The first principal component of progress “moving from pretraining to RL + product feedback loops” is not exactly a new thesis at this point. Still, the specific numbers here and the overall rapid progress of open source Chinese models along all dimensions is very impressive.

@jenzhuscott: So @TencentHunyuan @ShunyuYao12 did not disappoint - just dropped Hy3. Apache 2.0. The numbers 🔥🔥

• 295B total params, 21B active. Compare to DeepSeek/Qwen at ~1T, overseas frontier at ~10T.
• API: ¥1 / ¥4 / ¥0.25 per M tokens. Cheapest Chinese model on the market. Cheaper than DeepSeek (if one can imagine that👽) . ~7× cheaper than GLM-5.2.
• Token efficiency vs GLM-5.2 on office tasks: docs -47%, PPT -49%.
• Hallucination rate 12.5% → 5.4%. MRCR long-context 42.9% → 75.1%.
• SWE-Bench variance across 3 scaffolds (Codebuddy/Cline/KiloCode) ≤4 pp.
• 270-expert blind eval: Hy3 2.67/4 vs GLM5.1 2.51/4.

"Striking distance of frontier" is the wrong frame. At 21B active params, Hy3 isn't trying to be frontier. It's showing the frontier isn't where the value is.

The actual thesis buried in the announcement: scaling is moving from pretraining to RL + product feedback loops. WorkBuddy (Tencent's white-collar agent, #1 in workflow by user base) is the data flywheel. Pretraining scale plateaued. The new bottleneck is real-world task environments.

If that's right, the small-model-cheap-API play isn't a price war. It's the new architecture 🚀