Text Summarization Quality and Cost Explained — A Media Catalog Decision Guide

Choose a summarization model by measured catalog outcomes and tenant-level accounting, not by a universal quality ranking. A media catalog has short titles, duplicated metadata, rights notes, and occasionally a description that is really an HTML fragment; the best API is the one whose errors you can detect and whose usage you can charge to the right tenant.

Short answer: keep the model behind a provider-neutral interface, evaluate OpenAI, Claude, and Gemini on the same redacted catalog set, and record input tokens, output tokens, latency, retries, and validation results for every tenant. There is no honest quality-cost winner without that workload-specific record.

What exactly are we comparing?

The useful unit is not a single prompt. It is a catalog enrichment job with a contract. For each messy description, the service should return a bounded summary, preserved identifiers, and a reason when it refuses to infer a fact.…