This paper compares Retrieval Augmented Generation (RAG) and long-context (LC) LLMs, highlighting their trade-offs between performance and computational cost. While LC models outperform RAG in most cases, especially when resources are sufficient, RAG remains cost-effective due to its lower computational requirements. The study proposes SELF-ROUTE, a hybrid approach that dynamically routes queries to either RAG or LC based on model self-reflection. SELF-ROUTE significantly reduces computation costs while maintaining performance comparable to LC. Results show that SELF-ROUTE achieves performance similar to LC for most queries, with a substantial cost reduction. For example, it reduces costs by 65% for Gemini-1.5-Pro and 39% for GPT-4O. The study also identifies common failure patterns of RAG, such as multi-step reasoning and implicit queries, and discusses the trade-offs between cost and performance. The findings suggest that RAG is effective for many tasks, while LC excels in long-context scenarios. The study provides insights into the practical application of long-context LLMs and offers a framework for optimizing RAG techniques.This paper compares Retrieval Augmented Generation (RAG) and long-context (LC) LLMs, highlighting their trade-offs between performance and computational cost. While LC models outperform RAG in most cases, especially when resources are sufficient, RAG remains cost-effective due to its lower computational requirements. The study proposes SELF-ROUTE, a hybrid approach that dynamically routes queries to either RAG or LC based on model self-reflection. SELF-ROUTE significantly reduces computation costs while maintaining performance comparable to LC. Results show that SELF-ROUTE achieves performance similar to LC for most queries, with a substantial cost reduction. For example, it reduces costs by 65% for Gemini-1.5-Pro and 39% for GPT-4O. The study also identifies common failure patterns of RAG, such as multi-step reasoning and implicit queries, and discusses the trade-offs between cost and performance. The findings suggest that RAG is effective for many tasks, while LC excels in long-context scenarios. The study provides insights into the practical application of long-context LLMs and offers a framework for optimizing RAG techniques.