Question

Your team is building a document-summarization feature in Azure AI Foundry. Source documents routinely exceed 60,000 tokens and must be summarized in a single pass. The team wants the lowest-cost model available in the model catalog that can accept the full document without chunking. Which criterion should drive your model selection?

Accepted Answer

Choose the model with the largest maximum context window that can hold the full document in one request, then compare per-token pricing among qualifying models.

Answer

Choose the model with the highest MMLU benchmark score regardless of its context window size.

Answer

Choose any chat model and enable provisioned throughput units to expand its context window.

Answer

Choose the model with the fastest time-to-first-token latency, since summarization is latency sensitive.