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Large language models handle general-purpose text generation, reasoning, and instruction-following across domains. Teams reach for LLMs when they need flexible natural language capabilities without task-specific fine-tuning — drafting content, answering questions, summarizing documents, or driving conversational interfaces. Context window size matters most: models that hold 32k+ tokens let you work with full codebases or long documents in a single pass, while smaller windows force chunking strategies that lose coherence. Choosing an LLM means balancing cost against output quality and speed. Smaller models respond faster and cost less per token, making them practical for high-volume tasks where good-enough accuracy suffices. Larger models produce more nuanced reasoning and handle complex instructions better, but burn through budget quickly. Test your actual prompts across price tiers before committing — the gap between adjacent model sizes often matters less than vendors suggest.