CameraBench is a model for analyzing camera motion in videos, aimed at understanding the motion patterns of cameras through video interpretation. Its main advantage lies in using generative visual language models for principle classification of camera motions and video-text retrieval. Compared with traditional Structure from Motion (SfM) and Simultaneous Localization and Mapping (SLAM) methods, this model shows significant advantages in capturing scene semantics. The model is open-source and suitable for use by researchers and developers, with more improved versions to be released later.

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.

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.

HiDream-I1 is a new open-source image generation base model with 17 billion parameters, capable of generating high-quality images within seconds. This model is suitable for research and development and has performed excellently in multiple evaluations, demonstrating high efficiency and flexibility, making it suitable for various creative design and generation tasks.

EasyControl is a framework that provides efficient and flexible control for Diffusion Transformer (DiT), aiming to solve the efficiency bottlenecks and lack of model adaptability in the current DiT ecosystem. Its main advantages include: supporting multiple conditional combinations, improving generation flexibility and inference efficiency. This product is developed based on the latest research results and is suitable for use in image generation, style transfer, and other fields.

QVQ-Max is a visual reasoning model launched by the Qwen team, capable of understanding and analyzing image and video content to provide solutions. It is not limited to text input but can also handle complex visual information. Suitable for users who need multi-modal information processing, such as in education, work, and life scenarios. This product is developed based on deep learning and computer vision technology and is suitable for students, professionals, and creative individuals. This is the initial release, and subsequent optimizations will be continuous.

Qwen2.5-Omni is a new generation of end-to-end multimodal flagship model launched by Alibaba Cloud's Tongyi Qianwen team. Designed for comprehensive multimodal perception, this model seamlessly handles various input formats such as text, images, audio, and video, and generates text and natural speech synthesis output simultaneously through real-time streaming responses. Its innovative Thinker-Talker architecture and TMRoPE positional encoding technology enable it to excel in multimodal tasks, especially in audio, video, and image understanding. The model surpasses similar-scale unimodal models in several benchmark tests, demonstrating powerful performance and broad application potential. Currently, Qwen2.5-Omni is open-sourced on Hugging Face, ModelScope, DashScope, and GitHub, providing developers with abundant usage scenarios and development support.

CameraBench is a model for analyzing camera motion in videos, aimed at understanding the motion patterns of cameras through video interpretation. Its main advantage lies in using generative visual language models for principle classification of camera motions and video-text retrieval. Compared with traditional Structure from Motion (SfM) and Simultaneous Localization and Mapping (SLAM) methods, this model shows significant advantages in capturing scene semantics. The model is open-source and suitable for use by researchers and developers, with more improved versions to be released later.

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.

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.

InternVL3 is a multimodal large language model (MLLM) open-sourced by OpenGVLab, possessing superior multimodal perception and reasoning capabilities. This model series includes 7 sizes ranging from 1B to 78B parameters, capable of simultaneously processing various information types such as text, images, and videos, demonstrating excellent overall performance. InternVL3 excels in industrial image analysis and 3D visual perception, with its overall text performance even surpassing the Qwen2.5 series. The open-sourcing of this model provides strong support for multimodal application development and helps promote the application of multimodal technology in more fields.

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.

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.