Tencent open-sources Hunyuan Hy4 preview — 770B params, 1M context
China's open-weight race just got its heaviest new contender, and Tencent is pitching it as a productivity workhorse rather than a benchmark-chaser.
Tencent is open-sourcing Hy4 preview, a 770 billion-parameter Mixture-of-Experts model with a 1 million-token context window.
The architecture activates only 49 billion parameters per token — a 78-layer stack where each layer beyond the first holds 256 routed experts plus one shared expert, with eight experts firing per token. It ships under the Apache 2.0 license with an FP8 quantized version included, and is live on Hugging Face, ModelScope, and GitCode, with vLLM and SGLang support plus access through Tencent Cloud TokenHub and OpenRouter. It is the follow-up to April's Hy3 preview (295 billion total parameters), and Tencent telegraphed it on its August 14 earnings call.
Under the hood it leans hard on inference efficiency: a gated take on DeepSeek's sparse attention (Gated DSA) with IndexCache reusing sparse indices across layers, identity-hyperconnection residual paths, and a native multi-token-prediction layer for speculative decoding. The stated targets are software engineering's long-horizon tasks, cross-file office work, one-prompt playable game prototypes, and research reasoning.
The benchmark story is the most interesting part, and it requires careful reading. Tencent published no public benchmark scores. Instead it ran 163 internal experts across 203 engineering tasks, scoring 2.99 out of 4.00 — marginally ahead of GLM-5.3 (2.92) and Kimi K3 (2.94). That is a net win of roughly six percentage points over GLM-5.3, real but thin enough that the honest verdict is "same tier, third-party confirmation pending" — especially since Tencent set the questions and did the grading itself.
Tencent is transparent about the caveats: it calls this an early preview, acknowledges the model still underperforms on long reasoning and tends to over-verify itself, and its science demos — a 2× speedup on a molecular-dynamics simulation, quantum-transport leakage cut from 48.2% to 4.8%, and a century-old geometry lower bound pushed from 0.3808 to 0.41104 — are un-peer-reviewed and were run in Tencent's own environment.
What to watch: independent benchmarks are the only thing that will confirm whether Hy4's internal lead survives contact with evaluators Tencent doesn't control.
Does a 770B model shipping without public benchmark scores deserve first-tier status yet? Tell us in the comments.
Sources: Introducing Hy4 preview (Tencent) · Tencent-Hunyuan/Hy4-preview (GitHub) · tencent/Hy4-preview (Hugging Face) · ChainCatcher · ChooseAI
