Public benchmarks can be misleading due to overfitting and benchmark gaming.
We strongly recommend running your own evaluation on your
audio data to identify the best model for your use case.
Word error rate (WER)
Word Error Rate (WER) is the classical metric for speech-to-text accuracy. It counts substitutions, deletions, and insertions against a reference transcript, divided by the total word count in the ground truth. WER weights every word equally, so a misrecognized filler word counts the same as a misrecognized email or phone number. For voice agents, we recommend pairing WER with Missed entity rate, which measures accuracy on the high-stakes entities — names, emails, phone numbers, and medical terms — that break agent conversion in production.English benchmarks
Most recent update: March 2026.Multilingual benchmarks
Most recent update: March 2026.Missed entity rate
For voice agents, the words that break conversion are entities — emails, phone numbers, organizations, names, and medical terms. The Missed Entity Rate (MER) measures how often a model fails to correctly transcribe these high-stakes terms. See Missed Entity Rate for the full definition. Universal-3.5 Pro Streaming delivers double-digit relative gains over Universal Streaming across most categories, with the biggest improvements on emails, locations, and medical terms.
Source: AssemblyAI Streaming product page.
Latency
Most recent update: May 2026. These figures are measured using in-domain voice agent conversation data from our internal evaluation set. In production, actual latency will vary depending on audio characteristics, silence duration, and turn detection configuration. Latency claims referenced elsewhere in our documentation may reflect different evaluation conditions or measurement approaches. If you have questions about benchmarking methodology or need help interpreting these numbers for your use case, reach out to our team at support@assemblyai.com.Emission latency
Time from when a word is spoken to when that word is returned by the API. Best metric for use cases that consume partial transcripts in real time.
Universal-3.5 Pro Streaming emits segment-level partials rather than word-by-word, so the standard emission-latency metric doesn’t apply.
Time to Complete Turn (TTCT)
Time from end of speaker turn to receipt of the finalized transcript. Best metric for voice agent responsiveness.Time to First Token (TTFT) is a useful metric for LLMs but is not well suited to measuring real-time STT performance. For real-time STT, use emission latency (for partial transcript speed) and TTCT (for voice agent responsiveness).