Optimizing LLMs for Text Summarization

Text summarization remains one of the highest-volume production workloads for LLMs. Whether you are condensing legal discovery documents, medical transcripts, or engineering logs, the challenge is not simply generating a shorter version. It is preserving factual accuracy, maintaining narrative coherence, and doing so across tens of thousands of tokens without breaking the inference budget. Token-based scaling means cost grows linearly with input length, which makes long-document summarization expensive and unpredictable. Oxlo.ai uses a flat per-request pricing model, so sending a 100,000-token deposition costs the same as a one-turn greeting. That structural difference changes how you design summarization pipelines.

Matching the Model to the Context Size

The first optimization is choosing a model whose context window