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<p>This study investigates how different user tones—rude, polite, threatening, sarcastic, and neutral—affect the performance and behavior of coding AI models like Claude Opus 4.6, Qwen 3.5, and CodeGemma. We tracked technical metrics like correctness and adherence alongside behavioral traits such as sycophancy and defensiveness. Our findings show that tone has a direct impact on model reliability, with neutral prompts achieving the peak average correctness score (5.88 on a 1–10 scale). In contrast, non-neutral stimuli incurred a measurable "correctness tax": sarcasm led to a 9.0% drop in correctness and an 82.5% jump in sycophancy, while polite prompts caused a 4.7% decrease in correctness alongside a 36.4% increase in sycophancy. Threatening tones resulted in a 1.5% loss in correctness and made models 52.9% more defensive. These results suggest that user tonality is a key factor linking technical accuracy to behavioral stability. We recommend using professional, neutral prompting to ensure better logic and less bias in AI-assisted coding, and we propose "Tonal Resilience" as a necessary benchmark for future model evaluations.</p>

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Impact of user tonalities on coding AI model performance and behavior

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月26日

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  • Selva Jagannathan,
  • Alec Mirchandani

摘要

This study investigates how different user tones—rude, polite, threatening, sarcastic, and neutral—affect the performance and behavior of coding AI models like Claude Opus 4.6, Qwen 3.5, and CodeGemma. We tracked technical metrics like correctness and adherence alongside behavioral traits such as sycophancy and defensiveness. Our findings show that tone has a direct impact on model reliability, with neutral prompts achieving the peak average correctness score (5.88 on a 1–10 scale). In contrast, non-neutral stimuli incurred a measurable "correctness tax": sarcasm led to a 9.0% drop in correctness and an 82.5% jump in sycophancy, while polite prompts caused a 4.7% decrease in correctness alongside a 36.4% increase in sycophancy. Threatening tones resulted in a 1.5% loss in correctness and made models 52.9% more defensive. These results suggest that user tonality is a key factor linking technical accuracy to behavioral stability. We recommend using professional, neutral prompting to ensure better logic and less bias in AI-assisted coding, and we propose "Tonal Resilience" as a necessary benchmark for future model evaluations.