Index-AniSora is a top-level animation video generation model open-sourced by Bilibili, based on AniSora technology. It supports one-click generation of multiple 2D style video shots, such as anime, national creation, comic改编 animations, VTubers, animated PVs, and meme animations. The model improves the efficiency and quality of animation content production through a reinforcement learning technology framework, and its technical principles have been accepted by IJCAI2025. The openness of Index-AniSora brings new technological breakthroughs to the animation video generation field, providing powerful tools for developers and creators, and promoting further development of 2D content creation.

PixVerse-MCP is a tool that allows users to access PixVerse's latest video generation models through applications that support the Model Context Protocol (MCP). This product offers features such as text-to-video generation and is suitable for creators and developers to generate high-quality videos anywhere. The PixVerse platform requires API credits, which users need to purchase themselves.

Wan2.1-FLF2V-14B is an open-source, large-scale video generation model designed to advance the field of video generation. This model excels in multiple benchmark tests, supports consumer-grade GPUs, and efficiently generates 480P and 720P videos. It performs exceptionally well in various tasks, including text-to-video and image-to-video, possessing strong visual-text generation capabilities suitable for diverse real-world applications.

Skywork-OR1 is a high-performance mathematical code reasoning model developed by Kunlun Wanwei's Tiangong team. This model series achieves industry-leading inference performance with comparable parameter scales, breaking through the bottleneck of large models in logical understanding and complex task solving. The Skywork-OR1 series includes three models: Skywork-OR1-Math-7B, Skywork-OR1-7B-Preview, and Skywork-OR1-32B-Preview, focusing on mathematical reasoning, general reasoning, and high-performance reasoning tasks, respectively. This open-source release not only includes model weights but also fully opens the training dataset and complete training code. All resources have been uploaded to GitHub and Hugging Face, providing the AI community with a fully reproducible practical reference. This comprehensive open-source strategy helps to promote the common progress of the entire AI community in reasoning ability research.

Pusa introduces an innovative approach to video diffusion modeling through frame-level noise control, enabling high-quality video generation suitable for various tasks (text-to-video, image-to-video, etc.). With its superior motion fidelity and efficient training process, the model offers an open-source solution for convenient video generation.

Kimi-VL is an advanced expert-mixed visual language model designed for multi-modal reasoning, long-context understanding, and powerful agent capabilities. This model excels in several complex domains, boasting efficient 2.8B parameters while exhibiting outstanding mathematical reasoning and image understanding capabilities. Kimi-VL sets a new standard for multi-modal models with its optimized computational performance and ability to handle long inputs.

Dream 7B is the latest diffusion large language model jointly launched by the NLP group of the University of Hong Kong and Huawei Noah's Ark Lab. It demonstrates excellent performance in text generation, especially in complex reasoning, long-term planning, and contextual coherence. The model adopts advanced training methods, possesses strong planning capabilities and flexible reasoning capabilities, and provides stronger support for various AI applications.

Llama-3.1-Nemotron-Ultra-253B-v1 is a large language model based on Llama-3.1-405B-Instruct, which has undergone multi-stage post-training to enhance reasoning and chat capabilities. This model supports context lengths up to 128K, offering a good balance between accuracy and efficiency. Suitable for commercial use, it aims to provide developers with powerful AI assistant functionality.

Index-AniSora is a top-level animation video generation model open-sourced by Bilibili, based on AniSora technology. It supports one-click generation of multiple 2D style video shots, such as anime, national creation, comic改编 animations, VTubers, animated PVs, and meme animations. The model improves the efficiency and quality of animation content production through a reinforcement learning technology framework, and its technical principles have been accepted by IJCAI2025. The openness of Index-AniSora brings new technological breakthroughs to the animation video generation field, providing powerful tools for developers and creators, and promoting further development of 2D content creation.

EmaFusion? is an innovative AI model that integrates over 100 foundation and specialized models to deliver the highest accuracy at the lowest cost and latency. Tailored for enterprises, it ensures secure, efficient, and scalable AI applications with built-in fault tolerance and customized controls. EmaFusion? is designed to boost the efficiency of AI applications and is suitable for a wide range of business needs.

PixVerse-MCP is a tool that allows users to access PixVerse's latest video generation models through applications that support the Model Context Protocol (MCP). This product offers features such as text-to-video generation and is suitable for creators and developers to generate high-quality videos anywhere. The PixVerse platform requires API credits, which users need to purchase themselves.

Vidu Q1, launched by Shengshu Technology, is a domestically produced video generation large language model designed for video creators. It supports high-definition 1080p video generation and features cinematic camera movements and start/end frame functionality. This product ranked first in the VBench-1.0 and VBench-2.0 evaluations, offering exceptional value for money at only one-tenth the price of competitors. It is suitable for film, advertising, animation, and other fields, significantly reducing production costs and improving creative efficiency.

Wan2.1-FLF2V-14B is an open-source, large-scale video generation model designed to advance the field of video generation. This model excels in multiple benchmark tests, supports consumer-grade GPUs, and efficiently generates 480P and 720P videos. It performs exceptionally well in various tasks, including text-to-video and image-to-video, possessing strong visual-text generation capabilities suitable for diverse real-world applications.

FramePack is an innovative video generation model designed to improve the quality and efficiency of video generation by compressing the context of input frames. Its main advantage lies in addressing the drift problem in video generation, maintaining video quality through a bidirectional sampling method, and being suitable for users who need to generate long videos. This technology is based on in-depth research and experiments on existing models to improve the stability and coherence of video generation.

GPT-4.1 is a series of new models that offer significant performance improvements, particularly in coding, instruction following, and handling long text contexts. Its context window has been expanded to 1 million tokens, and it excels in real-world applications, making it suitable for developers to create more efficient applications. This model is relatively low-cost and offers fast response times, making it more efficient for developing and executing complex tasks.

GLM-4-32B is a high-performance generative language model designed to handle various natural language tasks. Trained using deep learning techniques, it can generate coherent text and answer complex questions. This model is suitable for academic research, commercial applications, and developers. It is reasonably priced, precisely positioned, and a leading product in the field of natural language processing.