What changed
On August 28, 2026, Tencent released Hy4 preview as open weights on Hugging Face and other model hubs. The Mixture-of-Experts model has 770B total parameters with 49B activated per token, 78 backbone layers, a one-million-token context window and a native MTP speculative-decoding layer. Tencent publishes both full and FP8 checkpoints under Apache 2.0 and provides vLLM and SGLang serving recipes, tool-call and reasoning parsers, fine-tuning guidance and quantization tooling. Reuters independently confirmed the release and Tencent’s positioning around software engineering, research and analytical work.
Why it matters
Hy4 adds another frontier-scale open model that builders can actually inspect and run behind their own infrastructure rather than accessing only through a proprietary API. The 49B-active sparse design can reduce per-token compute relative to its 770B total size, but the practical deployment floor remains high: the full Hugging Face repository is about 1.56TB and Tencent’s published FP8 serving recipe uses eight-way tensor parallelism. For organizations that can operate that class of hardware, the release expands model-control and privacy options for long-context coding and knowledge work. Smaller builders are more likely to consume it through hosted inference or later quantizations.
The release is open enough to benchmark independently
Tencent publishes the model under Apache 2.0 and provides full and FP8 checkpoints plus source recipes. That means the launch can be evaluated on private repositories, long documents, agent tools and organization-specific tasks rather than relying only on Tencent’s product demos.
Sparse activation does not make this a desktop model
Only 49B of 770B backbone parameters are active per token, but all model weights still need to live somewhere. The full Hugging Face repository is about 1.56TB and the FP8 variant remains hundreds of gigabytes. Tencent’s vLLM example uses tensor parallelism across eight GPUs, so builders should distinguish active-compute efficiency from memory footprint.
Tencent is optimizing for long-horizon productivity work
Tencent describes Hy4 as stronger in software engineering, financial and office analysis, game development and scientific research. The architecture provides a 1M-token context window and native speculative decoding, while the serving stack includes automatic tool choice and model-specific tool/reasoning parsers.
The quality evidence is useful but vendor-led
Tencent reports a blind internal evaluation in which 163 employees rated 203 engineering tasks and Hy4 scored slightly ahead of GLM-5.3 and Kimi K3. That is more concrete than a marketing adjective, but it remains an internal Tencent evaluation with company-selected tasks and raters. Independent public harnesses will be more informative for cross-model decisions.
Tencent itself labels this an early model
The project documents known tendencies to over-reason and over-verify and calls Hy4 an early preview with further pre- and post-training work planned. Teams should expect model and serving behavior to evolve rather than treating the preview as a frozen production baseline.