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764 calls across 8 models: too much detail kills small models, filler words are load-bearing, and format preference is a myth
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764 calls across 8 models: too much detail kills small models, filler words are load-bearing, and format preference is a myth

764 次呼叫、8 個模型的真相：細節太多反而害小模型，廢話其實很重要，格式偏好根本是迷思

I wanted to know if the prompting advice you see everywhere, be specific, add examples, use XML tags, actually works on small local models. So I ran 764 calls across 8 models, 6 local on M2 96GB and RTX 5070 Ti via Ollama, and 2 frontier APIs (GPT-4.1-mini and Claude Haiku 4.5) for cross-validation. Total API cost was $0.03. Three findings that changed how I prompt local models. First, too much detail hurts small models. I tested the same task content at four levels of structural complexity.

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Keywords

prompt engineeringsmall modelslocal modelsLLM behaviormodel comparisonprompting techniquesXML tagsexamples

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