Introducing Hy4 Preview
Tencent released Hy4 Preview, an open-weight text-only LLM with 770B total parameters and 49B active parameters, significantly larger than its predecessor Hy3 The model supports a 1M token context window and is available as a 1.56TB download on Hugging Face Hy4 features two reasoning modes: "high" (default, chain-of-thought enabled) and "no_think" (reasoning disabled) This represents a major scaling leap from Hy3 (295B total / 21B active, 256K context, 598GB) released just one month prior The mo
Analysis
TL;DR
- Tencent released Hy4 Preview, an open-weight text-only LLM with 770B total parameters and 49B active parameters, significantly larger than its predecessor Hy3
- The model supports a 1M token context window and is available as a 1.56TB download on Hugging Face
- Hy4 features two reasoning modes: "high" (default, chain-of-thought enabled) and "no_think" (reasoning disabled)
- This represents a major scaling leap from Hy3 (295B total / 21B active, 256K context, 598GB) released just one month prior
- The model is text-input only with no vision capabilities
Why It Matters
Tencent's rapid iteration from Hy3 to Hy4 in under a month signals intensifying competition in the open-weight large language model space, particularly from Chinese AI labs. The 1M token context window positions Hy4 as a strong contender for long-context applications such as document analysis, codebase understanding, and extended reasoning tasks. The dual reasoning-mode design offers practitioners flexibility between performance and latency/cost trade-offs.
Technical Details
- Architecture: Mixture-of-Experts (MoE) design with 770B total parameters and 49B active parameters per token, enabling efficient inference despite massive scale
- Context Window: 1,000,000 tokens, a 4x increase over Hy3's 256K context, supporting extremely long document and conversation processing
- Reasoning Modes: Two explicit modes via
reasoning_effortparameter — "high" (default, full chain-of-thought reasoning) and "no_think" (direct response, no visible reasoning trace) - Model Size: 1.56TB checkpoint on Hugging Face, up from Hy3's 598GB, reflecting the substantial parameter and context scaling
- Input Modality: Text-only input with no vision capabilities, distinguishing it from multimodal competitors
- Chat Template: Uses Jinja2-based chat templates with strict validation on reasoning effort values, raising exceptions for invalid or missing inputs
Industry Insight
- The aggressive monthly iteration cycle (Hy3 in July → Hy4 in August) suggests Chinese AI labs are prioritizing rapid open-weight releases to capture developer mindshare, potentially pressuring Western labs to accelerate their own open model roadmaps
- The 1M token context window at open weight is a significant milestone for enterprise applications involving long documents, legal contracts, and full-codebase analysis — practitioners should evaluate Hy4 for long-context workloads where proprietary models previously dominated
- The explicit "no_think" mode reflects an emerging industry pattern: offering users direct control over reasoning overhead, balancing quality against speed and cost, which is increasingly important for production deployment scenarios
Disclaimer: The above content is generated by AI and is for reference only.