OPENAISPEECH-TO-TEXT91,431 SAMPLES / 30 DAYSLAST RUN SEP 15, 2026, 08:30 UTC
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.
- Time to Final Segment#24 / 28
- 690ms
- 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.
How GPT-4o mini Transcribe ranks
Full STT dashboard- #1Qwen3 ASR 1.7bDedicated inference. Shared endpoints serve many customers on the same infrastructure, while dedicated endpoints run on hardware reserved for a single customer.39 ms
- #11Whisper Large v3via BasetenDedicated inference. Shared endpoints serve many customers on the same infrastructure, while dedicated endpoints run on hardware reserved for a single customer.125 ms
- #15Defaultvia Speechmatics209 ms
- #17Defaultvia Gradium246 ms
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- #6Qwen3 ASR 1.7bDedicated inference. Shared endpoints serve many customers on the same infrastructure, while dedicated endpoints run on hardware reserved for a single customer.4.1%
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- #16Defaultvia Speechmatics5.4%
- #18Whisper Large v3via BasetenDedicated inference. Shared endpoints serve many customers on the same infrastructure, while dedicated endpoints run on hardware reserved for a single customer.5.6%
- #28Defaultvia Gradium10.0%
- #1Whisper Large v3via BasetenDedicated inference. Shared endpoints serve many customers on the same infrastructure, while dedicated endpoints run on hardware reserved for a single customer.912 ms
- #2Qwen3 ASR 1.7bDedicated inference. Shared endpoints serve many customers on the same infrastructure, while dedicated endpoints run on hardware reserved for a single customer.931 ms
- #11Defaultvia Speechmatics1428 ms
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- #22Defaultvia Gradium1980 ms
| # | Model | Host | TTFS | WER | TTFT | Samples |
|---|---|---|---|---|---|---|
| 1 | Qwen3 ASR 1.7b | Baseten | 39 ms | 931 ms | 1,256 | |
| 2 | Qwen3 ASR Fast | Nari | 46 ms | 1748 ms | 4,451 | |
| 3 | STT RT v5 | Soniox | 57 ms | 1529 ms | 22,468 | |
| 4 | STT 1 | Inworld AI | 65 ms | 1400 ms | 22,836 | |
| 5 | Parakeet TDT 0.6B v3 | Together AI | 81 ms | 1215 ms | 22,488 | |
| 6 | Nova 3 | Deepgram | 89 ms | 1419 ms | 22,855 | |
| 7 | Nova 2 | Deepgram | 92 ms | 1420 ms | 22,733 | |
| 8 | Flux Multilingual | Deepgram | 98 ms | 1164 ms | 12,251 | |
| 9 | Flux | Deepgram | 99 ms | 1076 ms | 12,243 | |
| 10 | Ink 2 | Cartesia | 122 ms | 1827 ms | 22,862 | |
| 11 | Whisper Large v3 | Baseten | 125 ms | 912 ms | 1,252 | |
| 12 | Scribe v2 Realtime | ElevenLabs | 133 ms | 2175 ms | 22,866 | |
| 13 | Universal 3.5 Pro | AssemblyAI | 173 ms | 1041 ms | 20,253 | |
| 14 | Grok STT | xAI | 207 ms | — | 22,853 | |
| 15 | Default | Speechmatics | 209 ms | 1428 ms | 22,876 | |
| 16 | Pulse | Smallest | 212 ms | 2011 ms | 22,856 | |
| 17 | Default | Gradium | 246 ms | 1980 ms | 22,839 | |
| 18 | resonant-1 | Reson8 | 264 ms | — | 22,860 | |
| 19 | Whisper Large v3 | Together AI | 295 ms | 1338 ms | 22,744 | |
| 20 | Enhanced | Speechmatics | 299 ms | 1492 ms | 22,876 | |
| 21 | Gemini 3.5 Transcribe Live | Gemini | 305 ms | 1679 ms | 16,403 | |
| 22 | Voxtral Mini Transcribe Realtime 2602 | Mistral | 401 ms | 1851 ms | 22,583 | |
| 23 | GPT Realtime Whisper | OpenAI | 551 ms | 1814 ms | 22,812 | |
| 24 | GPT-4o mini Transcribe | OpenAI | 690 ms | — | 22,830 | |
| 25 | GPT-4o Transcribe | OpenAI | 754 ms | — | 22,831 | |
| 26 | Chirp 3 | 776 ms | 5974 ms | 22,875 | ||
| 27 | Solaria 1 | Gladia | 805 ms | 1804 ms | 22,439 | |
| 28 | Chirp 2 | 873 ms | 6071 ms | 22,789 | ||
| — | Nemotron 3.5 ASR Streaming | Together AI | — | 1548 ms | 22,747 | |
| — | Universal Streaming | AssemblyAI | — | 1514 ms | 20,235 |
Highest relative placement: 13th of 30 on Word Error Rate.
Latency vs accuracy
Where the errors come from
- GPT-4o mini Transcribe4.9%
Averages and tail latency
Averages hide slow outliers — these are the distributions behind each figure.
- Time to Final Segmentp50 652 ms · p99 1424 ms
- GPT-4o mini Transcribep50 0.0% · p99 47.1%
| Metric | Average | p25 | p50 | p75 | p90 | p95 | p99 | Samples |
|---|---|---|---|---|---|---|---|---|
| Time to Final Segment | 690 ms | 578 ms | 652 ms | 729 ms | 875 ms | 1021 ms | 1424 ms | 22,830 |
| Word Error Rate | 4.9% | 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 by dataset
- WildASR accents633 ms
- WildASR phone codec686 ms
- WildASR clipping689 ms
- WildASR far-field690 ms
- WildASR reverb691 ms
- WildASR clean692 ms
- PipeCat (production)693 ms
- WildASR noise gaps707 ms
| Dataset | TTFS | Samples |
|---|---|---|
| LibriSpeech | 883 ms | 1 |
| ProductionPipeCat | 693 ms | 11,497 |
| AccentsWildASR | 633 ms | 1,115 |
| CleanWildASR | 692 ms | 4,570 |
| ClippingWildASR | 689 ms | 1,136 |
| Far-fieldWildASR | 690 ms | 1,141 |
| Noise gapsWildASR | 707 ms | 1,141 |
| Phone codecWildASR | 686 ms | 1,135 |
| ReverbWildASR | 691 ms | 1,094 |
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.
Evaluate your own voice agent
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