According to God of Prompt on Twitter, Anthropic's recent tests with Claude Sonnet 4 found that giving the AI model more reasoning time can actually degrade its performance, rather than improve it as previously assumed (source: @godofprompt, Jan 8, 2026). This challenges a widely held belief in the AI industry that extended reflection or step-by-step thinking leads to better output quality. The findings highlight the importance of optimizing AI models for effective, concise reasoning rather than simply increasing computation or context, which could have major implications for AI application design, especially in business-critical areas like customer service, financial analysis, and legal automation.

According to God of Prompt on Twitter, recent tests show that as reasoning token count increases during simple counting tasks with distractors, Claude's accuracy drops due to heightened sensitivity to irrelevant information. In contrast, OpenAI's o-series models maintain focus but tend to overfit to the specific problem framing, rather than being distracted. This highlights a divergence in reasoning approaches between leading AI models, with implications for task reliability and practical deployment in business applications that require consistent accuracy in data processing and reasoning under noise (source: God of Prompt, Twitter, Jan 8, 2026).