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Large language models handle text generation, analysis, and reasoning tasks — from drafting content and answering questions to extracting structured data from unstructured sources. Teams reach for LLMs when they need natural language understanding at scale, whether that's customer support automation, document processing, or building conversational interfaces. The technical attribute that separates useful models from mediocre ones is context window size: models with 100K+ token windows let you process entire documents or long conversation histories without chunking, while smaller windows force you into retrieval patterns that add latency and complexity. Choosing an LLM means balancing cost against quality and speed. Smaller models (under 10B parameters) run fast and cheap but struggle with complex reasoning or nuanced tone. Larger models deliver better output but cost more per token and add response latency. If your use case tolerates a two-second wait and needs reliable accuracy, pay for a frontier model. If you're processing high volumes of simple requests where speed matters more than perfection, start with a smaller option and test whether quality holds.

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