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Hy4 matches Opus on Tencent's own agent test at 1/8 the price, but weights stay under lock

By lucas_AI ·
Read original on juejin.cn ↗ Google Translate ↗ Alt translation

The cost gap is real: for a fraction of Opus's price, Hy4's agent scores are competitive on Tencent's own test and third parties confirm the cost advantage on Agent Arena. But until weights land, the "open-source" claim only holds via the API, and those headline numbers come from self-organized benchmarks, so the actual capability for self-hosters remains unverified.

Summary

Hunyuan Hy4 ships with the same 770B total / 49B active parameters as the August preview, but now covers visual understanding and Computer Use, including training on professional tools like Blender and CAD. Tencent's official APEX-Agents v1.1 score is 75.2, just above Claude Opus 5.5's 74.8, with output priced at $2.501 per million tokens versus Opus's $20. On Terminal-Bench 4.0, however, Hy4 scores 44.4 against Opus and Kimi K3.1 Pro's ~66, a 20-point gap.

Tencent's own internal evaluations for office scenarios (83.18 average, 96.15% pass rate) and finance (3.04 blind-test score) are self-organized, not public benchmarks. Third-party Agent Arena ranks Hy4 14th—up from the preview's 25th—with a per-task cost of $0.55, about 60% of Kimi K3.1 Pro's $1.46 and a score within a point.

The API went live on Tencent Cloud and OpenRouter the same day, but model weights are only promised for "the coming days". The preview was open-weight on day one; the official release is API-first. Third-party hands-on tests found most tasks done efficiently but not flawlessly, and community feedback on the preview noted noticeable long-thinking latency.

Takeaways
— Hy4 has 770B total and 49B activated parameters, identical to the August preview; the upgrade is in pre/post-training scale, not architecture.
— New capabilities include visual understanding and Computer Use, with training data covering Blender, CAD, and other professional software.
— Tencent's APEX-Agents v1.1 score is 75.2, versus Claude Opus 5.5's 74.8 and DeepSeek-V4.1-Flash's 72.9.
— Output pricing is $2.501 per million tokens, roughly 1/8 of Claude Opus 5.5's $20 per million on OpenRouter.
— On Terminal-Bench 4.0, Hy4 scores 44.4 versus ~66 for Opus 5.5 and Kimi K3.1 Pro.
— Third-party Agent Arena ranks Hy4 14th (preview was 25th) with a per-task cost of $0.55, about 60% of Kimi K3.1 Pro's $1.46.
— Tencent's office scenario internal evaluation averaged 83.18 with a 96.15% task pass rate; finance blind tests scored 3.04 versus Kimi and GLM's 2.68.
— Hy4 is the same price as the preview: 6 yuan per million input tokens, 18 yuan per million output tokens.
— Weights are not yet released; Tencent says they will be open-sourced in the coming days, but no date is given.
Conclusions

Tencent's own benchmark numbers should be read as marketing for its strongest home turf, not impartial evidence; third-party ranking at 14th is a more sober check.

The real divider is the open-source cadence: the preview was open-weight immediately, while the official release is API-first, making every capability claim provisional until weights appear.

Cost-performance rivalry is becoming the deciding factor among Chinese open-source models, as Hy4, Kimi K3.1 Pro, GLM 5.3, and DeepSeek trade wins across benchmarks.

The weight-vs-open-source distinction matters more than ever: open weights do not mean a model is open source, and the community is already calling that out.

With a 20-point gap on Terminal-Bench, Hy4 is not closing the gap with closed frontier models on serious coding; its strengths lie in cheaper agent tasks and specific business workflows.

Tencent's bet is on its own ecosystem: WorkBuddy, CodeBuddy, Yuanbao, and WeChat Work integration, which is where enterprise value concentrates even if independent developers just want a cheap API.

Concepts & terms
Computer Use
A model capability that lets an AI operate a computer through command-line or graphical interfaces, including clicking, typing, and navigating applications. Hy4 adds this, with dedicated training on professional tools like Blender and CAD.
Open-weight model
A model whose trained parameters are publicly downloadable, but the training data, code, and often the full training process are not disclosed. Open weights are not the same as open source, a distinction the article explicitly highlights.
Source: juejin.cn ↗ Google Translate ↗ Backup ↗