Tencent's Hunyuan family rounds out the 2026 Chinese open-model field, and its angle is efficiency plus multimodality. Where others chase raw scale, Hunyuan Hy3 is engineered to match much bigger models on agentic and coding tasks while activating only a small slice of its parameters — and Tencent brings serious pedigree in image, 3D and video generation.
A sparse model that punches up
Hy3 is a 295-billion-parameter mixture-of-experts that routes each token through just 21 billion active parameters. That 14-to-1 sparsity is the point: Tencent positions Hy3 as matching the agentic and coding performance of flagship models 2–5× its size, at far lower cost per token. If you want the intuition for how a big model can be cheap to run, our parameters & scaling laws guide explains MoE sparsity.
Apache-2.0 and a 256K context
Hy3 is released under Apache-2.0 — permissive, commercial-friendly, self-hostable — with a 256K-token context window. That is smaller than the 1M-token contexts of Kimi K3, DeepSeek V4 and GLM-5.2, but ample for most agent and document tasks, and the smaller active footprint makes it comparatively affordable to serve.
The multimodal edge
Tencent's differentiator is generative multimodality. Beyond text, the Hunyuan ecosystem includes strong image, 3D-asset and video generation models (the lineage behind HunyuanVideo and Hunyuan3D). For teams whose work is visual — marketing content, game assets, product imagery, short video — Hunyuan is often the most directly useful open family, not just a text model with pictures bolted on.
Where Hunyuan fits for a Malaysian team
Two sweet spots. First, cost-efficient agents: if you want strong coding/agent behaviour without the GPU bill of a 700B-plus model, Hy3's sparse design is attractive and Apache-2.0 keeps it self-hostable for PDPA control. Second, creative and multimodal production: image, 3D and video generation for content, e-commerce and media. Build either on the deployment skills in our AI Engineering course, and orchestrate multi-step pipelines with n8n.
Limitations
The 256K context, while generous, trails the 1M-token leaders for very long single-request tasks. On the hardest pure-reasoning benchmarks the largest models may still edge ahead. And as with the multimodal generators, check licensing and usage terms per component — multimodal releases sometimes carry conditions the text model does not. Validate on your workload and pin the version.
Hunyuan Hy3 benchmarks (2026)
| Benchmark | Score | What it measures |
|---|---|---|
| SWE-bench Verified | 74.4% | Real GitHub issue fixes — exceeds most closed models |
| GPQA Diamond | 90.4 | Graduate-level science reasoning |
| USAMO 2026 | 72.0 | Olympiad-level mathematics |
| Expert blind eval | 2.67 / 4 | 270 experts on real workplace tasks (vs GLM-5.1's 2.51) |
Official resources & downloads
Go straight to the source — official sites, model cards and weight downloads: