OPENAISPEECH-TO-TEXT91,431 SAMPLES / 30 DAYSLAST RUN SEP 15, 2026, 08:30 UTC

official resource

GPT-4o mini Transcribe speech-to-text benchmarks

GPT-4o mini Transcribe, hosted by OpenAI, measures mean 690 ms time to final segment (24th of 28) and 4.9% word error rate (13th of 30) among STT systems. Results cover the last 30 days. Coval benchmarks hosted endpoints daily using a consistent evaluation methodology. Last measured .

GPT-4o mini Transcribe is OpenAI's smaller GPT-4o-based transcription model.

Word Error Rate#13 / 30
4.9%

Overview

OpenAI offers it as a dedicated Audio API transcription model with streaming output and prompt context for vocabulary guidance.

GPT-4o mini Transcribe is tested every day on fixed public audio — clean, accented, noisy, reverberant, far-field, clipped and phone-codec speech — for transcription accuracy and streaming latency.

Technical specifications

Made by
OpenAI
Hosted by
OpenAI
Source
Official API
Licensing
Proprietary
Deployment
Cloud
Region
US
Features
Keyterm biasing, Multilingual, VAD

How GPT-4o mini Transcribe ranks

Full STT dashboard
Time to Final Segment — every measured STT modelMilliseconds · lower is better · 30-day averageEvery STT model ranked on Time to Final Segment, with GPT-4o mini Transcribe highlighted.
  1. #1Qwen3 ASR 1.7b39 ms
  2. #3STT RT v557 ms
  3. #4STT 165 ms
  4. #6Nova 389 ms
  5. #7Nova 292 ms
  6. #9Flux99 ms
  7. #10Ink 2122 ms
  8. #11Whisper Large v3via Baseten125 ms
  9. #14Grok STT207 ms
  10. #15Defaultvia Speechmatics209 ms
  11. #16Pulse212 ms
  12. #17Defaultvia Gradium246 ms
  13. #18resonant-1264 ms
  14. #19Whisper Large v3via Together AI295 ms
  15. #20Enhanced299 ms
Show all 28 models
  1. #26Chirp 3776 ms
  2. #27Solaria 1805 ms
  3. #28Chirp 2873 ms
median of all models · 208 ms
STT models over the last 30 days, ranked on Time to Final Segment.
#ModelHostTTFSWERTTFTSamples
1Qwen3 ASR 1.7bBaseten39 ms931 ms1,256
2Qwen3 ASR FastNari46 ms1748 ms4,451
3STT RT v5Soniox57 ms1529 ms22,468
4STT 1Inworld AI65 ms1400 ms22,836
5Parakeet TDT 0.6B v3Together AI81 ms1215 ms22,488
6Nova 3Deepgram89 ms1419 ms22,855
7Nova 2Deepgram92 ms1420 ms22,733
8Flux MultilingualDeepgram98 ms1164 ms12,251
9FluxDeepgram99 ms1076 ms12,243
10Ink 2Cartesia122 ms1827 ms22,862
11Whisper Large v3Baseten125 ms912 ms1,252
12Scribe v2 RealtimeElevenLabs133 ms2175 ms22,866
13Universal 3.5 ProAssemblyAI173 ms1041 ms20,253
14Grok STTxAI207 ms22,853
15DefaultSpeechmatics209 ms1428 ms22,876
16PulseSmallest212 ms2011 ms22,856
17DefaultGradium246 ms1980 ms22,839
18resonant-1Reson8264 ms22,860
19Whisper Large v3Together AI295 ms1338 ms22,744
20EnhancedSpeechmatics299 ms1492 ms22,876
21Gemini 3.5 Transcribe LiveGemini305 ms1679 ms16,403
22Voxtral Mini Transcribe Realtime 2602Mistral401 ms1851 ms22,583
23GPT Realtime WhisperOpenAI551 ms1814 ms22,812
24GPT-4o mini TranscribeOpenAI690 ms22,830
25GPT-4o TranscribeOpenAI754 ms22,831
26Chirp 3Google776 ms5974 ms22,875
27Solaria 1Gladia805 ms1804 ms22,439
28Chirp 2Google873 ms6071 ms22,789
Nemotron 3.5 ASR StreamingTogether AI1548 ms22,747
Universal StreamingAssemblyAI1514 ms20,235
Under-sampled models are excluded; tied models share a place. Dotted WER values split into substitutions, deletions and insertions on hover or tap.

Highest relative placement: 13th of 30 on Word Error Rate.

Latency vs accuracy

Where the errors come from

WER compositionGPT-4o mini Transcribe's Word Error Rate split by error type · 30-day averageGPT-4o mini Transcribe's WER split into substitutions, deletions and insertions.
  • GPT-4o mini Transcribe4.9%
SubstitutionsDeletionsInsertions

Averages and tail latency

Averages hide slow outliers — these are the distributions behind each figure.

GPT-4o mini Transcribe latency distributionMilliseconds · shared axis across metrics · last 30 daysGPT-4o mini Transcribe's latency percentiles per metric: p25–p75 band, p50 tick, whisker to p99.
  • Time to Final Segmentp50 652 ms · p99 1424 ms
band p25–p75 · tick p50 · whisker to p99 with p90 and p95 stops
Average and percentile values per metric, with the number of samples behind each row.
MetricAveragep25p50p75p90p95p99Samples
Time to Final Segment690 ms578 ms652 ms729 ms875 ms1021 ms1424 ms22,830
Word Error Rate4.9%0.0%0.0%6.5%13.3%20.0%47.1%22,867

Last 30 days

Daily medians from the same measurement runs · gaps are days without qualifying runs.

Time to Final Segment — daily p50Line p50 · band p25–p75 · UTC daysGPT-4o mini Transcribe's daily median Time to Final Segment over the last 30 days.
GPT-4o mini Transcribe · Sep 1: 620 msGPT-4o mini Transcribe · Sep 2: 656 msGPT-4o mini Transcribe · Sep 3: 671 msGPT-4o mini Transcribe · Sep 4: 934 msGPT-4o mini Transcribe · Sep 5: 581 msGPT-4o mini Transcribe · Sep 6: 589 msGPT-4o mini Transcribe · Sep 7: 660 msGPT-4o mini Transcribe · Sep 8: 686 msGPT-4o mini Transcribe · Sep 9: 661 msGPT-4o mini Transcribe · Sep 10: 668 msGPT-4o mini Transcribe · Sep 11: 609 msGPT-4o mini Transcribe · Sep 12: 548 msGPT-4o mini Transcribe · Sep 13: 575 msGPT-4o mini Transcribe · Sep 14: 663 msGPT-4o mini Transcribe · Sep 15: 680 ms
Word Error Rate — daily averageDaily average · UTC daysGPT-4o mini Transcribe's daily Word Error Rate over the last 30 days.
GPT-4o mini Transcribe · Sep 1: 4.5%GPT-4o mini Transcribe · Sep 2: 5.2%GPT-4o mini Transcribe · Sep 3: 5.3%GPT-4o mini Transcribe · Sep 4: 5.4%GPT-4o mini Transcribe · Sep 5: 6.0%GPT-4o mini Transcribe · Sep 6: 5.5%GPT-4o mini Transcribe · Sep 7: 5.9%GPT-4o mini Transcribe · Sep 8: 5.7%GPT-4o mini Transcribe · Sep 9: 6.0%GPT-4o mini Transcribe · Sep 10: 5.6%GPT-4o mini Transcribe · Sep 11: 4.9%GPT-4o mini Transcribe · Sep 12: 5.6%GPT-4o mini Transcribe · Sep 13: 5.2%GPT-4o mini Transcribe · Sep 14: 5.4%GPT-4o mini Transcribe · Sep 15: 6.9%

Time to Final Segment by dataset

GPT-4o mini Transcribe by test conditionMilliseconds · lower is better · best condition firstGPT-4o mini Transcribe's Time to Final Segment on each benchmark dataset.
Time to Final Segment per dataset, with the number of samples behind each figure.
DatasetTTFSSamples
LibriSpeech883 ms1
ProductionPipeCat693 ms11,497
AccentsWildASR633 ms1,115
CleanWildASR692 ms4,570
ClippingWildASR689 ms1,136
Far-fieldWildASR690 ms1,141
Noise gapsWildASR707 ms1,141
Phone codecWildASR686 ms1,135
ReverbWildASR691 ms1,094

Strongest condition: WildASR accents at 633 ms · weakest: LibriSpeech at 883 ms.

How fast is GPT-4o mini Transcribe?

On OpenAI, GPT-4o mini Transcribe measures mean 690 ms time to final segment (24th of 28). Last measured 2026-09-15.

How accurate is GPT-4o mini Transcribe?

On OpenAI, GPT-4o mini Transcribe measures 4.9% word error rate (13th of 30). Last measured 2026-09-15.

Who hosts GPT-4o mini Transcribe?

GPT-4o mini Transcribe is created by OpenAI and served by OpenAI. Coval measures each hosted endpoint separately.

Limits of this comparison

The mini model receives the same audio as GPT-4o Transcribe for a direct accuracy and latency comparison.

  • The results apply to this API model and Coval configuration, not every batch or Realtime transcription workflow.

Official sources

Results are re-measured daily using fixed datasets and reported over a rolling 30-day window. 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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