Hunyuan-A13B-Instruct activates only 13 B of its 80 B parameters, yet matches much larger LLMs on mainstream benchmarks. It offers hybrid reasoning: low-latency “fast” mode or high-precision “slow” mode, switchable per call. Native 256 K-token context lets it digest book-length documents without degradation. Agent skills are tuned for BFCL-v3, τ-Bench and C3-Bench leadership, making it an excellent autonomous assistant backbone. Grouped Query Attention plus multi-format quantization delivers memory-light, GPU-efficient inference for real-world deployment, with built-in multilingual support and robust safety alignment for enterprise-grade applications....

Built for real-world business scenarios, Hy3 features a 295B/21B active MoE architecture, native 256K context support, and three reasoning modes. It enhances coding, long-form comprehension, multi-turn dialogue, and agentic task execution, balancing reliability, efficiency, and cost across both high-frequency interactions and complex workflows....

Hy3 preview is a 295B-parameter Mixture-of-Experts (MoE) language model from Tencent Hunyuan, built for production-grade agent workloads. With only 21B parameters activated per token and native 256K context support, it handles complex tasks like cross-file code refactoring, long-document analysis, and multi-step tool use, rather than just generating fluent dialogue. Hy3 scores near state-of-the-art on SWE-bench Verified and advanced STEM benchmarks, while offering three inference modes (no_think, think_low, think_high) to dynamically trade off latency and reasoning depth. Its sparse activation architecture delivers competitive intelligence at a significantly lower token cost....