Prompt Length Matters: Qwen 3.5 vs 3.6 and Gemma 4 Performance Differences

The author ran a comparative test on three models—Qwen 3.5, Qwen 3.6, and Gemma 4—using two prompt styles: a concise version and a longer, narrative version. Each combination was executed ten times to gather statistical consistency. Results showed that most incorrect answers assumed a flat $5 per box, yielding $150 instead of the correct $300, except for Qwen 3.6 IQ2 which sometimes ignored sibling boxes. Gemma 4 performed best on the long prompt, interpreting it as a business scenario with varying buying and selling prices, while Qwen 3.6 struggled with the long prompt even in the highest quality Q8 quant, often missing the business context or sibling boxes. The IQ2 quant surprisingly delivered accurate answers in both prompt lengths. The author highlights that similar models can require different prompting styles, underscoring the importance of prompt engineering for each LLM. The full dataset and token statistics are available on the author’s website, and the comparison tool can be replicated locally. This study suggests that prompt length and wording can significantly influence model accuracy across different architectures.