Tencent just made a significant move in the open-weights AI race. The full Hunyuan Hy3 model is officially live — and after months of preview builds, the final release is everything the preview promised and more.
What Is Hy3?
Hy3 is a 295B total parameter Mixture-of-Experts (MoE) model from the Tencent Hy Team. MoE means not all those parameters are active at once: only 21B parameters activate per token, plus a 3.8B MTP (Multi-Token Prediction) layer. The result is a model with frontier-class capability but substantially lower compute costs than dense 295B models.
Key specs at a glance:
| Property | Value |
|---|---|
| Architecture | Mixture-of-Experts (MoE) |
| Total Parameters | 295B |
| Active Parameters | 21B per token |
| Context Window | 256K tokens |
| Max Agent Steps | 495-step workflows |
| SWE-bench Verified | ~74.4% |
| License | Apache 2.0 |
| Pricing | ~1 yuan/million input tokens |
That pricing figure is striking. At roughly 1 yuan per million input tokens, Hy3 offers competitive cost efficiency for teams running production agent workloads who are currently paying API bills to closed-source frontier providers.
Why Agentic Workloads Specifically?
Tencent built Hy3 with explicit attention to multi-step, multi-agent task execution. The headline number is 74.4% on SWE-bench Verified — a benchmark that requires models to actually resolve real GitHub issues in software codebases, not just describe how they might. This places Hy3 solidly among the top open-weights models on that metric.
A few standout architectural features worth noting:
- 256K context window — Large enough to hold substantial codebases, long agent trajectories, and complex multi-turn state without chunking
- Up to 495-step agent workflows — Unlike models that tend to degrade after a few dozen tool-use steps, Hy3 is explicitly benchmarked for very long agent runs
- Fast and slow thinking modes — The model supports both quick, efficient responses and extended reasoning chains, which is exactly what reliable agent pipelines need. Some tasks want speed; others want deliberation.
After releasing a preview in late April 2026 and gathering feedback from 50+ internal and external products, the Tencent Hy Team scaled up post-training with higher quality data. The gap between “preview” and “full release” here is meaningful — not a rename but a genuine capability upgrade.
How to Get Hy3
The weights are publicly available under Apache 2.0 — the most permissive open-source license, with no copyleft requirements for commercial use.
You can find the model at:
- Hugging Face: tencent/Hy3
- ModelScope: Tencent-Hunyuan/Hy3
- GitCode: via Tencent’s official GitCode AI hub
- GitHub README: Tencent-Hunyuan/Hy3
Deploying Hy3 for Agent Pipelines
The official README includes deployment guides for vLLM and SGLang — two of the most widely used open-source LLM serving frameworks. Both support tensor parallelism and are optimized for high-throughput inference.
For teams evaluating Hy3 for production agent workloads, the architecture suggests a few practical deployment considerations:
vLLM is a strong default for most teams. It handles the MoE routing efficiently and is well-integrated with the OpenAI-compatible API format that most agent frameworks expect. Check the official vLLM documentation and the Hy3 README for current --tensor-parallel-size requirements for a 295B MoE model.
SGLang is worth evaluating if you’re building high-concurrency agent pipelines — it has strong performance on batched, multi-turn inference scenarios.
⚠️ Accuracy note: Exact deployment commands are in the Hy3 README on GitHub. Always refer to the official documentation for the precise flags, especially
--tensor-parallel-size,--gpu-memory-utilization, and quantization settings, as these change with framework versions.
The Cost Efficiency Angle
For context on why this matters: running GPT-5 or Claude Sonnet at scale through the API gets expensive quickly. Hy3 at ~1 yuan/million tokens via Tencent’s own hosted API (or self-hosted from weights) is a real competitive pressure on that dynamic.
For engineering teams evaluating open-weights alternatives to frontier APIs for production agent workloads, Hy3 is now a serious candidate — especially for coding, tool use, and extended multi-step tasks where SWE-bench scores are a meaningful proxy.
What Changed Between Preview and Full Release
The April 2026 preview built buzz; the full release delivers on it. The Tencent team collected feedback from 50+ products and specifically improved post-training quality. While the architecture itself is the same, the training quality improvement means better instruction-following, more reliable tool use, and fewer failure modes in long agent runs.
This pattern — preview → community feedback → improved training → full release — is increasingly the way top open-source model releases work. The community acts as a distributed evaluation harness.
Sources
- Tencent-Hunyuan/Hy3 GitHub Repository — official README, model card, deployment docs
- Hy3 on Hugging Face — model weights and documentation
- Hy3 on ModelScope — additional distribution channel
- vLLM Documentation — deployment framework reference
Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260706-0800
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