Qwen 3.8 vs 3.6 27b low reasoning loops way less now
Summary
This article compares Qwen 3.8 to 3.6, noting that Qwen 3.8 reduces reasoning loops on low settings and includes a preserve_thinking parameter to avoid redundant reasoning.
Similar Articles
Qwen 3.8 27B is excellent, but it defaults to wildly overthinking things
Qwen 3.8 27B is a powerful open-source 27B parameter vision-capable LLM from Alibaba's Qwen research lab, praised for its benchmarks but criticized for defaulting to excessive reasoning effort, which slows down performance on consumer hardware.
Qwen3.8-27B different thinking levels
The Qwen3.8-27B model is introduced with varying thinking levels, showing improved reasoning capabilities compared to previous versions like Qwen 3.7 plus and Qwen3.6-27B.
Unpopular opinion : Qwen 3.8 27b is not an overthinker
The article argues that Qwen 3.8 27b's increased reasoning token usage is similar to other Chinese AI models like GLM and DeepSeek, with user frustration stemming from hardware limitations. It suggests using a reasoning budget can maintain performance over Qwen 3.6.
Qwen 3.8 27B Overthinking, It has to be done, it has to be overthinking to punch Opus 4.6
The article discusses Qwen 3.8 27B, a 27B parameter model that uses extensive reasoning tokens to compete with larger models, emphasizing trade-offs in token usage and benefits for local deployment.
Tested in Coding: Q8_K_XL Qwen3.8 27B vs BF16 Qwen3.6 27B
A user's detailed comparison of Qwen3.8 and Qwen3.6 models in coding tasks, highlighting improvements in instruction following and tracing for Qwen3.8, but with inefficiencies in reasoning.