Tencent Releases Hy4 LLM: First Introduction of Computer-Use Capabilities, 770 Billion Parameters
Tencent launched Hy4, integrating computer-use capabilities for the first time in the Hy series, featuring 770 billion parameters and claiming significant performance gains over the preview version in long-context understanding and game development benchmarks.
Chinese tech giant Tencent Holdings Ltd. released its latest large language model, Hy4, on November 4, 2026. The model introduces "computer-use" capabilities for the first time in the Hy series, leveraging advanced visual understanding, reasoning, and tool-calling to deliver long-horizon agentic performance.
According to SiliconANGLE, Tencent stated that Hy4 marks a significant leap in performance over the Hy4 Preview released on August 28. The new model weighs in at 770 billion total parameters with 49 billion active parameters and supports a 1 million token context window, placing it in the same range as current open-weights frontier models from European and Chinese developers, including Mistral AI, Moonshot Technology, and Z.AI.
Tencent reported substantially scaling both pre-training and post-training, achieving gains in instruction following, long-context understanding, agentic long-horizon tasks, and deep research. The company also noted that the previous version suffered from excessive reasoning and over-verification during its "thinking" processes, which the new model has optimized.
In terms of practical application, Hy4 focuses heavily on real-world productivity for workplace collaboration, human experience, and professional work. Tencent highlighted improvements in web development, document summarization, visual creative tasks, and front-end/game development. For complex game logic, state management, and orchestration, Tencent said Hy4 integrates deeply with major engines like Unreal Engine, Unity, and Godot. The company claimed a 19.1 percent improvement in the GameDev bench and 9.8 percent in GameLogic benchmarks compared to the preview, although actual baseline numbers remain internal and unreleased.