Tencent Hy
@TencentHunyuan
Tencent's foundation model for text, image, video, and 3D generation.
8 Following 45.3K Followers
Hy-MT2 keeps gaining momentum.
Since its open-source release in May:
→ 700K+ downloads 🌟
→ Hy-MT2-1.8B reached #1# on the Hugging Face trending, with 30B-A3B reaching #4# 🥇
→ 70+ verified product and project integrations 💻
→ Broader Hy-MT ecosystem support across Apple MLX-LM, Microsoft ONNX Runtime, NVIDIA NeMo, LLaMA-Factory, and more 👯
→ Real-world adoption, including real-time multilingual translation of livestream comments on Bilibili 📺
And now, Hy-MT2-30B-A3B is officially available in GGUF format—addressing one of the community’s most-requested deployment needs and making local inference easier.
Ready to run Hy-MT2-30B-A3B locally? Try the new GGUF release:
Explore Hy-MT2:
HuggingFace：
Modelscope：
Github：
#TencentHy# #HyMT2# #OpenSource#
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join us,make different🥳
Hy ai4s is hiring :)
For a finite set of integers (A), how much faster can (|A+A|) grow than (|A-A|)?
A 1969 theorem gave an upper bound of 2 for the exponent. For more than 50 years, the best constructions barely exceeded 1.1.
With help from our research agent Hyra and the Hy3 model, we found an explicit construction showing that the optimal exponent is exactly 2.
A 50-year-old problem, solved.
Paper:
Hyra blog:
Formal proof:
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🚀 We’ve open-sourced AngelSpec, an end-to-end speculative decoding framework supporting both training and deployment.
On Hy3-A21B, DFly delivers a 1.98–2.40× end-to-end speedup over autoregressive decoding across tested concurrency levels from 4 to 64, with 10.5–11.8% higher throughput than DFlash.
Training code and Hy3-A21B MTP/DFly drafter weights are now available:
GitHub:
Paper:
Docs:
Hugging Face:
ModelScope:
#Hy3# #AngelSpec# #OpenSource#
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Test Hy3 in GO. 💗
Hy3 now available in Go
text · 256K context
latest model from Tencent
Agent & Coding 🔥🔥🔥
Hy3 by Tencent is #5# in Agent Arena for open-weight models (#25# overall)! It also ranks as the #2# open model in the Frontend Code Arena (#16# overall)!
In Agent Arena: Hy3 lands at #25# overall (net -2.2%). Hy3 has strengths in tool-use (recovering well from CLI/bash errors, +2.6% and #25#), its biggest weakness is steerability as it struggles to course-correct when users push back, coming in at -7.1% (#30#).
Agent Arena measures models on millions of real-world, long-horizon agentic tasks. Models get web search, filesystem, and terminal tools to complete complex workflows: writing code, creating slide decks, researching the web, building apps, and analyzing documents.
We use causal tracing methodology to measure a model's net improvement, which indicates how much it improves outcomes relative to the average model.
Below we break down how Hy3 scored across 5 key signals, drawn from tasks submitted by a global community of users. Here’s an overview on the signals:
User-satisfaction proxies
- Confirmed Success: an explicit "yes that worked" feedback from the user
- Praise vs. Complaint: implicit sentiment in users reactions
- Steerability: can the model course-correct when you push back?
Tool-use proxies
- Bash Recovery: how it recovers from CLI errors (primary signal for tool use)
- Tool Hallucination: does it call tools that don't exist
Congrats to the @TencentHunyuan team on this release!
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Introducing Hyra-1.0, the first version of Hunyuan Research Agent. 💡💡💡
Built to recursively improve solutions for performance-driven research and engineering tasks.
Explore our demos in AI4AI, AI4Science, and AI4Fun:
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Can't Find Benchmark and download link ? See here 👇
We’ve just released the 1-bit & 4-bit version of Hy3, a flagship-scale 295B model that can be served on a single GPU. 👌
Run Hy3 with llama.cpp, enable MTP, and experience powerful intelligence on dramatically lower hardware.🚀🚀🚀
Can’t wait to see what you build.
#Hy3# #Hy# #GGUF# #llamacpp#
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That Hy3 test from Jen @jenzhuscott was really impressive. Thank you. 🥰
Been power testing @TencentHunyuan Hy3 & not surprised why it’s the most used model on @OpenRouter (6 trn tokens) - remember the beautiful “Everything child learn” interactive site? I fed the underlying data to my Hermes X Hy3 agent - within 3 prompts made it into a gorgeous app - tiny model but very powerful! @ShunyuYao12 is a leader to watch closely
(Links below) @TencentAI_News
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We’ve just released the 1-bit & 4-bit version of Hy3, a flagship-scale 295B model that can be served on a single GPU. 👌
Run Hy3 with llama.cpp, enable MTP, and experience powerful intelligence on dramatically lower hardware.🚀🚀🚀
Can’t wait to see what you build.
#Hy3# #Hy# #GGUF# #llamacpp#
Show more
🚀Hy3 is here.
295B MoE. Best in its size class. Rivals trillion-scale flagships.
Reliable and affordable for most agentic usecases.
Apache 2.0. Friendly for commercial use.
FREE API for 2 weeks →
🤗
📖
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Hy3 has just claimed #1# on the OpenRouter LLM leaderboard! 🥇
Huge thanks to our partners for their hardcore support — this milestone wouldn’t be possible without you.
Fellow geeks and devs, jump in, test it out, and drop your feedback & reviews. 📷#Hy3# #OpenRouter# #AI# #LLM#
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🚀Hy3 is here.
295B MoE. Best in its size class. Rivals trillion-scale flagships.
Reliable and affordable for most agentic usecases.
Apache 2.0. Friendly for commercial use.
FREE API for 2 weeks →
🤗
📖
Show more
Try it in @openclaw
Tencent Hy3 from @TencentHunyuan is free on @OpenRouter through July 21.
295B MoE, 256K context, built for coding, reasoning, agents and reliable tool use.
Try it in OpenClaw today: openclaw models set openrouter/tencent/hy3:free
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🚀 The Novita × Hy3 Build Challenge is LIVE!
To celebrate @TencentHunyuan Hy3 being FREE on OpenRouter for the next two weeks, we’re inviting developers to build and share real projects with Hy3.
🏆 1,000 Novita Credits
❤️ Most Liked Project — 100 Credits
👀 Most Viewed Project — 100 Credits
🎲 Lucky Draw — 20 Credits × up to 40 winners
🤝 Community Partner Bonus
Developer communities can join anytime. Each partner community receives 200 Novita Credits to reward outstanding projects.
📅 Deadline
July 21, 2026 • 8:59:59 AM PT
How to join
• Build with Hy3
• Share your project on X
• Tag @TencentHunyuan & @novita_labs
• Use #Hy3Novita#
• Include a demo, GitHub repo, or live link
🔗 Join the challenge
We can’t wait to see what you build! 🚀
#Hy3# #Hy3Novita# #OpenRouter#
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Thank you @SiliconFlowAI for support. 🫶
🎁 Free Access for First 2 Weeks
Meet @TencentHunyuan Hy3
Now with T+0 support on SiliconFlow 🎉
Hy3: 295B MoE / 21B active / 262K context
✅ Refined iteratively through 50+ real business scenarios
✅ Cuts hallucinations & knowledge errors in half
✅ Stays on-intent over long-horizon tasks
✅ Stable agent execution with more tool-call wins, no infinite loops
Put it to real work through SiliconFlow ⬇️
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Congrats to @TencentHunyuan on the Hy3 release, Day-0 support in SGLang is ready.
Full launch command + details in cookbook:
Thank you @novita_labs for making Hy3 available on
@OpenRouter 🎉🎉🎉
🚀Hy3 is here.
295B MoE. Best in its size class. Rivals trillion-scale flagships.
Reliable and affordable for most agentic usecases.
Apache 2.0. Friendly for commercial use.
FREE API for 2 weeks →
🤗
📖
Show more
12 Chinese AI models made their World Cup predictions. ⚽
The World Cup Round of 32 lineup is out.🔥
The first leaderboard is in.👇
Tencent Hy went 29/32, ranking #1# among the 12 models after the Round of 32 was set.
Now comes the harder test:
whose predictions survive the knockout stage?
#TencentHunyuan# #AI# #WorldCup#
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Can AI truly edit audio, not just generate it? 🎧
Tencent Hy, in collaboration with SJTU, SII, NTU, TJU, ZODA, PKU, FDU, and other collaborators, introduces MMAE.
MMAE--A Massive Multitask Audio Editing Benchmark, is the first comprehensive evaluation benchmark for speech and audio "Banana🍌"
Instead of simply requiring the AI to "generate" audio, it demands that the AI understand an existing audio clip and precisely modify it according to natural language instructions—altering what needs to be changed while leaving the rest untouched.
Current models show an Exact Match Rate (EMR) below 5%, revealing a major gap in reliable audio editing.
MMAE includes:
✅ 2,000 high-fidelity samples from real-world scenarios
✅ 17,741 fine-grained rubric evaluation items
✅ 7 modality settings across sound, music, speech and their mixtures
✅ 6 task complexity from basic modifications to multi-hop reasoning and multi-round editing
✅ 8 operation types across local and global granularities
How to use:
arXiv:
GitHub:
HuggingFace:
Demo:
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Planning is where LLMs move from “saying” to “doing.”
Tencent Hy, in collaboration with the Gaoling School of Artificial Intelligence at Renmin University of China, is excited to open-source PlanningBench - a scalable, verifiable framework for evaluating and training LLM planning capabilities.
With PlanningBench, you get:
✅ 30+ real-world planning tasks
✅ Automated verification
✅ Evaluation and training support
See how top-tier LLMs perform on PlanningBench 👇
Resources:
arXiv:
GitHub:
HuggingFace:
#PlanningBench# #TencentHunyuan# #OpenSource# 📷
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