Qwen 3.8 27B is excellent, but it defaults to wildly overthinking things

Qwen 3.8 27B is an open-weights, vision-capable language model released by Alibaba's Qwen research lab. Coming in a 17GB file size, it is designed to run locally on well-equipped consumer hardware like laptops and workstations. The model supports a large context length of 262,144 tokens, handles tool-calling, and possesses strong vision and code generation capabilities.

A defining characteristic of the model is its official support for adjustable reasoning effort, which defaults to an extra-high setting. At this default level, the model tends to dramatically overthink simple prompts, consuming thousands of reasoning tokens and significant time to generate elaborate, over-engineered outputs. For instance, a basic request to draw an SVG circle resulted in a complex geometric study animation instead of a simple shape, and a request to generate an image-labeling tool led the model to invent its own built-in demo data. Turning down the reasoning effort or disabling it entirely yields faster results, though complex tasks like single-shot tool creation benefit from the reasoning trace.

In testing, the model successfully performed tasks such as accurately placing bounding boxes on photographs, driving coding agent loops, and writing functional Python code to convert session transcripts. However, a major drawback is its slow generation speed, averaging between 15 and 30 tokens per second on local machines. Performance can be improved using multi-token prediction features in server configurations like llama.cpp to speed up inference.

Ultimately, the model proves that a relatively small, open-weights file can perform complex coding, vision, and reasoning tasks locally without requiring expensive datacenter hardware.