Tencent が Hy3 をリリース
Tencent から Hy3 がリリースされました。
▸何が変わったのか
中文 ｜ English
[](#license)
[](https://huggingface.co/tencent/Hy3)
[](https://modelscope.cn/models/Tencent-Hunyuan/Hy3)
[](https://cnb.cool/ai-models/tencent/Hy3)
[](https://ai.gitcode.com/tencenthunyuan/Hy3)
🖥️ Official Website |
💬 GitHub
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Table of Contents
– Model Introduction
– Stronger Agent Performance
– Product Experience: More Reliable, More Cost-Effective
– Benchmark Appendix
– News
– Model Links
– Quickstart
– Deployment
– vLLM
– SGLang
– Finetuning
– Quantization
– License
– Contact Us
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Model Introduction
Hy3 is a 295B-parameter Mixture-of-Experts (MoE) model with 21B active parameters and 3.8B MTP layer parameters, developed by the Tencent Hy Team. Following the Hy3 Preview launch in late April, we gathered feedback from 50+ product teams. We fixed various issues in task execution and interaction, and improved both the quality and scale of our post-training pipeline. Today, we are launching Hy3. It significantly outperforms similar-size models and rivals flagship open-source models with 2-5x the parameters. It also shows solid gains in utility across productivity tasks and real-world applications.
| Property | Value |
|:—|:—|
| Architecture | Mixture-of-Experts (MoE) |
| Total Parameters | 295B |
| Activated Parameters | 21B |
| MTP Layer Parameters | 3.8B |
| Number of Layers (excluding MTP layer) | 80 |
| Number of MTP Layers | 1 |
| Attention Heads | 64 (GQA, 8 KV heads, head dim 128) |
| Hidden Size | 4096 |
| Intermediate Size | 13312 |
| Context Length | 256K |
| Vocabulary Size | 120832 |
| Number of Experts | 192 experts, top-8 activated |
| Supported Precisions | BF16 |
Stronger Agent Performance
Building on Hy3 Preview, we improved post-training data quality and diversity while scaling up RL training. Hy3 shows solid gains across reasoning, agentic workflows, and long-context tasks. Its performance is close to leading flagship models, both domestic and international.
In productivity scenarios such as coding, document processing, financial analysis, game development, and frontend design, Hy3 has made solid gains, positioning it as a reliable, cost-effective option.
We don’t think public benchmark scores tell the full story. So we ran a blind test with 270 experts from various disciplines, working on real-world workflows, and collected 312 valid comparisons. Hy3 scored 2.67/4, outperforming GLM-5.1 at 2.51/4. The advantage was clearest in frontend development, CI/CD, and data & storage.
Product Experience: More Reliable, More Cost-Effective
Utility in production is not fully captured by benchmarks. Based on extensive user feedback and product telemetry, we identified real-world behavior issues that break product experience and improved the model’s capabilities in those areas, earning uniformly positive feedback from product teams.
Output Formatting and Tool Calling Stability: We fixed multiple baseline reliability issues, brin
SOURCE: Tencent (2026-07-02)
