The open-weights bar has moved up again. On August 28, Tencent announced and open-sourced Hy4 preview, its next-generation large language model: 770B total parameters with 49B active per token in a mixture-of-experts design, and a context window exceeding 1M tokens. The company positions it in the top tier of open-source models.

Built for productivity, not benchmark slides

Hy4 preview is explicitly aimed at real-world productivity across three fronts: software engineering, office work, and scientific research.

On the engineering side, Tencent says the model delivers stronger understanding, planning, debugging and validation for long-context development tasks, plus better front-end visual quality and interaction. For office scenarios it targets financial analysis, data analysis and cross-document collaboration, covering the full workflow from information processing to producing documents, spreadsheets and presentations. In game development, it can generate a playable prototype from a single natural-language request and refine complex projects with game engines over multi-turn interactions. In research, the company names AI R&D, molecular dynamics simulation, condensed-matter physics and fundamental mathematics as areas of notable improvement.

Behind this is a data strategy Tencent describes as co-created with its own experts across software engineering, gaming, finance and security, plus deep co-design with products like WorkBuddy.

Internal blind test: slightly ahead of GLM-5.3 and Kimi K3

Tencent shared an internal blind evaluation: 163 experts, 203 engineering tasks, and Hy4 preview scored an average of 2.99 out of 4.00 — slightly ahead of GLM-5.3 (2.92) and Kimi K3 (2.94). Worth noting this is Tencent's own evaluation, so treat the magnitude, not the ranking, as the signal.

The most interesting detail: the model helped build itself

The most notable part of the announcement is that Hy4 preview contributed to its own development for the first time. It participated in automated optimization of training methods, data strategies, evaluation frameworks and low-level operators — proposing approaches, running experiments and iterating, with the resulting code, logs and feedback fed into subsequent rounds. Tencent calls this an early-stage recursive self-improvement loop.

There is also a concrete number: the model autonomously analyzed bottlenecks in its inference system and ran multiple rounds of optimization on operator fusion and communication, raising end-to-end throughput by 31.8% over the baseline, with consistent gains across different context lengths and concurrency levels. An AI optimizing its own inference stack is a genuinely hardcore case of AI applied to AI infrastructure.

Pricing and availability

Hy4 preview ships as an open-source model, and is also accessible through WorkBuddy, CodeBuddy, Yuanbao, ima and other Tencent products, with API access via Tencent Cloud TokenHub and OpenRouter. It is free for two weeks on WorkBuddy and CodeBuddy, and free access to Hy3 on both platforms has been extended to September 30. API pricing is USD 0.834 per million input tokens, USD 2.501 per million output tokens, and USD 0.042 per million tokens for cache hits. Tencent says the next batch of models in the Hy4 series is expected soon.

For developers, 770B total / 49B active with a 1M-token context at this price point makes the open-weights field one option richer for serious production evaluation — as for the "top tier" claim, that stays Tencent's own words until independent leaderboards weigh in.

Reference: Tencent official announcement — https://www.tencent.com/tencent-releases-and-open-sources-tencent-hy4-preview/