# qwen 61

Published articles for qwen 61.

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

DevFeed: [Qwen 3.8 27B is excellent, but it defaults to wildly overthinking things](<https://devfeed.tech/articles/qwen-3-8-27b-is-excellent-but-it-defaults-to-wildly-overthinking-things-30498.md>)

Original publisher: [Read original article](<https://simonwillison.net/2026/Aug/16/qwen-38-27b/>)

Author: Simon Willison

Published: 2026-08-16T22:00:39Z

Content type: opinion

Language: en

Sources: [Simon Willison](<https://devfeed.tech/sources/simon-willison.md>)

Topics: [qwen](<https://devfeed.tech/topics/qwen.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>), [DGX Spark](<https://devfeed.tech/topics/dgx-spark.md>), [NVIDIA DGX](<https://devfeed.tech/topics/nvidia-dgx.md>), [SVG](<https://devfeed.tech/topics/svg.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-2-235](<https://devfeed.tech/tags/ai-2-235.md>), [ai-in-china](<https://devfeed.tech/tags/ai-in-china.md>), [ai-in-china-108](<https://devfeed.tech/tags/ai-in-china-108.md>), [coding-agents](<https://devfeed.tech/tags/coding-agents.md>), [coding-agents-248](<https://devfeed.tech/tags/coding-agents-248.md>), [cost](<https://devfeed.tech/tags/cost.md>), [dgx-spark](<https://devfeed.tech/tags/dgx-spark.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [generative-ai-1-981](<https://devfeed.tech/tags/generative-ai-1-981.md>), [llama-cpp](<https://devfeed.tech/tags/llama-cpp.md>), [llama-cpp-29](<https://devfeed.tech/tags/llama-cpp-29.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llm-reasoning](<https://devfeed.tech/tags/llm-reasoning.md>), [llm-reasoning-103](<https://devfeed.tech/tags/llm-reasoning-103.md>), [llm-release](<https://devfeed.tech/tags/llm-release.md>), [llm-release-231](<https://devfeed.tech/tags/llm-release-231.md>), [llms](<https://devfeed.tech/tags/llms.md>), [llms-1-947](<https://devfeed.tech/tags/llms-1-947.md>), [lm-studio](<https://devfeed.tech/tags/lm-studio.md>), [lm-studio-23](<https://devfeed.tech/tags/lm-studio-23.md>), [local-llms](<https://devfeed.tech/tags/local-llms.md>), [local-llms-164](<https://devfeed.tech/tags/local-llms-164.md>), [nvidia-dgx](<https://devfeed.tech/tags/nvidia-dgx.md>), [nvidia-spark](<https://devfeed.tech/tags/nvidia-spark.md>), [nvidia-spark-6](<https://devfeed.tech/tags/nvidia-spark-6.md>), [pelican-riding-a-bicycle](<https://devfeed.tech/tags/pelican-riding-a-bicycle.md>), [pelican-riding-a-bicycle-142](<https://devfeed.tech/tags/pelican-riding-a-bicycle-142.md>), [pi](<https://devfeed.tech/tags/pi.md>), [pi-6](<https://devfeed.tech/tags/pi-6.md>), [qwen](<https://devfeed.tech/tags/qwen.md>), [qwen-61](<https://devfeed.tech/tags/qwen-61.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [speed](<https://devfeed.tech/tags/speed.md>), [svg](<https://devfeed.tech/tags/svg.md>)

### AI overview

Simon Willison evaluates Qwen 3.8 27B, a vision-capable 27-billion-parameter LLM that can run locally on suitable hardware. He finds that its default xhigh reasoning setting consumes substantial context and time, while adjusting the reasoning effort and increasing the context limit improves practicality. He also reports strong results generating an SVG locally.

### Source excerpt

Friday's big release was Qwen 3.8 27B, an Apache 2 licensed 27B parameter vision-capable LLM from Alibaba's Qwen research lab. I've been looking forward to this one: 27B is an excellent size for running a model on a reasonably specced laptop, and its predecessor Qwen 3.6 27B was impressive. Qwen's self-reported benchmarks for this model are eye-opening. They show a boost from both Qwen 3.6 27B and the closed-weight Qwen 3.7-Plus, which was one of Qwen's strongest models of any size as recently as May this year. It will be interesting to hear what independent benchmarks have to say about the model. I've been running the model on two different machines: my 128GB M5 Max MacBook Pro, and an NVIDIA DGX Spark. On both machines I'm running LM Studio and their 17GB Q4_K_M quantized build. I also tried using llama-server directly on the Spark. The default of extra high results in spectacular over-thinking Qwen's documentation describes the model as defaulting to xhigh for the reasoning effort, and the LM Studio GGUF I've been trying preserves that default: Qwen3.8 comes with official support for reasoning_effort, which can be used to adjust reasoning depth and control cost: xhigh (default): for complex tasks demanding thorough analysis medium: balancing accuracy and speed low: efficient reasoning optimizing for speed and cost This is a hilarious default. It's absolutely not a good way to run the model, especially on consumer hardware. I've been finding the results extremely entertaining. I quickly ran into problems with LM Studio's default context limit of 8,192 tokens - Qwen was using them all up thinking about even the most mundane of problems. I loaded the model with the full 262,144 maximum context length and that problem went away. Here's the pelican riding a bicycle SVG I got from my first attempt with that increased context length. It took 21 minutes to generate, using 22,276 reasoning tokens to produce 3,223 tokens of output. You can read the reasoning trace here. Th