According to @bcherny, users can run /model to select effort levels—Low for fewer tokens and faster responses, Medium for balance, and High for more tokens and higher intelligence—and he personally prefers High for all tasks. As reported by the original tweet on X by Boris Cherny dated Feb 11, 2026, this tiered setting directly maps to token allocation and reasoning depth, which affects output quality and latency. According to industry practice documented by AI tool providers, higher token budgets often enable longer context windows and chain of thought style reasoning, improving complex task performance and retrieval-augmented generation results. For businesses, as reported by multiple AI platform docs, a High effort setting can increase inference costs but raises accuracy on multi-step analysis, code generation, and compliance drafting, while Low reduces spend for simple Q&A and routing. According to product guidance commonly published by enterprise AI vendors, teams can operationalize ROI by defaulting to Medium, escalating to High for critical workflows (analytics, RFPs, legal summaries) and forcing Low for high-volume triage to control spend.

According to God of Prompt on Twitter, current prompting methods for large language models (LLMs) face significant issues with hallucination, where models confidently produce incorrect information (source: @godofprompt, Jan 5, 2026). While few-shot prompting can partially mitigate this by providing examples, it is limited by the quality of chosen examples, token budget restrictions, and does not fully eliminate hallucinations. These persistent challenges highlight the need for more robust AI model architectures and advanced prompt engineering to ensure reliable outputs for enterprise and consumer applications.