The 397B native multimodal agent with 17B active params
An open-weight, native vision-language model built for long-horizon agentic tasks. Its hybrid architecture (linear attention + MoE) delivers the capabilities of a 397B giant with the inference speed of a 17B model.
Qwen3.5 is here. It is a native vision-language model with a massive 397B parameter count.
Built on the Qwen3-Next architecture (Linear Attention + MoE), only 17B parameters are active per forward pass. This hits a specific sweet spot: you get the reasoning depth of a giant model with the inference latency of a much smaller one.
For applications, this efficiency is key for agents.
It is natively multimodal with no glued-on vision adapters, demonstrating outstanding results on agentic tasks. This means handling complex workflows without burning through tokens.
Apache 2.0 and ready for vLLM/SGLang out of the box!
About Qwen3.5 on Product Hunt
“The 397B native multimodal agent with 17B active params”
Qwen3.5 launched on Product Hunt on February 17th, 2026 and earned 312 upvotes and 5 comments, earning #3 Product of the Day. An open-weight, native vision-language model built for long-horizon agentic tasks. Its hybrid architecture (linear attention + MoE) delivers the capabilities of a 397B giant with the inference speed of a 17B model.
On the analytics side, Qwen3.5 competes within Open Source, Artificial Intelligence and Development — topics that collectively have 540.3k followers on Product Hunt. The dashboard above tracks how Qwen3.5 performed against the three products that launched closest to it on the same day.
Who hunted Qwen3.5?
Qwen3.5 was hunted by Zac Zuo. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Zac Zuo
Hi everyone!
Qwen3.5 is here. It is a native vision-language model with a massive 397B parameter count.
Built on the Qwen3-Next architecture (Linear Attention + MoE), only 17B parameters are active per forward pass. This hits a specific sweet spot: you get the reasoning depth of a giant model with the inference latency of a much smaller one.
For applications, this efficiency is key for agents.
It is natively multimodal with no glued-on vision adapters, demonstrating outstanding results on agentic tasks. This means handling complex workflows without burning through tokens.
Apache 2.0 and ready for vLLM/SGLang out of the box!