Qwen 3.8 27B, a new Apache 2 licensed vision-capable LLM from Alibaba, delivers impressive benchmark gains over its predecessor and even the closed-weight Qwen 3.7-Plus, but ships with a default 'xhigh' reasoning effort that leads to absurd overthinking—taking 21 minutes and 22,276 reasoning tokens to draw a simple pelican SVG, or turning a request for a plain circle into an elaborate animated artwork. Testing on an M5 Max MacBook Pro and an NVIDIA DGX Spark shows the model excels at bounding-box detection and can drive coding agents like Pi reasonably well, but inference speed (15-30 tokens/sec) lags well behind hosted API models. Running the model with llama.cpp's Multi-Token Prediction (MTP) support gave a roughly 72% speed boost over the default LM Studio GGUF. The strong recommendation is to disable or lower the default reasoning effort when running the model.
Questions this post answers
What reasoning effort does Qwen 3.8 27B default to and why is that a problem?
Qwen 3.8 27B defaults to 'xhigh' reasoning effort, which causes extreme overthinking even on trivial prompts. A request to draw a simple SVG circle produced pages of reasoning about palettes and animation before returning something entirely different from what was asked, and a pelican SVG took 21 minutes and 22,276 reasoning tokens. Running with reasoning off or on 'low' is recommended, cutting the same pelican task down to about 137 seconds. Anyone tuning local LLM reasoning settings can track real-world model quirks like this on daily.dev.
How much faster does Qwen 3.8 27B run with llama.cpp Multi-Token Prediction compared to the default LM Studio GGUF?
Using llama.cpp's Multi-Token Prediction (MTP) support with the flags --spec-default and --spec-type draft-mtp gave roughly a 72% speed improvement over the default LM Studio Q4_K_M GGUF build, tested on an NVIDIA DGX Spark with a comparative benchmark run by GPT-5.6 in Codex. MTP works by having a cheaper mechanism guess several tokens ahead that the main model then verifies. Developers optimizing local model inference speed can follow tricks like this via daily.dev.
Can Qwen 3.8 27B run as the model behind a coding agent like Pi?
Yes, Qwen 3.8 27B successfully drove the Pi coding agent, answering questions about a codebase's authentication setup and writing working Python code to convert a JSONL transcript to markdown after being configured as a custom OpenAI-compatible provider pointing at an LM Studio server shared via tailscale serve. Pi was chosen for its shorter system prompt, which suits smaller local models better. Developers evaluating small local models for agentic coding workflows can follow reports like this on daily.dev.