Voice AI BenchmarksBy

RESON8SPEECH-TO-TEXT106,612 SAMPLES / 30 DAYSLAST RUN OCT 14, 2026, 06:30 UTC

official resource

resonant-1 speech-to-text benchmarks

resonant-1, hosted by Reson8, measures mean 252 ms time to final segment (18th of 29) and 2.6% word error rate (4th of 31) among STT systems. Results cover the last 30 days. Coval benchmarks hosted endpoints daily using a consistent evaluation methodology. Last measured .

Realtime is Reson8's streaming transcription endpoint.

Time to Final Segment#18 / 29
252ms
Word Error Rate#4 / 31
2.6%

Overview

The configured European service is multilingual and includes diarization plus keyterm controls.

resonant-1 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
Reson8
Hosted by
Reson8
Source
Official API
Licensing
Proprietary
Deployment
Cloud
Region
Europe
Features
Diarization, Keyterm biasing, Multilingual

How resonant-1 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 resonant-1 highlighted.
  1. #1Qwen3 ASR 1.7b39 ms
  2. #2STT RT v545 ms
  3. #3Qwen3 ASR Fast47 ms
  4. #4STT 166 ms
  5. #5Nova 385 ms
  6. #6Nova 287 ms
  7. #7Parakeet TDT 0.6B v397 ms
  8. #8Flux Multilingual98 ms
  9. #9Flux99 ms
  10. #10Scribe v2 Realtime122 ms
  11. #11Whisper Large v3via Baseten131 ms
  12. #12Universal 3.6 Pro135 ms
  13. #13Ink 2148 ms
  14. #14Universal 3.5 Pro168 ms
  15. #15Whisper Large v3via Together AI200 ms
  16. #16Pulse205 ms
  17. #17Grok STT223 ms
  18. #18resonant-1252 ms
Show all 29 models
  1. #19Default258 ms
  2. #20Linden 1271 ms
  3. #21Gemini 3.5 Transcribe Live317 ms
  4. #22Daytona STT399 ms
  5. #23Voxtral Mini Transcribe Realtime 2602410 ms
  6. #24GPT Realtime Whisper599 ms
  7. #25Solaria 1636 ms
  8. #26GPT-4o mini Transcribe708 ms
  9. #27Chirp 3768 ms
  10. #28GPT-4o Transcribe783 ms
  11. #29Chirp 2908 ms
median of all models · 200 ms
STT models over the last 30 days, ranked on Time to Final Segment.
#ModelHostTTFSWERTTFTSamples
1Qwen3 ASR 1.7bBaseten39 ms946 ms13,669
2STT RT v5Soniox45 ms1518 ms23,533
3Qwen3 ASR FastNari Labs47 ms1752 ms26,652
4STT 1Inworld AI66 ms1211 ms24,749
5Nova 3Deepgram85 ms1431 ms26,002
6Nova 2Deepgram87 ms1417 ms25,960
7Parakeet TDT 0.6B v3Together AI97 ms1228 ms26,461
8Flux MultilingualDeepgram98 ms1165 ms26,007
9FluxDeepgram99 ms1091 ms26,005
10Scribe v2 RealtimeElevenLabs122 ms2158 ms26,640
11Whisper Large v3Baseten131 ms924 ms13,663
12Universal 3.6 ProAssemblyAI135 ms1071 ms24,223
13Ink 2Cartesia148 ms1966 ms26,652
14Universal 3.5 ProAssemblyAI168 ms1026 ms26,654
15Whisper Large v3Together AI200 ms1261 ms26,445
16PulseSmallest205 ms1976 ms26,654
17Grok STTSpaceXAI223 ms—26,576
18resonant-1Reson8252 ms—26,653
19DefaultGradium258 ms1986 ms26,632
20Linden 1Speechmatics271 ms1537 ms26,595
21Gemini 3.5 Transcribe LiveGemini317 ms1609 ms26,567
22Daytona STTGuava399 ms5695 ms5,225
23Voxtral Mini Transcribe Realtime 2602Mistral410 ms1851 ms26,200
24GPT Realtime WhisperOpenAI599 ms1824 ms26,622
25Solaria 1Gladia636 ms1780 ms26,200
26GPT-4o mini TranscribeOpenAI708 ms—26,621
27Chirp 3Google768 ms5957 ms26,638
28GPT-4o TranscribeOpenAI783 ms—26,625
29Chirp 2Google908 ms6098 ms26,650
—Nemotron 3.5 ASR StreamingTogether AI—1544 ms26,542
—Universal StreamingAssemblyAI—1509 ms26,624
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: 4th of 31 on Word Error Rate.

Latency vs accuracy

TTFS vs WEREach point is one measured model · resonant-1 highlighted · 30-day averagesTime to Final Segment against Word Error Rate for every measured STT model, with resonant-1 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 — 66 ms, 7.1%Nova 3 — 85 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%Universal 3.6 Pro — 135 ms, 2.1%Ink 2 — 148 ms, 3.6%Universal 3.5 Pro — 168 ms, 2.4%Whisper Large v3 via Together AI — 200 ms, 7.4%Pulse — 205 ms, 4.6%Grok STT — 223 ms, 4.1%Default — 258 ms, 8.8%Linden 1 — 271 ms, 3.9%Gemini 3.5 Transcribe Live — 317 ms, 3.0%Daytona STT — 399 ms, 4.9%Voxtral Mini Transcribe Realtime 2602 — 410 ms, 5.4%GPT Realtime Whisper — 599 ms, 4.0%Solaria 1 — 636 ms, 6.0%GPT-4o mini Transcribe — 708 ms, 3.9%Chirp 3 — 768 ms, 3.2%GPT-4o Transcribe — 783 ms, 3.8%Chirp 2 — 908 ms, 3.9%resonant-1 — 252 ms, 2.6%

Where the errors come from

WER compositionresonant-1's Word Error Rate split by error type · 30-day averageresonant-1's WER split into substitutions, deletions and insertions.
  • resonant-12.6%
SubstitutionsDeletionsInsertions

Averages and tail latency

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

resonant-1 latency distributionMilliseconds · shared axis across metrics · last 30 daysresonant-1's latency percentiles per metric: p25–p75 band, p50 tick, whisker to max.
  • Time to Final Segmentp50 248 ms · Max 2783 ms
band p25–p75 · tick p50 · whisker to max with p90 and p95 stops
Average and percentile values per metric, with the number of samples behind each row.
MetricAveragep25p50p75p90p95MaxSamples
Time to Final Segment252 ms235 ms248 ms269 ms282 ms293 ms2783 ms26,653
Word Error Rate2.6%0.0%0.0%4.3%9.5%14.3%83.3%26,653

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 daysresonant-1's daily median Time to Final Segment over the last 30 days.
resonant-1 · Sep 14: 247 msresonant-1 · Sep 15: 269 msresonant-1 · Sep 16: 254 msresonant-1 · Sep 17: 259 msresonant-1 · Sep 18: 260 msresonant-1 · Sep 19: 259 msresonant-1 · Sep 20: 258 msresonant-1 · Sep 21: 257 msresonant-1 · Sep 22: 275 msresonant-1 · Sep 23: 261 msresonant-1 · Sep 24: 262 msresonant-1 · Sep 25: 259 msresonant-1 · Sep 26: 270 msresonant-1 · Sep 27: 261 msresonant-1 · Sep 28: 272 msresonant-1 · Sep 29: 251 msresonant-1 · Sep 30: 248 msresonant-1 · Oct 1: 244 msresonant-1 · Oct 2: 233 msresonant-1 · Oct 3: 232 msresonant-1 · Oct 4: 241 msresonant-1 · Oct 5: 230 msresonant-1 · Oct 6: 241 msresonant-1 · Oct 7: 243 msresonant-1 · Oct 8: 229 msresonant-1 · Oct 9: 238 msresonant-1 · Oct 10: 228 msresonant-1 · Oct 11: 249 msresonant-1 · Oct 12: 239 msresonant-1 · Oct 13: 238 msresonant-1 · Oct 14: 247 ms
Word Error Rate — daily averageDaily average · UTC daysresonant-1's daily Word Error Rate over the last 30 days.
resonant-1 · Sep 14: 3.9%resonant-1 · Sep 15: 4.0%resonant-1 · Sep 16: 3.5%resonant-1 · Sep 17: 3.7%resonant-1 · Sep 18: 3.2%resonant-1 · Sep 19: 3.3%resonant-1 · Sep 20: 4.4%resonant-1 · Sep 21: 3.6%resonant-1 · Sep 22: 4.5%resonant-1 · Sep 23: 3.7%resonant-1 · Sep 24: 4.8%resonant-1 · Sep 25: 4.4%resonant-1 · Sep 26: 3.5%resonant-1 · Sep 27: 3.9%resonant-1 · Sep 28: 3.0%resonant-1 · Sep 29: 3.1%resonant-1 · Sep 30: 3.7%resonant-1 · Oct 1: 3.3%resonant-1 · Oct 2: 2.8%resonant-1 · Oct 3: 3.0%resonant-1 · Oct 4: 3.3%resonant-1 · Oct 5: 3.4%resonant-1 · Oct 6: 4.2%resonant-1 · Oct 7: 3.3%resonant-1 · Oct 8: 3.3%resonant-1 · Oct 9: 3.4%resonant-1 · Oct 10: 3.3%resonant-1 · Oct 11: 3.1%resonant-1 · Oct 12: 3.5%resonant-1 · Oct 13: 4.1%resonant-1 · Oct 14: 4.2%

Time to Final Segment by dataset

resonant-1 by test conditionMilliseconds · lower is better · best condition firstresonant-1's Time to Final Segment on each benchmark dataset.
  1. WildASR accents241 ms
  2. WildASR clipping250 ms
  3. WildASR phone codec251 ms
  4. WildASR noise gaps251 ms
  5. WildASR clean252 ms
  6. WildASR far-field252 ms
  7. PipeCat (production)254 ms
Time to Final Segment per dataset, with the number of samples behind each figure.
DatasetTTFSSamples
ProductionPipeCat254 ms14,019
AccentsWildASR241 ms1,403
CleanWildASR252 ms5,612
ClippingWildASR250 ms1,405
Far-fieldWildASR252 ms1,406
Noise gapsWildASR251 ms1,403
Phone codecWildASR251 ms1,405

Strongest condition: WildASR accents at 241 ms · weakest: PipeCat (production) at 254 ms.

How fast is resonant-1?

On Reson8, resonant-1 measures mean 252 ms time to final segment (18th of 29). Last measured 2026-10-14.

How accurate is resonant-1?

On Reson8, resonant-1 measures 2.6% word error rate (4th of 31). Last measured 2026-10-14.

Who hosts resonant-1?

resonant-1 is created by Reson8 and served by Reson8. Coval measures each hosted endpoint separately.

Limits of this comparison

Because the model name is generic, these results refer specifically to the endpoint served by Reson8.

  • These results do not apply to similarly named real-time transcription endpoints from other providers.

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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