Large language models (LLMs) with expanded context windows are revolutionizing applications like code analysis and long-form question answering. However, processing massive text inputs presents significant computational hurdles. A new benchmark called SCBench reveals surprising insights into how LLMs handle long contexts, especially when information is reused across multiple requests, as in multi-turn conversations. Traditional benchmarks evaluate LLMs on single requests, neglecting how they manage and reuse previously processed information. SCBench focuses on real-world usage by testing LLMs on tasks with shared contexts and multiple follow-up queries, mirroring how we interact with these models in chatbots and other applications. SCBench evaluates different techniques for optimizing long-context processing, such as sparse attention and memory compression. It assesses four key abilities: string retrieval, semantic retrieval, processing global information, and handling multiple tasks simultaneously. The research reveals that methods relying on limited memory struggle in multi-turn scenarios. In contrast, methods that preserve more information, even with higher initial processing costs, maintain better accuracy over multiple interactions. Specifically, sparse encoding methods, which process the full context initially, perform more consistently across multiple requests than methods that aggressively compress memory. This suggests that preserving a richer representation of the context, even at a higher upfront cost, can be more beneficial for handling complex, multi-turn interactions. This research sheds light on the importance of evaluating LLMs in scenarios that reflect real-world usage. It suggests that focusing solely on reducing processing costs for single requests may not be the most effective strategy. Instead, optimizing how LLMs manage and reuse information across multiple interactions is critical for building truly efficient and capable long-context models. The insights from SCBench are crucial for developing more effective strategies for optimizing long-context LLMs, paving the way for more efficient and responsive AI assistants and applications.