Tencent has open-sourced its Hy4 preview model, a 770B parameter LLM with a 1M context window. The model card for Hy4 admits to spending excessive time on complex tasks and over-verifying its work, despite strong benchmark results. An independent evaluation by the author of the article involved creating an agentic coding exam where Hy4 successfully implemented a refund feature after several iterations, demonstrating its ability to adhere to complex rules and integrate new functionality within an existing architecture. AI
IMPACT Tencent's Hy4 release offers a large context window and demonstrates capability in agentic tasks, potentially influencing future model development and evaluation methods.
RANK_REASON Frontier-lab model release with system card and independent evaluation. [lever_c_demoted from frontier_release: ic=1 ai=1.0]
- events.py
- GLM 5.3
- Hy4
- Kimi k3
- money.py
- orderflow
- pipeline.py
- REFUND_POLICY.md
- SPEC.md
- state.py
- Tencent
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