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Hy3 preview
The first step in rebuilding Tencent Hunyuan model
Hy3 Preview is a 295B-parameter MoE model (21B active) from Tencent. Built on a completely rebuilt RL infrastructure, it delivers best-in-class cost efficiency and significant improvements in complex reasoning, instruction following, and agentic coding tasks.
This is a milestone release from Tencent. After rebranding their Hunyuan foundation models to the "Hy" series, pronounced like "hi", this is the first model shipped since they rebuilt their pre-training and RL infrastructure in February 2026.
The model itself is a 295B MoE with 21B active parameters, 256K context, and three reasoning modes for different latency / depth tradeoffs. The biggest stated gains are in complex reasoning, context learning, instruction following, coding, and agent tasks, with support for @OpenClaw, @opencode, and @Kilo Code agent workflows.
It probably does not directly challenge the current frontier model landscape yet. But it does show something important: a very large ship is turning toward a much more aggressive direction. Definitely worth watching.
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About Hy3 preview on Product Hunt
“The first step in rebuilding Tencent Hunyuan model”
Hy3 preview was submitted on Product Hunt and earned 7 upvotes and 1 comments, placing #44 on the daily leaderboard. Hy3 Preview is a 295B-parameter MoE model (21B active) from Tencent. Built on a completely rebuilt RL infrastructure, it delivers best-in-class cost efficiency and significant improvements in complex reasoning, instruction following, and agentic coding tasks.
Hy3 preview was featured in Open Source (68.4k followers), Artificial Intelligence (467.3k followers) and Development (5.8k followers) on Product Hunt. Together, these topics include over 104.2k products, making this a competitive space to launch in.
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Zac Zuo
Hi everyone!
This is a milestone release from Tencent. After rebranding their Hunyuan foundation models to the "Hy" series, pronounced like "hi", this is the first model shipped since they rebuilt their pre-training and RL infrastructure in February 2026.
The model itself is a 295B MoE with 21B active parameters, 256K context, and three reasoning modes for different latency / depth tradeoffs. The biggest stated gains are in complex reasoning, context learning, instruction following, coding, and agent tasks, with support for @OpenClaw, @opencode, and @Kilo Code agent workflows.
It probably does not directly challenge the current frontier model landscape yet. But it does show something important: a very large ship is turning toward a much more aggressive direction. Definitely worth watching.
The model is already open-sourced, and you can try it for free on OpenRouter until May 8.