Tencent Hunyuan Hy3 Official Version Launches: Improved Reasoning and Agent Capabilities, Open-Sourced with Price Reduction

On July 6, 2026, Tencent released the official version of Hunyuan Hy3, approximately two and a half months after the Preview version on April 23. Hy3 adopts a MoE architecture with 295B total parameters, 21B activated parameters, and a 256K context window. Compared to the Preview version, the official version shows significant improvements in reasoning, instruction following, agent execution, and hallucination control, with positive growth across all 12 evaluation dimensions.

Core Improvements and Evaluation Performance

• Reasoning & Mathematics: GPQA Diamond (PhD-level science questions) scored 90.4%, close to GPT-5.5's 93.6%; MathArena Apex jumped from 12.8 in Preview to 38.7, a 207% improvement, but still below GPT-5.5's 85.4.
• Agent & Tool Calling: ClawEval scored 68.5%, second only to Claude Opus 4.8 (72.1%); SkillsBench (text-only) improved from 29.1 to 55.3, nearly 90% increase; MCP Atlas scored 79.1%, ranking last among mainstream models.
• Code & Software Engineering: SWE-bench Pro scored 57.9%, over 25% improvement from Preview, but trailing Claude Opus 4.8 (69.2%) and GLM-5.2 (62.1%).
• Search & Information Retrieval: BrowseComp scored 84.2%, nearly on par with GPT-5.5 (84.4%).

Reliability Improvements, Open Source, and Price Reduction

• Hallucination rate: Dropped from 12.5% to 5.4%.
• Multi-turn question rate: Dropped from 17.4% to 7.9%.
• Tool calling stability: Significantly improved.
• Open source & pricing: Released under Apache 2.0 license; API input price reduced to 1 yuan per million tokens.

Product Integration and Ecosystem

Hy3 has been integrated into Tencent Yuanbao, ima Knowledge Base, Tencent Docs, and other products. The WorkBuddy agent platform supports Hy3, offering three major scenarios: daily office work, code development, and creative design, with deep integration into the Tencent ecosystem (WeCom, Tencent Meeting, WeChat Mini Programs, etc.).

Controversies and Weaknesses

• Mathematical reasoning: Despite significant progress, still lags behind top international models and consumes more tokens.
• MCP tool calling: Insufficient fault tolerance in cross-tool collaboration scenarios.
• Instruction following: Strong proactive tendency, impressive when requirements are vague, but may lack restraint under strict constraints.

Industry Background

Hy3 was developed by the Yao Shunyu team, emphasizing "pragmatic AI" focused on real-world utility rather than benchmarks. It maintains restraint in parameter scale and context window, pushing performance limits through post-training and RL compute.