Core Insights - ByteDance has launched an advanced visual-language multimodal model, Seed 1.5-VL, showcasing significant improvements in multimodal understanding and reasoning capabilities [1][2][3]. Group 1: Model Features - Seed 1.5-VL demonstrates enhanced visual localization and reasoning, with the ability to quickly and accurately identify various elements in images and videos [3][4]. - The model can process a single image and a prompt to identify and classify multiple objects, providing precise coordinates [4]. - It can analyze video footage to answer specific questions, showcasing its advanced video understanding capabilities [5]. Group 2: Performance Metrics - Despite having only 20 billion activation parameters, Seed 1.5-VL performs comparably to Gemini 2.5 Pro, achieving state-of-the-art results in 38 out of 60 public evaluation benchmarks [6]. - The inference cost is competitive, with input priced at 0.003 yuan per 1,000 tokens and output at 0.009 yuan per 1,000 tokens [7]. Group 3: Practical Applications - Developers can access Seed 1.5-VL through an API, enabling the creation of AI visual assistants, inspection systems, and interactive agents [7]. - The model's capabilities extend to complex tasks such as identifying emotions in images and solving visual puzzles, demonstrating its versatility [17][20]. Group 4: Technical Architecture - Seed 1.5-VL consists of three core components: a visual encoding module (SeedViT), a multi-layer perceptron (MLP) adapter, and a large language model (Seed1.5-LLM) [27]. - The model has undergone a unique training process, including multi-modal pre-training and reinforcement learning strategies, enhancing its performance while reducing inference costs [29][30]. Group 5: Industry Impact - The advancements presented at the Shanghai event indicate that ByteDance is building a comprehensive AI ecosystem, integrating various technologies from video generation to deep visual understanding [32]. - The emergence of Seed 1.5-VL signifies a step towards a true multimodal intelligent era, reshaping interactions with visual data [32][33].