Voice AI BenchmarksBy

DEEPGRAMSPEECH-TO-TEXT130,035 SAMPLES / 30 DAYSLAST RUN OCT 14, 2026, 10:30 UTC

official resource

Flux Multilingual speech-to-text benchmarks

Flux Multilingual, hosted by Deepgram, measures mean 98 ms time to final segment (8th of 29) and 7.5% word error rate (28th of 31) among STT systems. Results cover the last 30 days. Coval benchmarks hosted endpoints daily using a consistent evaluation methodology. Last measured .

Flux General Multi is Deepgram's multilingual member of the Flux streaming family.

Time to Final Segment#8 / 29
98ms
Word Error Rate#28 / 31
7.5%
Time to First Token#6 / 27
1165ms

Overview

The Multi endpoint supports multilingual recognition, code switching, voice activity and keyterm controls.

Flux Multilingual 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
Deepgram
Hosted by
Deepgram
Source
Official API
Licensing
Proprietary
Deployment
On-prem
Region
US
Features
Code switching, Keyterm biasing, Multilingual, VAD

How Flux Multilingual 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 Flux Multilingual highlighted.
  1. #1Qwen3 ASR 1.7b39 ms
  2. #2STT RT v545 ms
  3. #3Qwen3 ASR Fast47 ms
  4. #4STT 167 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
Show all 29 models
  1. #13Ink 2149 ms
  2. #14Universal 3.5 Pro168 ms
  3. #15Whisper Large v3via Together AI199 ms
  4. #16Pulse205 ms
  5. #17Grok STT223 ms
  6. #18resonant-1252 ms
  7. #19Default258 ms
  8. #20Linden 1271 ms
  9. #21Gemini 3.5 Transcribe Live317 ms
  10. #22Daytona STT399 ms
  11. #23Voxtral Mini Transcribe Realtime 2602411 ms
  12. #24GPT Realtime Whisper599 ms
  13. #25Solaria 1626 ms
  14. #26GPT-4o mini Transcribe708 ms
  15. #27Chirp 3768 ms
  16. #28GPT-4o Transcribe784 ms
  17. #29Chirp 2908 ms
median of all models · 199 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 AI67 ms1209 ms24,749
5Nova 3Deepgram85 ms1432 ms26,002
6Nova 2Deepgram87 ms1417 ms25,960
7Parakeet TDT 0.6B v3Together AI97 ms1228 ms26,461
8Flux MultilingualDeepgram98 ms1165 ms26,007
9FluxDeepgram99 ms1092 ms26,005
10Scribe v2 RealtimeElevenLabs122 ms2157 ms26,640
11Whisper Large v3Baseten131 ms924 ms13,663
12Universal 3.6 ProAssemblyAI135 ms1072 ms24,375
13Ink 2Cartesia149 ms1967 ms26,653
14Universal 3.5 ProAssemblyAI168 ms1026 ms26,654
15Whisper Large v3Together AI199 ms1261 ms26,446
16PulseSmallest205 ms1976 ms26,654
17Grok STTSpaceXAI223 ms—26,576
18resonant-1Reson8252 ms—26,653
19DefaultGradium258 ms1987 ms26,632
20Linden 1Speechmatics271 ms1537 ms26,595
21Gemini 3.5 Transcribe LiveGemini317 ms1607 ms26,566
22Daytona STTGuava399 ms5695 ms5,225
23Voxtral Mini Transcribe Realtime 2602Mistral411 ms1851 ms26,168
24GPT Realtime WhisperOpenAI599 ms1824 ms26,622
25Solaria 1Gladia626 ms1775 ms26,215
26GPT-4o mini TranscribeOpenAI708 ms—26,621
27Chirp 3Google768 ms5957 ms26,638
28GPT-4o TranscribeOpenAI784 ms—26,625
29Chirp 2Google908 ms6099 ms26,650
—Nemotron 3.5 ASR StreamingTogether AI—1544 ms26,549
—Universal StreamingAssemblyAI—1509 ms26,625
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: 6th of 27 on Time to First Token.

Latency vs accuracy

TTFS vs WEREach point is one measured model · Flux Multilingual highlighted · 30-day averagesTime to Final Segment against Word Error Rate for every measured STT model, with Flux Multilingual 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 — 85 ms, 5.3%Nova 2 — 87 ms, 6.7%Parakeet TDT 0.6B v3 — 97 ms, 11.1%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 — 149 ms, 3.6%Universal 3.5 Pro — 168 ms, 2.3%Whisper Large v3 via Together AI — 199 ms, 7.4%Pulse — 205 ms, 4.6%Grok STT — 223 ms, 4.1%resonant-1 — 252 ms, 2.6%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 — 411 ms, 5.3%GPT Realtime Whisper — 599 ms, 4.0%Solaria 1 — 626 ms, 6.0%GPT-4o mini Transcribe — 708 ms, 3.9%Chirp 3 — 768 ms, 3.2%GPT-4o Transcribe — 784 ms, 3.7%Chirp 2 — 908 ms, 3.9%Flux Multilingual — 98 ms, 7.5%

Where the errors come from

WER compositionFlux Multilingual's Word Error Rate split by error type · 30-day averageFlux Multilingual's WER split into substitutions, deletions and insertions.
  • Flux Multilingual7.5%
SubstitutionsDeletionsInsertions

Averages and tail latency

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

Flux Multilingual latency distributionMilliseconds · shared axis across metrics · last 30 daysFlux Multilingual's latency percentiles per metric: p25–p75 band, p50 tick, whisker to max.
  • Time to Final Segmentp50 99 ms · Max 10805 ms
  • Time to First Tokenp50 1176 ms · Max 5618 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 Segment98 ms62 ms99 ms124 ms147 ms162 ms10805 ms26,007
Word Error Rate7.5%0.0%4.3%12.1%21.4%31.0%100.0%26,007
Time to First Token1165 ms817 ms1176 ms1458 ms1654 ms2013 ms5618 ms26,007

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 daysFlux Multilingual's daily median Time to Final Segment over the last 30 days.
Flux Multilingual · Sep 14: 111 msFlux Multilingual · Sep 15: 102 msFlux Multilingual · Sep 16: 106 msFlux Multilingual · Sep 17: 87 msFlux Multilingual · Sep 18: 91 msFlux Multilingual · Sep 19: 86 msFlux Multilingual · Sep 20: 91 msFlux Multilingual · Sep 21: 91 msFlux Multilingual · Sep 22: 111 msFlux Multilingual · Sep 23: 97 msFlux Multilingual · Sep 24: 112 msFlux Multilingual · Sep 25: 113 msFlux Multilingual · Sep 26: 76 msFlux Multilingual · Sep 27: 108 msFlux Multilingual · Sep 28: 99 msFlux Multilingual · Sep 29: 85 msFlux Multilingual · Sep 30: 117 msFlux Multilingual · Oct 1: 93 msFlux Multilingual · Oct 2: 92 msFlux Multilingual · Oct 3: 110 msFlux Multilingual · Oct 4: 87 msFlux Multilingual · Oct 5: 109 msFlux Multilingual · Oct 6: 83 msFlux Multilingual · Oct 7: 102 msFlux Multilingual · Oct 8: 101 msFlux Multilingual · Oct 9: 110 msFlux Multilingual · Oct 10: 98 msFlux Multilingual · Oct 11: 71 msFlux Multilingual · Oct 12: 105 msFlux Multilingual · Oct 13: 102 msFlux Multilingual · Oct 14: 97 ms
Word Error Rate — daily averageDaily average · UTC daysFlux Multilingual's daily Word Error Rate over the last 30 days.
Flux Multilingual · Sep 14: 8.8%Flux Multilingual · Sep 15: 8.5%Flux Multilingual · Sep 16: 8.8%Flux Multilingual · Sep 17: 10.2%Flux Multilingual · Sep 18: 8.0%Flux Multilingual · Sep 19: 7.7%Flux Multilingual · Sep 20: 8.1%Flux Multilingual · Sep 21: 9.8%Flux Multilingual · Sep 22: 9.3%Flux Multilingual · Sep 23: 7.3%Flux Multilingual · Sep 24: 9.4%Flux Multilingual · Sep 25: 9.7%Flux Multilingual · Sep 26: 10.4%Flux Multilingual · Sep 27: 8.0%Flux Multilingual · Sep 28: 9.0%Flux Multilingual · Sep 29: 8.0%Flux Multilingual · Sep 30: 8.2%Flux Multilingual · Oct 1: 9.6%Flux Multilingual · Oct 2: 9.9%Flux Multilingual · Oct 3: 9.2%Flux Multilingual · Oct 4: 9.1%Flux Multilingual · Oct 5: 9.1%Flux Multilingual · Oct 6: 9.5%Flux Multilingual · Oct 7: 8.6%Flux Multilingual · Oct 8: 8.9%Flux Multilingual · Oct 9: 8.7%Flux Multilingual · Oct 10: 6.7%Flux Multilingual · Oct 11: 8.6%Flux Multilingual · Oct 12: 8.2%Flux Multilingual · Oct 13: 8.6%Flux Multilingual · Oct 14: 9.9%

Time to Final Segment by dataset

Flux Multilingual by test conditionMilliseconds · lower is better · best condition firstFlux Multilingual's Time to Final Segment on each benchmark dataset.
  1. PipeCat (production)94 ms
  2. WildASR clean100 ms
  3. WildASR accents103 ms
  4. WildASR noise gaps103 ms
  5. WildASR far-field103 ms
  6. WildASR phone codec105 ms
  7. WildASR clipping111 ms
Time to Final Segment per dataset, with the number of samples behind each figure.
DatasetTTFSSamples
ProductionPipeCat94 ms13,680
AccentsWildASR103 ms1,369
CleanWildASR100 ms5,475
ClippingWildASR111 ms1,371
Far-fieldWildASR103 ms1,372
Noise gapsWildASR103 ms1,369
Phone codecWildASR105 ms1,371

Strongest condition: PipeCat (production) at 94 ms · weakest: WildASR clipping at 111 ms.

How fast is Flux Multilingual?

On Deepgram, Flux Multilingual measures mean 98 ms time to final segment (8th of 29) and mean 1165 ms time to first token (6th of 27). Last measured 2026-10-14.

How accurate is Flux Multilingual?

On Deepgram, Flux Multilingual measures 7.5% word error rate (28th of 31). Last measured 2026-10-14.

Who hosts Flux Multilingual?

Flux Multilingual is created by Deepgram and served by Deepgram. Coval measures each hosted endpoint separately.

Limits of this comparison

Coval tests that endpoint as its own recognition system, including the latency of partial and finalized transcripts.

  • Coval's current audio is English-heavy, so these results are not a language-by-language evaluation of Flux's multilingual coverage.

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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