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LatestTencentHy4-preview
TencentText / LLM

Tencent Previews Hunyuan Hy4, an Apache MoE Model

The company's next-generation Hunyuan language model arrives as an early preview with a permissive license and a mixture-of-experts design.

Aug 27, 2026
NotableApache 2.0
Hunyuan Hy4 (preview)

Tencent has quietly posted an early preview of its next-generation Hunyuan language model, Hy4-preview, on Hugging Face. The release marks the debut of the v4 line and arrives under the permissive Apache 2.0 license, which allows commercial use and modification without the restrictions that accompany many large open-weight models.

The model uses a mixture-of-experts (MoE) architecture and is positioned for both general text generation and reasoning tasks. Tencent has not published parameter counts, context-length figures, or benchmark numbers in the preview listing, so the model's exact scale and capabilities remain to be detailed.

Why it matters

MoE designs have become the dominant approach for scaling large models efficiently, activating only a subset of parameters per token to keep inference costs manageable. A permissively licensed preview from Tencent adds another serious entrant to a field where Chinese labs — including Alibaba's Qwen and DeepSeek — have been shipping increasingly capable open weights.

A few things worth noting about this release:

  • It is an explicit preview, suggesting a fuller v4 launch may follow with more documentation.
  • The Apache 2.0 license is unusually permissive for a flagship model line.
  • Key specifications are still absent, so independent evaluation will have to wait.

For now, developers can inspect the weights and configuration directly on the Hugging Face repository, though those hoping for official benchmarks or a technical report will need to wait for Tencent to say more about how Hy4 compares to its predecessors and rivals.

Sources

  • tencent/Hy4-preview

    Hugging Face

    Visit
OlderQwen-Drive 1.0 targets autonomous driving with a 4B VLMQwen · Alibaba · Vision-Language · 2 months agoNewerH Company's NeoMME rethinks visual document retrievalH company · Embeddings · last month

Get the model

Hugging Face

Specs

Context window1.0M tokens
Size1.6 TB
PrecisionBF16
ArchitectureHYV4ForCausalLM
LicenseAPACHE-2.0
Downloads25.5K
Likes504

Can you run it?

Datacenter only — about 469.7 GB even at 4-bit.

  • BF16 (as published)

    Multi-node cluster

    1.6 TB
  • 8-bit

    Multi-node cluster

    828.5 GB
  • 4-bit

    8×H100 80 GB node

    469.7 GB
Your machine
GGUF builds
  • Mixture-of-experts: every expert must be loaded, so memory follows total parameters, not the active slice.
  • KV cache sized for 8,192 tokens of context; longer prompts need proportionally more.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Text / LLMReasoning

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