Voice AI BenchmarksBy

PROVIDERLAST 30 DAYS

official resource

AssemblyAI voice AI models and benchmarks

AssemblyAI (Assembly AI, assemblyai.com) lists 2 STT models in Coval. Its fastest dated STT result is Universal 3.5 Pro at mean 173 ms time to final segment (13th of 28) among STT systems, with 3.1% WER. Results cover the last 30 days. Last measured .

AssemblyAI builds managed speech recognition APIs.

Measured models
2
STT
Best STT model#13 / 28
173ms
Universal 3.5 Pro

Overview

AssemblyAI builds and serves its own endpoints, with multilingual, diarization and keyterm capabilities.

Every model below is measured daily on the same fixed inputs and ranked against the full field, never blended into a company score.

Model lineup

Speech-to-Text
Universal Streaming, Universal 3.5 Pro

Speech-to-Text

Full STT dashboard

Ranked on Time to Final Segment against 28 measured models.

Time to Final Segmentms · lower is better · AssemblyAI models markedEvery measured STT model on Time to Final Segment, with AssemblyAI's models highlighted.
  1. #1Qwen3 ASR 1.7b39 ms
  2. #2Qwen3 ASR Fast46 ms
  3. #3STT RT v557 ms
  4. #4STT 165 ms
  5. #5Parakeet TDT 0.6B v381 ms
  6. #6Nova 389 ms
  7. #7Nova 292 ms
  8. #13Universal 3.5 Pro173 ms
leaders plus AssemblyAI models · 20 other models in the full table
Word Error Rate% · lower is better · AssemblyAI models markedEvery measured STT model on Word Error Rate, with AssemblyAI's models highlighted.
  1. #1Universal 3.5 Pro3.1%
  2. #2Qwen3 ASR Fast3.2%
  3. #3resonant-13.4%
  4. #4Gemini 3.5 Transcribe Live3.9%
  5. #5Chirp 34.1%
  6. #6Qwen3 ASR 1.7b4.1%
  7. #7Enhanced4.3%
  8. #23Universal Streaming6.9%
leaders plus AssemblyAI models · 22 other models in the full table
Time to First Tokenms · lower is better · AssemblyAI models markedEvery measured STT model on Time to First Token, with AssemblyAI's models highlighted.
  1. #1Whisper Large v3via Baseten912 ms
  2. #2Qwen3 ASR 1.7b931 ms
  3. #3Universal 3.5 Pro1041 ms
  4. #4Flux1076 ms
  5. #5Flux Multilingual1164 ms
  6. #6Parakeet TDT 0.6B v31215 ms
  7. #7Whisper Large v3via Together AI1338 ms
  8. #13Universal Streaming1514 ms
leaders plus AssemblyAI models · 18 other models in the full table
TTFS vs WER — AssemblyAI vs every measured modelEach point is one measured STT model · AssemblyAI models highlighted · 30-day averagesTime to Final Segment against Word Error Rate for every measured STT model, with AssemblyAI's models highlighted.
Qwen3 ASR 1.7b — 39 ms, 4.1%Qwen3 ASR Fast — 46 ms, 3.2%STT RT v5 — 57 ms, 5.7%STT 1 — 65 ms, 4.4%Parakeet TDT 0.6B v3 — 81 ms, 11.2%Nova 3 — 89 ms, 6.2%Nova 2 — 92 ms, 7.9%Flux Multilingual — 98 ms, 8.8%Flux — 99 ms, 6.7%Ink 2 — 122 ms, 4.8%Whisper Large v3 via Baseten — 125 ms, 5.6%Scribe v2 Realtime — 133 ms, 5.5%Grok STT — 207 ms, 4.6%Default via Speechmatics — 209 ms, 5.4%Pulse — 212 ms, 5.3%Default via Gradium — 246 ms, 10.0%resonant-1 — 264 ms, 3.4%Whisper Large v3 via Together AI — 295 ms, 8.5%Enhanced — 299 ms, 4.3%Gemini 3.5 Transcribe Live — 305 ms, 3.9%Voxtral Mini Transcribe Realtime 2602 — 401 ms, 6.2%GPT Realtime Whisper — 551 ms, 5.1%GPT-4o mini Transcribe — 690 ms, 4.9%GPT-4o Transcribe — 754 ms, 4.6%Chirp 3 — 776 ms, 4.1%Solaria 1 — 805 ms, 7.1%Chirp 2 — 873 ms, 4.9%Universal 3.5 Pro — 173 ms, 3.1%
Benchmarked models with their Time to Final Segment over the last 30 days.
ModelHostTTFSRank
Universal StreamingAssemblyAI——
Universal 3.5 ProAssemblyAI173 ms13th of 28

How fast are AssemblyAI's STT models?

Universal 3.5 Pro measures mean 173 ms time to final segment (13th of 28) among STT systems. Last measured 2026-09-15.

How accurate are AssemblyAI's STT models?

Universal 3.5 Pro measures 3.1% word error rate (1st of 30) among STT systems. Last measured 2026-09-15. Universal Streaming measures 6.9% word error rate (23rd of 30) among STT systems. Last measured 2026-09-15.

Which AssemblyAI model is fastest?

Its fastest dated STT result is Universal 3.5 Pro at mean 173 ms time to final segment (13th of 28) among STT systems, with 3.1% WER. Last measured 2026-09-15.

Limits of this comparison

Coval tracks its accuracy-oriented Universal model and its Universal Streaming endpoint separately so a product mode does not become a single provider score.

  • Coval's measurements do not cover every AssemblyAI audio-intelligence feature, language or asynchronous workflow.

Official resources

Same datasets, prompts and metric definitions for every model, measured by Coval’s open-source runner. Full methodology on the overview.

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