{"id":3346,"date":"2025-10-26T10:04:43","date_gmt":"2025-10-26T02:04:43","guid":{"rendered":"https:\/\/crepal.ai\/blog\/?p=3346"},"modified":"2025-10-26T10:11:25","modified_gmt":"2025-10-26T02:11:25","slug":"longcat-video-meituan","status":"publish","type":"post","link":"https:\/\/crepal.ai\/blog\/aivideo\/longcat-video-meituan\/","title":{"rendered":"Meituan’s LongCat Video: The Ultimate Guide to the Open-Source Video AI"},"content":{"rendered":"\n
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Hello, I’m an AI content strategist and a long-time practitioner in the generative AI space. I’ve spent the last few years testing, benchmarking, and breaking down countless AI models. When Meituan, a tech giant known more for logistics than foundation models, released LongCat Video under an MIT license, I knew I had to dive in. This isn’t just another model; it’s a significant move in the open-source video generation race. Today, I’ll share my hands-on experience and in-depth analysis of what makes LongCat-Video tick.<\/p>\n\n\n\n

What is LongCat-Video? (An Essential Introduction)<\/h2>\n\n\n\n

At its core, LongCat-Video is a foundational video generation model developed by Meituan’s LongCat team. It’s designed to create video content from various inputs, including text, images, and even existing video clips. My first encounter with it was shortly after its release in late 2025, and what immediately stood out was its promise of generating minutes-long<\/em> videos, a notorious challenge for most open-source models at the time.<\/p>\n\n\n\n

It’s crucial to distinguish LongCat-Video from its sibling, LongCat-Flash<\/strong>, which is a 560-billion-parameter language model<\/em> (LLM) focused on text-based reasoning and agentic tasks. LongCat-Video is purely dedicated to the visual domain, a specialist in a family of increasingly capable AI models.<\/p>\n\n\n\n

LongCat-Video unifies multiple generation tasks into a single, powerful framework.<\/p>\n\n\n\n

To give you a clear, at-a-glance overview, I’ve compiled its key specifications based on the official documentation. This table summarizes the technical DNA of the model.<\/p>\n\n\n\n

Parameter Category<\/th>Specific Metric<\/th>Data \/ Description<\/th>Source & Time<\/th><\/tr><\/thead>
Model Architecture<\/td>Base Architecture<\/td>Dense Transformer<\/td>LongCat-Video GitHub<\/a> (2025-09-15)<\/td><\/tr>
Model Size<\/td>Total Parameters<\/td>13.6 Billion<\/td>LongCat-Video GitHub<\/a> (2025-09-15)<\/td><\/tr>
Core Capabilities<\/td>Supported Tasks<\/td>Text-to-Video, Image-to-Video, Video Continuation<\/td>LongCat-Video Technical Report<\/a> (2025-09-15)<\/td><\/tr>
Key Innovation<\/td>Efficiency Mechanism<\/td>Block Sparse Attention<\/td>ComfyUI GitHub Issue #10480<\/a> (2025-09-16)<\/td><\/tr>
Training Method<\/td>Optimization<\/td>Multi-reward Group Relative Policy Optimization (GRPO)<\/td>LongCat-Video GitHub<\/a> (2025-09-15)<\/td><\/tr>
License<\/td>Model Weights<\/td>MIT License<\/td>LongCat-Video GitHub<\/a> (2025-09-15)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
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Data Source: Analysis of public releases from Meituan’s GitHub<\/a> and VentureBeat<\/a>.<\/p>\n\n\n\n

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Data Source: Based on features described in the official repository<\/a>.<\/p>\n\n\n\n

Key Features of LongCat-Video & The Competitive Landscape<\/h2>\n\n\n\n

What makes LongCat-Video more than just another name in a crowded field? From my testing, its strength lies in a unique combination of versatility, long-form coherence, and open-source accessibility. Unlike proprietary models like Google’s Veo or OpenAI’s Sora, which are powerful but locked in a black box, LongCat-Video invites developers to build upon it.<\/p>\n\n\n\n

Side-by-side comparison often reveals subtle but crucial differences in motion quality and temporal consistency.<\/p>\n\n\n\n

Core Feature Deep Dive<\/h3>\n\n\n\n