Best LLMs for Extracting Structured Data from Transcripts (JSON Output, Speed, and Cost)

Extracting structured information from spoken transcripts is more difficult than document extraction because speech-to-text errors, corrections, ambiguous names and dates, speaker attribution, cross-talk, and disfluencies can cause models to infer incorrect facts. Voice Standard Bench evaluates more than 50 LLM Gateway models on extracting participants, action items, decisions, and entities into a fixed JSON structure across 30-second, 2-minute, 15-minute, and 60-minute transcripts, scoring schema validity, groundedness, completeness, cost, and response time. As of October 28, 2026, leading performance models varied by length, with gpt-5.6-sol leading 30-second calls, gpt-6-luna leading 15-minute support calls, and gpt-6.1-sol leading 60-minute meetings, while low-cost and fast small models performed more competitively on short inputs but declined on longer contexts. The post recommends selecting models based on transcript length, latency requirements, schema strictness, and the consequences of hallucinated information; using strict JSON schemas, empty fields or “unspecified” values for absent information, output validation and retries, and speaker-labeled transcripts; and testing a shortlist against an organization’s own calls before deployment. For longer calls, it suggests extracting by chunks and merging confirmed facts or creating a grounded intermediate summary, while stable extraction needs may be served by a dedicated speech-understanding system instead of a configurable LLM workflow.

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