The Alibaba team has introduced the open-source Qwen3.8-27B model. In just two days, the model topped the global trend on the Hugging Face platform, surpassing one million downloads, including the FP8 version; the quantized Unsloth version approached three million. Developers who conducted testing dubbed it 'the local Opus 4.6,' as this model with under 30 billion parameters outperforms all models released four months ago, including Opus 4.6, which was compared to DeepSeek V4-Pro and GPT 5.6 Luna, making it the first local model to reach advanced development levels. In its quantized form, it operates using 17 GB of RAM.
The model is a natively multimodal dense model capable of understanding images and videos. It features a native context window of 262K, expandable up to 1 million, and is distributed under the Apache 2.0 license, allowing for commercial use.
When evaluated on coding and agent tasks, Qwen3.8-27B achieved a SWE-bench Pro score of 61.7 compared to 53.4 for Opus 4.6 Max. In the DeepSWE 1.1 test, it scored 42.2, approximately three times higher than its predecessor's score of 13.3. Results in office work and agent tasks are also high: CoWorkBench showed 70.7 versus 68.2 for Opus 4.6 Max, and JobBench recorded 33.4. The Pass@1 rate for agent exams was 20.4, nearly double that of the previous version (10.6). Furthermore, IFBench received 79.5, and LiveCodeBench v6 scored 90.3, the highest score in the table.
Multimodality is implemented natively in the architecture, enabling the processing of images and videos, ranging from STEM graphs to hourly videos. When tested on computer operations according to the OSWorld standard, the model scored 84.3, which is 11.6 points higher than Opus 4.6 Max. Phone operations on AndroidWorld are rated at 81.9, and browser operations tested by WebArena reached 64.8, an increase of 9.5 points over its predecessor. In the area of tool-free visual reasoning, MathVision showed 90.0 versus 65.5, BabyVision showed 65.7 versus 12.6, and CharXiv graph analysis showed 83.7 versus 66.0. The combination of the ability to read screenshots, click buttons, fill out forms, control browsers and phones, along with coding skills, forms a complete agent cycle for performing computer operations.
The 27B size is the most sought-after by the community because it allows the model to be run on laptops after quantization using consumer GPUs. The Unsloth version runs with 17 GB of RAM, and Atomic Dynamic GGUF compression reduces the size from 28.9 GB in 8-bit format to 8.5 GB in 1-bit. On the same day, tools such as SGLang, Ollama, and vLLM were released. Developers note the surprising possibility of having unlimited, free superintelligent devices right on their desktops: with only an RTX 5090, one can achieve intelligence equivalent to Opus 4.8. Simon Williamson shared that he hadn't enjoyed working with a local model for a long time. He noted that the default thinking mode is enabled but can be disabled upon request, and adjusting the reasoning_effort depth allows regulating this process; Williamson found that the xhigh setting makes the model think too much and advised starting with a low or absent level of reasoning.
Thus, Qwen3.8-27B raises coding and agent performance to the Opus level, providing control over weights, thought depth, and data security.
