Qwen3.8-Flash-Next is a 125B multimodal MoE with only 6B active parameters and a new architecture built around QSA, Gated Residual, N-gram embeddings, and Muon. Its open weights give an early look at the architecture Qwen is building toward Qwen4.
Qwen3.8-Max is Qwen’s most capable model to date, a 2.4T-parameter MoE with 95B active parameters, 1M context, and multimodal agent capabilities for coding, research, cowork, and long-horizon tasks.
Qwen just released the Qwen3.5 Small Model Series — 0.8B, 2B, 4B and 9B. Native multimodal with improved architecture and scaled RL. 0.8B and 2B are tiny and fast for edge devices, 4B makes a strong lightweight agent base, and 9B is already closing the gap with much larger models. Base versions released too.
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.
Qwen-Image-2512 is the new open-source SOTA for text-to-image generation. It delivers drastically improved photorealism, finer natural details, and superior text rendering.
Qwen3.6-35B-A3B is a highly efficient open-source MoE model with 35B total and just 3B active parameters. It delivers frontier-level agentic coding and multimodal reasoning, rivaling much larger dense models. Apache 2.0 licensed and available now.
Qwen3.6-27B is a fully open-source dense model that punches way above its weight. Surpassing the previous 397B MoE flagship in agentic coding, it supports multimodal reasoning and thinking modes while remaining perfectly sized for local self-hosting.
Qwen3.6-Plus is Qwen’s latest hosted model with a 1M context window, major gains in agentic coding, stronger multimodal reasoning, and much tighter support for real development workflows across tools like OpenClaw, Claude Code, and Qwen Code.
Qwen3.6-Max-Preview is an early release of Qwen's next proprietary flagship. It delivers measurable improvements over Qwen3.6-Plus in agentic coding, world knowledge, and instruction following, securing top scores across major development benchmarks.
Qwen-Image-Layered decomposes images into transparent RGBA layers, unlocking inherent editability. You can move, resize, or delete objects without artifacts. Supports recursive decomposition and variable layer counts.
