The video reviews Tencent’s new HY3 preview model, highlighting its strong performance in STEM and agentic tasks through flexible reasoning modes and advanced web integration, despite being smaller than trillion-parameter models. While Alibaba’s smaller Quen 27B model offers efficient and polished outputs, HY3 excels in handling complex prompts, detailed reasoning, and retrieving full web content, positioning it as a promising tool for research and advanced applications.
The video explores the new HY3 preview model developed by Tencent, China’s largest company, boasting 295 billion parameters and designed primarily for STEM fields and agentic tasks. Despite not reaching the trillion-parameter scale, HY3 impresses with its 21 billion active parameters and commercial-friendly AVA license, making it accessible for most users except those with extremely high monthly active users. The presenter compares HY3 against Alibaba’s Quen 27B model, which is significantly smaller but widely used, to evaluate whether bigger models truly deliver better performance.
Initial tests focus on mathematical reasoning and token generation speeds, revealing that HY3’s reasoning modes (off, low, high) significantly affect output quality and token count. While reasoning off produces faster but less accurate results, enabling low or high reasoning improves correctness but increases processing time and token generation. The high reasoning mode, although slow and resource-intensive, delivers the most accurate and detailed answers, as demonstrated with complex math problems and 3D solar system visualizations, closely matching cloud-based inferencing results.
The video also showcases creative coding tasks, such as generating a Flappy Birds clone and a 3D city simulation, comparing HY3’s outputs with Quen’s. Despite HY3’s larger size, Quen often produces more polished and playable results with lower memory usage, highlighting efficient optimization. However, HY3 excels in generating visually impressive environments with dynamic features like time-of-day sliders and lighting effects, especially when reasoning is enabled, demonstrating its potential for complex agentic applications.
Logic and decision-making tests further illustrate HY3’s capabilities, where different reasoning levels yield varying depths of analysis and token usage. The model effectively handles moral dilemmas and practical questions, with low reasoning providing concise answers and high reasoning offering more nuanced, detailed explanations. Additionally, HY3’s integration with web tools allows it to fetch and analyze live web content, outperforming Quen in retrieving full articles for research tasks, showcasing its strength in agentic and information retrieval scenarios.
In conclusion, the HY3 preview model from Tencent presents a promising advancement in large language models, particularly for STEM and agentic tasks, with flexible reasoning modes and strong web integration. While Quen remains competitive with efficient performance and polished outputs, HY3’s ability to handle complex prompts and access full web content positions it as a valuable tool for research and advanced applications. The video encourages further testing and comparison with other leading models to fully assess HY3’s capabilities as it continues to develop beyond its preview stage.