Qwen3.8 is launching and going open-weight soon!🌐 With a massive 2.4T parameters, this model is continuously evolving. We believe it’s one of the most powerful model available today, compatible to leading frontier AI models , second only to Fable 5. You don't have to wait to test it. Just now, the Qwen3.8-Max-Preview made its debut on Alibaba’s Token Plan, Qoder, and QoderWork. Be among the very f...

Qwen 3.6 27B is a dense local model recommended for its powerful performance in general intelligence tasks. It is slower than the mixture-of-experts variant Qwen 3.6 35B A3B but is considered more effective for local development.

Rio-3.5-Open-397B is a 397B model that combines weights from Nex-N2_pro and Qwen3.5-397B-A17B in a ratio of 0.6 to 0.4. There is no evidence of independent training for this model, indicating it is a direct merge of existing models.

An RTX 5080 and RTX 3090 setup achieves over 80 tokens per second on the Qwen 3.6 27B Q8 model. The RTX 3090, with 24GB of memory, significantly enhances performance, allowing for initial speeds of 30 tokens per second, increasing to 50-60 tokens per second with MTP.

Qwen3.6-35B-A3B generated a superior image of a pelican compared to Claude Opus 4.7. The Qwen model was run on a MacBook Pro M5 using LM Studio.

Qwen Chat provides extensive capabilities, including chatbot functionality, image and video comprehension, image generation, document processing, web search integration, and tool utilization. The platform also supports the handling of various artifacts.

Alibaba's Qwen team has released the Qwen 3.5 family of open weight models. Junyang Lin, the lead researcher, announced his departure from the team via Twitter.

Key figures from Alibaba's Qwen AI team, known for their extensive contributions to open source generative models, have departed following the release of the Qwen3.5 small model series. The release received public acclaim from Elon Musk for its notable intelligence density.

Qwen3-Coder-Next is an open-weight language model designed for coding agents and local development, built on the Qwen3-Next-80B-A3B backbone. It features a sparse Mixture-of-Experts (MoE) architecture with 80 billion total parameters, activating only 3 billion parameters per token to optimize performance and reduce inference costs.