腾讯Hy3模型发布

Tencent released Apache-licensed Hy3, a 295B MoE (21B active parameters) model beating larger rivals outside coding. Hy3 is open-sourced under the Apache 2.0 license HuggingFace, so enterprises can legally use and affordably host. So GLM-5.2 (from Zhipu AI) still wins coding, especially hard repository-level coding benchmarks, but the point is GLM-5.2 is much larger model ( 744B total and about 40B active parameters). Hy3’s real strengths show up elsewhere. In agentic search, it scores 84.2 on BrowseComp and 91.0 on DeepSearchQA, beating every open model in Tencent’s table and staying competitive with Claude Opus 4.8 and GPT-5.5. Tencent also claims Hy3 became much more reliable than its April preview. Hallucinations reportedly dropped from 12.5% to 5.4%, while multi-turn issues also fell. From the deployment perspective comparison, GLM-5.2 at FP8 will need ~744GB, which makes an 8x H200 node the practical baseline for production serving. But Tencent's Hy3, at 295B total parameters, fits under a 300GB FP8 footprint, meaning it uses less than half the memory and roughly half the active parameters per token, cutting compute per request. For a team deciding what to self-host, that turns a heavy 1-node requirement into a much more attainable system, with headroom for KV cache and batching.  > **引用原帖 Tencent Hy (@TencentHunyuan):** > 🚀Hy3 is here. > 295B MoE. Best in its size class. Rivals trillion-scale flagships. > Reliable and affordable for most agentic usecases. > Apache 2.0. Friendly for commercial use. > FREE API for 2 weeks → https://t.co/EyURKwTdgi > 🤗 https://t.co/twqJpqb2SL > 📖 https://t.co/4uEkIU1cW4 https://t.co/LLJTfDFNQu > https://x.com/TencentHunyuan/status/2074148098876768478 Rohan Paul (@rohanpaul_ai): https://t.co/uN6Fr24vwO https://t.co/wneVpdnUQF