Reasoning Large Language Models (LLMs) are advancing with complex architectures like mixtures of experts, enhancing reasoning capabilities. These models excel at tackling complex tasks, enabling solutions to challenges previously out of reach. However, this advancement comes with a significant user experience challenge: increased response times. Reasoning models offer substantial benefits by providing detailed, accurate answers to intricate problems. They are invaluable for tasks such as debugging complex code, solving advanced mathematical problems, and navigating intricate dependencies. Despite their superior performance, reasoning models introduce latency due to additional computational steps required for complex reasoning. Users today expect immediate feedback, and any noticeable delay can be perceived as a weakness or inefficiency in the system. Balancing the need for quick responses with the demand for detailed, accurate information is crucial for the widespread adoption of reasoning models. One effective strategy to address this challenge is smart model routing, which dynamically selects the most appropriate model based on query complexity. Smart model routing ensures users receive a tailored balance of speed and intelligence, enhancing overall satisfaction while maintaining problem-solving quality. Integrating specialized tools like Daytona SDK can further mitigate UX concerns associated with reasoning models, providing flexibility in addressing challenges without compromising speed. The path forward involves integrating smart model routing with advanced toolsets to create a robust AI framework capable of adaptive responsiveness, scalable problem-solving, and enhanced user trust.