Alibaba's Qwen team released open weights for the Qwen 3.8 model family on August 14, headlined by Qwen3.8-27B, a 27-billion-parameter multimodal dense model, and the larger Qwen3.8-2.4T-A95B, a Mixture of Experts model with 95 billion active parameters operating at what the team calls the Max level2,3.
The weights ship under the Apache 2.0 license and are available on Hugging Face and ModelScope. A hosted version with one million tokens of context will be available through Qwen Cloud, Alibaba's AI service.
Qwen3.8-27B natively handles up to 262,000 tokens of context and can scale to one million tokens using the YaRN method. The model processes images and videos — including diagrams, documents, and multi-hour video — alongside text. A flexible thinking mode is enabled by default but can be toggled per query.
According to the Qwen team, Qwen3.8-27B outperforms the larger Qwen3.7-Plus in coding and office tasks. The team also describes improved agent capabilities, stating the model plans more independently and completes tasks more reliably.
Qwen3.8 Max, the Qwen3.8-2.4T-A95B variant, uses a Mixture of Experts architecture with 95 billion active parameters and was introduced by Alibaba in early August as the company's largest AI model. Its native multimodal architecture supports text and visual processing across enterprise tasks.
Third-party tooling support arrived quickly. Unsloth announced that both Qwen3.8-27B and Qwen3.8-2.4T can now be run locally through its platform, with Qwen3.8-27B operable on 17GB of RAM via Unsloth Dynamic GGUFs1ctx. Fine-tuning for Qwen3.8-27B is also supported in Unsloth, and the team uploaded NVFP4 quantizationsctx. The Unsloth release included a series of bug fixes and improvements, among them a fix for single-AMD-GPU detection on Windows and a resolution for a PowerShell progress bar issue that had throttled installer downloads to 0.65 MB/s.
A preliminary model card appeared on Hugging Face ahead of the full release, listing sections for highlights, model overview, quickstart, and best practices, though community members noted benchmark results were not yet included at that stage5.
ANALYSIS The Apache 2.0 licensing of both the 27B dense model and the much larger 2.4T MoE variant represents a broad permissive release, placing few restrictions on commercial use. The rapid Unsloth integration — enabling local inference on 17GB of RAM — lowers the hardware barrier for developers evaluating the model outside cloud environments. Releasing open weights for a model the team positions above its own Qwen3.7-Plus on coding and office benchmarks puts competitive pressure on other open-weight model families at comparable parameter counts.