3 | GLM-4.5V | zai | Vision-Language Model | $0.14/$0.86 per M tokens | MoE architecture with Thinking Mode |
Frequently Asked Questions
Our top three picks for the fastest open source multimodal models in 2025 are GLM-4.1V-9B-Thinking, Qwen2.5-VL-32B-Instruct, and GLM-4.5V. Each of these models stood out for their speed, innovation, performance, and unique approach to solving challenges in vision-language understanding and multimodal reasoning.
Our in-depth analysis shows different leaders for various needs. GLM-4.1V-9B-Thinking is ideal for applications requiring compact efficiency with strong reasoning. Qwen2.5-VL-32B-Instruct excels as a visual agent for tool integration and long document processing. GLM-4.5V is perfect for applications needing flagship-level performance with cost-effective inference through its MoE architecture.