Tencent Hy

Tencent opens its 3D generation platform and API internationally: text, multi-view image or sketch to 3D, OBJ/GLB output for Unreal, Unity and Blender, 20 free generations a day for new creators and 200 API credits at signup. HY 3D Studio goes global alongside it.

Tencent open-sources Hy3 preview, a 295B/21B-active reasoning and agent model pitched on cost efficiency, with weights on Hugging Face and a time-boxed free window on OpenRouter while the team collects feedback ahead of the official release.

HPC-Ops is a production LLM inference operator library written in CUDA and CuTe, reporting 30% QPM gains on Tencent's own models and 17% on DeepSeek, with kernel speedups up to 2.22x over FlashInfer/FlashAttention, 1.88x over DeepGEMM and 1.49x over TensorRT-LLM on FusedMoE.

The interactive world model gets the pieces that make it usable outside a demo: training code you can point at your own data, a lite 5B checkpoint that fits small-VRAM GPUs, inference and memory optimizations for real-time interaction, and an open hosted app with no waitlist.

Tencent maps self-evolving agents onto an L0–L4 taxonomy and pairs it with a reliability ladder for deciding when a self-modification should be accepted, backed by an open catalog of 549 works. The core rule: an update must not control the evidence used to accept it.

Tencent's foundation model moves to a 406B/32B-active MoE with a 256K window, split into Think and Instruct variants on Tencent Cloud. The agentic jump is the headline: SWE-bench Verified 6.0→53.0 and Tau2-Bench 17.1→72.4.

An open-weight 770B MoE with 49B active parameters and a 1M-token window, shipped alongside mixed-bit GGUFs that compress 1.5TB to roughly 200GiB by letting calibration data pick each layer's bit-width, reaching 1.31-bit in places and 2.06-bit elsewhere.

Free access to Hy3 preview on OpenRouter has ended and the model reverts to paid pricing. Tencent reports it took 15.4% of provider token volume over the two weeks, with the top position in coding and tool-calling usage.

Hy4 preview becomes selectable in Cline, with 770B parameters, 49B active, a 1M-token context and an Apache-2.0 license. Cline reports it leading SWE-bench Pro and calls it Tencent's largest measured generational jump; setup is npm install, /model, select.

Tencent opens the 2B member of an embodied-agent foundation family aimed at spatial-temporal perception, prediction and planning, with a 32B sibling for heavier reasoning. Notable choices: a Mixture-of-Transformers split by modality, latent perception tokens, and on-policy distillation from the larg

A reasoning-oriented vision-language model, placed third on the arena leaderboard at announcement and callable through Tencent Cloud immediately, with the model release and technical report scheduled for late October.

A billion-parameter flow-matching DiT turning text into 3D character animation, released with weights, code and a technical report. Tencent claims the first motion model trained through a complete pretrain→SFT→RL loop, spanning 200+ motion categories.

GradLoc uses distributed binary search to pin a gradient spike to the exact culprit token in O(log N) with negligible overhead. It surfaced layerwise gradient heterogeneity, specifically tokens with safe importance-sampling ratios that still blow up individual layers, a problem addressed by LayerClip, a per-layer clipping method.

Tencent open-sources UniRL, an RL post-training stack running one generate-score-advantage-update loop across flow/diffusion models, LLMs, VLMs and unified autoregressive+diffusion models. Ships FSDP2 sharding, pluggable SGLang/vLLM-Omni rollouts, and two new algorithms: Flow-DPPO and DRPO.

Tencent released HunyuanImage 3.0-Instruct under open weights, publishing full and distilled checkpoints on Hugging Face plus a GitHub repo. The lab claims top-tier standing on Arena's Image Edit leaderboard for open-source image-to-image editing.

Quantization-aware training takes a 1.8B model to a 2-bit representation, roughly a 0.3B bit-equivalent footprint at 600MB, while keeping dual chain-of-thought reasoning. Reported prefill speeds are 3-8x faster on Apple M4 and Dimensity 9500, with 2-3x faster generation. The model ships as GGUF.

A training-ready 3D corpus: 252k+ filtered high-fidelity objects, 240k+ part-level decompositions for fine-grained control, and 125k synthesized assets for class balance, shipped with Hunyuan3D-2.1-Small as a lightweight reproducible baseline.

A 1B-parameter OCR model handling text spotting, table and formula extraction to HTML/LaTeX, video subtitle pull and 14-language photo translation in a single instruction and a single inference pass. It scores 860 on OCRBench among sub-3B models and 94.1 on OmniDocBench.

PolyGen 1.5 lands in Hunyuan 3D Studio with end-to-end native quad mesh generation instead of the tri-meshes earlier generators produced, learning quad topology directly for cleaner, continuous edge loops. Both quad and triangular output are supported.

The 30B-A3B translation model gets an official GGUF build, closing the gap between open weights and actually running them locally on llama.cpp-family runtimes. Tencent also names support across Apple MLX-LM, ONNX Runtime, NVIDIA NeMo and LLaMA-Factory.

Text, image or video in; navigable 3D worlds out as mesh, 3DGS and point clouds that import into Unity and Unreal. The stack is itemized: panorama initialization without camera metadata, VLM-plus-navmesh trajectory planning, latent keyframe expansion, and an engine-agnostic renderer.

Tencent open-sources Hy-MT2 translation models at 1.8B, 7B and 30B-A3B across 33 languages. The 1.8B variant, quantized to 1.25-bit with AngelSlim, fits in 440MB and runs locally on mobile chips at 1.5x the speed of Hy-MT1.5.

CL-bench measures whether models can apply knowledge that exists only in their context: 1,899 expert-curated tasks over 500 complex contexts, gated by 31,000+ validation rules. Ten frontier models complete 17.2% on average, which the authors call a bottleneck for real context-dependent work.

Tencent open-weights a translation pair sized for two very different deployments: a 1.8B model claiming a 1GB footprint and 0.18s latency per 50 tokens on consumer hardware, and a 7B model positioned against far larger general systems. Weights and code are public.