Voice AI BenchmarksBy

PROVIDERLAST 30 DAYS

official resource

Mistral AI voice AI models and benchmarks

Mistral AI lists 1 STT model in Coval. Its fastest dated STT result is Voxtral Mini Transcribe Realtime 2602 at mean 401 ms time to final segment (22nd of 28) among STT systems, with 6.2% WER. Results cover the last 30 days. Last measured .

Mistral AI develops the Voxtral audio model family and hosts the measured real-time transcription endpoint in Europe.

Measured models
1
STT
Best STT model#22 / 28
401ms
Voxtral Mini Transcribe Realtime 2602

Overview

The full dated Voxtral model identifier remains visible, allowing later releases to coexist instead of silently replacing earlier measurements.

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
Voxtral Mini Transcribe Realtime 2602

Speech-to-Text

Full STT dashboard

Ranked on Time to Final Segment against 28 measured models.

Time to Final Segmentms · lower is better · Mistral AI models markedEvery measured STT model on Time to Final Segment, with Mistral AI'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. #22Voxtral Mini Transcribe Realtime 2602401 ms
leaders plus Mistral AI models · 20 other models in the full table
Word Error Rate% · lower is better · Mistral AI models markedEvery measured STT model on Word Error Rate, with Mistral AI'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. #21Voxtral Mini Transcribe Realtime 26026.2%
leaders plus Mistral AI models · 22 other models in the full table
Time to First Tokenms · lower is better · Mistral AI models markedEvery measured STT model on Time to First Token, with Mistral AI'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. #21Voxtral Mini Transcribe Realtime 26021851 ms
leaders plus Mistral AI models · 18 other models in the full table
TTFS vs WER — Mistral AI vs every measured modelEach point is one measured STT model · Mistral AI models highlighted · 30-day averagesTime to Final Segment against Word Error Rate for every measured STT model, with Mistral AI'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%Universal 3.5 Pro — 173 ms, 3.1%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%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%Voxtral Mini Transcribe Realtime 2602 — 401 ms, 6.2%
Benchmarked models with their Time to Final Segment over the last 30 days.
ModelHostTTFSRank
Voxtral Mini Transcribe Realtime 2602Mistral401 ms22nd of 28

How fast are Mistral AI's STT models?

Voxtral Mini Transcribe Realtime 2602 measures mean 401 ms time to final segment (22nd of 28) among STT systems. Last measured 2026-09-15.

How accurate are Mistral AI's STT models?

Voxtral Mini Transcribe Realtime 2602 measures 6.2% word error rate (21st of 30) among STT systems. Last measured 2026-09-15.

Which Mistral AI model is fastest?

Its fastest dated STT result is Voxtral Mini Transcribe Realtime 2602 at mean 401 ms time to final segment (22nd of 28) among STT systems, with 6.2% WER. Last measured 2026-09-15.

Limits of this comparison

  • The benchmark covers one Voxtral STT release, not every Voxtral or Mistral model.

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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