Voice AI BenchmarksBy

PROVIDERLAST 30 DAYS

official resource

AssemblyAI voice AI models and benchmarks

AssemblyAI (Assembly AI, assemblyai.com) lists 3 STT models in Coval. Its fastest dated STT result is Universal 3.6 Pro at mean 135 ms time to final segment (12th of 29) among STT systems, with 2.1% WER. Results cover the last 30 days. Last measured .

AssemblyAI builds managed speech recognition APIs.

Measured models
3
STT
Best STT model#12 / 29
135ms
Universal 3.6 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, Universal 3.6 Pro

Speech-to-Text

Full STT dashboard

Ranked on Time to Final Segment against 29 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. #2STT RT v545 ms
  3. #3Qwen3 ASR Fast47 ms
  4. #4STT 167 ms
  5. #5Nova 386 ms
  6. #6Nova 287 ms
  7. #7Parakeet TDT 0.6B v397 ms
  8. #12Universal 3.6 Pro135 ms
  9. #14Universal 3.5 Pro170 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 3.6 Pro2.1%
  2. #2Universal 3.5 Pro2.4%
  3. #3Qwen3 ASR Fast2.6%
  4. #4resonant-12.6%
  5. #5Qwen3 ASR 1.7b2.9%
  6. #6Gemini 3.5 Transcribe Live3.0%
  7. #7Chirp 33.2%
  8. #23Universal Streaming5.9%
leaders plus AssemblyAI models · 23 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 Baseten924 ms
  2. #2Qwen3 ASR 1.7b946 ms
  3. #3Universal 3.5 Pro1025 ms
  4. #4Universal 3.6 Pro1071 ms
  5. #5Flux1091 ms
  6. #6Flux Multilingual1165 ms
  7. #7STT 11205 ms
  8. #12Universal Streaming1509 ms
leaders plus AssemblyAI models · 19 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, 2.9%STT RT v5 — 45 ms, 4.8%Qwen3 ASR Fast — 47 ms, 2.6%STT 1 — 67 ms, 7.1%Nova 3 — 86 ms, 5.3%Nova 2 — 87 ms, 6.7%Parakeet TDT 0.6B v3 — 97 ms, 11.1%Flux Multilingual — 98 ms, 7.5%Flux — 99 ms, 5.8%Scribe v2 Realtime — 122 ms, 4.4%Whisper Large v3 via Baseten — 131 ms, 4.5%Ink 2 — 149 ms, 3.6%Whisper Large v3 via Together AI — 198 ms, 7.4%Pulse — 205 ms, 4.6%Grok STT — 223 ms, 4.1%resonant-1 — 252 ms, 2.6%Default — 262 ms, 8.8%Linden 1 — 274 ms, 3.9%Gemini 3.5 Transcribe Live — 318 ms, 3.0%Daytona STT — 399 ms, 4.9%Voxtral Mini Transcribe Realtime 2602 — 415 ms, 5.4%GPT Realtime Whisper — 599 ms, 4.0%Solaria 1 — 620 ms, 6.1%GPT-4o mini Transcribe — 710 ms, 3.9%Chirp 3 — 768 ms, 3.2%GPT-4o Transcribe — 784 ms, 3.8%Chirp 2 — 909 ms, 3.9%Universal 3.6 Pro — 135 ms, 2.1%Universal 3.5 Pro — 170 ms, 2.4%
Benchmarked models with their Time to Final Segment over the last 30 days.
ModelHostTTFSRank
Universal StreamingAssemblyAI——
Universal 3.5 ProAssemblyAI170 ms14th of 29
Universal 3.6 ProAssemblyAI135 ms12th of 29

How fast are AssemblyAI's STT models?

Universal 3.6 Pro measures mean 135 ms time to final segment (12th of 29) among STT systems. Last measured 2026-10-14. Universal 3.5 Pro measures mean 170 ms time to final segment (14th of 29) among STT systems. Last measured 2026-10-14.

How accurate are AssemblyAI's STT models?

Universal 3.6 Pro measures 2.1% word error rate (1st of 31) among STT systems. Last measured 2026-10-14. Universal 3.5 Pro measures 2.4% word error rate (2nd of 31) among STT systems. Last measured 2026-10-14. Universal Streaming measures 5.9% word error rate (23rd of 31) among STT systems. Last measured 2026-10-14.

Which AssemblyAI model is fastest?

Its fastest dated STT result is Universal 3.6 Pro at mean 135 ms time to final segment (12th of 29) among STT systems, with 2.1% WER. Last measured 2026-10-14.

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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