Qwen3.5 397B A17B (Non-reasoning)

Qwen3.5 397B A17B is a large-scale multimodal language model developed by Alibaba, representing the first open-weight release in the Qwen3.5 series. It utilizes a sparse Mixture-of-Experts (MoE) architecture containing 397 billion total parameters, with 17 billion parameters active during each forward pass. This version is designed for high-efficiency direct responses, prioritizing throughput and conciseness by bypassing the internal chain-of-thought processing seen in reasoning-centric configurations.

Technically, the model features a hybrid architecture that integrates Gated Delta Networks (linear attention) with traditional gated attention and sparse MoE layers. This design is optimized for efficiency and long-context performance, offering a native context window of 262,144 tokens, which can be extended for tasks requiring up to one million tokens. As a native vision-language model, it employs early fusion training to process text, image, and video inputs natively within a unified framework, achieving parity with dedicated multimodal systems.

The model was trained using large-scale reinforcement learning (RL) across millions of agentic environments, focusing on the difficulty and generalizability of real-world tasks. It supports 201 languages and dialects, offering expansive global accessibility and cultural understanding. Released under the Apache 2.0 license, Qwen3.5 397B A17B is intended for production-grade agentic workflows and complex instruction-following tasks, where it provides significantly faster decoding speeds than previous generations of the Qwen series.