@0x0SojalSec: Final take : Tencent recently drop a 295B parameter model that only activates 21B params per token. While most labs are…

Final take : Tencent recently drop a 295B parameter model that only activates 21B params per token. While most labs are still obsessed with parameter count, Hy3 is showing something more interesting: The real moat is shifting from pretraining scale to high-quality product,agent feedback loops. And it’s competitive with (sometimes beating) models 2-5x larger on agentic coding, tool use, and real productivity tasks. Key details: - 295B total, 21B active params (MoE) - 256K context - Strong gains in agentic coding, tool use & long-context tasks - Beats or matches much larger models on several practical benchmarks - Extremely cheap to run via API Apache 2.0 weights are out. Strong post-training from actual product feedback loops (WorkBuddy etc.). efficiency with real-world agent data to raw scale quite good , Hy3 is worth testing if you’re building agents or care about cost-performance.