On April 23, Tencent released and open-sourced Hunyuan Hy3 Preview — its first model after completing the pretraining and reinforcement learning infrastructure rebuild in February 2026. 295B total parameters, 21B activated parameters, 256K context, the core goal: no longer obsessing over benchmark scores, but targeting Agent capability and real-scenario deployment. Hy3 uses a fast-slow thinking fused Mixture-of-Experts (MoE) architecture. This design isn't Tencent's invention — Mixtral and DeepSeek V3 have both validated MoE inference efficiency's advantages — but this time the focus is on fusion rather than simply stacking parameters: slow thinking handles complex reasoning, fast thinking responds to daily tasks, with 21B activated parameters controlling inference cost while preserving capability. 256K context support provides the foundation for complex Agent tasks. Tencent's chief AI scientist Yao Shunyu emphasized that they hope to avoid traditional leaderboard gaming by building their own evaluation system. This statement reflects a change in industry consensus: leading vendors beginning to emphasize real-scenario performance over leaderboard first place is a signal of the industry's maturation. API pricing is as low as 1.2 yuan per million tokens, with cost-performance as a direct selling point. Hy3 has already been deployed on Tencent Cloud, Yuanbao, QQ, Tencent Docs, and supports open-source Agent frameworks like OpenClaw and KiloCode. Rather than colliding head-on on general benchmarks, building developer ecosystem moats in code generation and Agent scenarios — that's Tencent's differentiation strategy. Whether the first step of Hunyuan's rebuild plan can be delivered, community feedback will be the litmus test.