Tencent Open-Sources Hunyuan Hy3: 295B MoE, Hallucination Rate Drops to 5.4%
aigclink · x · 2026-07-06
Tencent has open-sourced the Hunyuan Hy3 large language model, featuring 295B total parameters and 21B activated parameters in an MoE architecture, with performance rivaling flagship models 2-5 times its size. It focuses on reliability in Agent scenarios: the hallucination rate dropped from 12.5% to 5.4%, common sense error rate from 25.4% to 12.7%, multi-turn dialogue failure rate from 17.4% to 7.9%, and long-context comprehension improved from 42.9% to 75.1%. It supports 256K context, switches between fast/slow thinking modes, and utilizes MTP speculative decoding to accelerate inference. In a blind test by 270 experts, Hy3 scored 2.67 (compared to GLM-5.1's 2.51), showing significant advantages in categories like frontend development, CI/CD, and data storage.
Related event: Tencent Open-Sources Hunyuan Hy3 for Agentic Workloads(26 posts)→
More from coding & agent
- Chaining dependent MCP tool calls: no rollback, duplicate risk — agentrsdg · 2026-09-11
- DeepMind-led paper makes design docs the source of truth, code disposable — SMART regenerates in 1.5-3h for ~$100 — Roger_M_Taylor · 2026-09-11
- Agent-built classifier labels 192k docs for $0.70 vs $13-26 with frontier LLMs — vanstriendaniel · 2026-09-11
- MathModelAgent gains traction: auto-solves math modeling and writes a submission-ready paper — jihe520 · 2026-09-11
- alphaXiv open-sources OpenResearch to run parallel research agents with any model — alphaXiv · 2026-09-11
- DeskcommCRM: open-source AI sales CRM with native agents and WhatsApp hits 1k stars — melgarafael · 2026-09-11