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

Alibaba's Qwen research lab released Qwen 3.8 27B, a 27-billion-parameter Apache 2.0 licensed AI model available in 17GB GGUF format that can run on high-end consumer laptops. The model supports vision, long contexts, and tool calling. The over-thinking problem Qwen's documentation describes the model as defaulting to xhigh for reasoning effort—a setting designed for complex tasks demanding thorough analysis. In practice, this causes the model to use its full 262,144 token context on even mun

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

Alibaba's Qwen research lab released Qwen 3.8 27B, a 27-billion-parameter Apache 2.0 licensed AI model available in 17GB GGUF format that can run on high-end consumer laptops. The model supports vision, long contexts, and tool calling.

The over-thinking problem

Qwen's documentation describes the model as defaulting to xhigh for reasoning effort—a setting designed for complex tasks demanding thorough analysis. In practice, this causes the model to use its full 262,144 token context on even mundane problems.

When asked to generate an SVG of a pelican riding a bicycle, the model used 22,276 reasoning tokens to produce 3,223 tokens of output, taking 21 minutes. The same prompt with reasoning disabled at low effort produced a functional result in just 137 seconds.

Strong performance markers

Vision tasks: Successfully returns accurate bounding boxes for object detection when prompted with 0-1000 scale coordinates.

Tool use: Ran coding agent loops effectively on both a 128GB M5 Max MacBook Pro and NVIDIA DGX Spark.

Code generation: Built a Python tool from a single prompt to convert JSONL transcripts to markdown.

Speed optimization discovered

Simon Willison found that the model supports Multi-Token Prediction (MTP), an architecture trick where a cheaper mechanism guesses multiple tokens ahead. Running with --spec-type draft-mtp on llama.cpp outperformed the default GGUF by roughly 72%.

The key insight

A 17GB open-weights general-purpose model with vision, long context, and strong tool calling capabilities can fit in a capable laptop. This demonstrates how quickly local AI models have progressed.

Sources

Simon Willison's Weblog: https://simonwillison.net/2026/Aug/16/qwen-38-27b/

Qwen 3.8 27B Hugging Face: https://huggingface.co/Qwen/Qwen3.8-27B

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