CARTESIASPEECH-TO-TEXT146,604 SAMPLES / 30 DAYSLAST RUN AUG 28, 2026, 21:30 UTC

official resource

Ink 2 speech-to-text benchmarks

Ink 2 is Cartesia's real-time speech recognition model.

Word Error Rate#8 / 28
4.9%
Time to First Token#17 / 24
1812ms

Overview

Cartesia provides Ink through a streaming speech-to-text API with voice activity and an on-premises option.

Ink 2 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
Cartesia
Hosted by
Cartesia
Source
Official API
Licensing
Proprietary
Deployment
On-prem
Region
US
Features
VAD

How Ink 2 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 Ink 2 highlighted.
  1. #1STT RT v564 ms
  2. #3STT 183 ms
  3. #4Nova 399 ms
  4. #5Nova 2101 ms
  5. #6Ink 2108 ms
  6. #9Defaultvia Azure155 ms
  7. #12Grok STT199 ms
Show all 24 models
  1. #13Pulse205 ms
  2. #14Defaultvia Speechmatics223 ms
  3. #15Defaultvia Gradium249 ms
  4. #16resonant-1288 ms
  5. #17Enhanced341 ms
  6. #21Solaria 1680 ms
  7. #23Chirp 2811 ms
  8. #24Chirp 3813 ms
median of all models · 202 ms
STT models over the last 30 days, ranked on Time to Final Segment.
#ModelHostTTFSWERTTFTSamples
1STT RT v5Soniox64 ms1533 ms29,322
2Parakeet TDT 0.6B v3Together AI70 ms1210 ms29,161
3STT 1Inworld AI83 ms1468 ms29,280
4Nova 3Deepgram99 ms1434 ms29,290
5Nova 2Deepgram101 ms1436 ms29,145
6Ink 2Cartesia108 ms1812 ms29,288
7Scribe v2 RealtimeElevenLabs120 ms2157 ms29,313
8Universal 3.5 ProAssemblyAI146 ms1030 ms29,280
9DefaultAzure155 ms1792 ms6,682
10Whisper Large v3Together AI180 ms1241 ms29,171
11Velma 2 STT StreamingModulate191 ms1576 ms17,895
12Grok STTxAI199 ms29,322
13PulseSmallest205 ms2072 ms29,313
14DefaultSpeechmatics223 ms1473 ms29,323
15DefaultGradium249 ms1979 ms28,235
16resonant-1Reson8288 ms17,066
17EnhancedSpeechmatics341 ms1536 ms29,322
18Voxtral Mini Transcribe Realtime 2602Mistral356 ms1828 ms29,175
19GPT Realtime WhisperOpenAI559 ms1823 ms29,286
20GPT-4o mini TranscribeOpenAI623 ms29,318
21Solaria 1Gladia680 ms1703 ms26,356
22GPT-4o TranscribeOpenAI742 ms29,318
23Chirp 2Google811 ms6000 ms29,237
24Chirp 3Google813 ms5999 ms29,319
FluxDeepgram1089 ms29,354
Flux MultilingualDeepgram1175 ms29,344
Nemotron 3.5 ASR StreamingTogether AI1540 ms29,193
Universal StreamingAssemblyAI1510 ms29,297
Under-sampled models are excluded; tied models share a place. Dotted WER values split into substitutions, deletions and insertions on hover or tap.

Latency vs accuracy

Where the errors come from

WER compositionInk 2's Word Error Rate split by error type · 30-day averageInk 2's WER split into substitutions, deletions and insertions.
  • Ink 24.9%
SubstitutionsDeletionsInsertions

Averages and tail latency

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

Ink 2 latency distributionMilliseconds · shared axis across metrics · last 30 daysInk 2's latency percentiles per metric: p25–p75 band, p50 tick, whisker to p99.
  • Time to Final Segmentp50 106 ms · p99 169 ms
  • Time to First Tokenp50 1773 ms · p99 3558 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 Segment108 ms92 ms106 ms118 ms132 ms142 ms169 ms29,288
Word Error Rate4.9%0.0%0.0%6.7%14.3%20.0%41.1%29,329
Time to First Token1812 ms1573 ms1773 ms1987 ms2373 ms2574 ms3558 ms29,329

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 daysInk 2's daily median Time to Final Segment over the last 30 days.
Ink 2 · Jul 30: 95 msInk 2 · Jul 31: 152 msInk 2 · Aug 1: 101 msInk 2 · Aug 2: 105 msInk 2 · Aug 3: 112 msInk 2 · Aug 4: 110 msInk 2 · Aug 5: 100 msInk 2 · Aug 6: 104 msInk 2 · Aug 7: 109 msInk 2 · Aug 8: 105 msInk 2 · Aug 9: 98 msInk 2 · Aug 10: 113 msInk 2 · Aug 11: 104 msInk 2 · Aug 12: 107 msInk 2 · Aug 13: 100 msInk 2 · Aug 14: 98 msInk 2 · Aug 15: 103 msInk 2 · Aug 16: 101 msInk 2 · Aug 17: 105 msInk 2 · Aug 18: 107 msInk 2 · Aug 19: 107 msInk 2 · Aug 20: 102 msInk 2 · Aug 21: 109 msInk 2 · Aug 22: 95 msInk 2 · Aug 23: 100 msInk 2 · Aug 24: 108 msInk 2 · Aug 25: 101 msInk 2 · Aug 26: 105 msInk 2 · Aug 27: 102 msInk 2 · Aug 28: 109 ms
Word Error Rate — daily averageDaily average · UTC daysInk 2's daily Word Error Rate over the last 30 days.
Ink 2 · Jul 30: 3.6%Ink 2 · Jul 31: 5.7%Ink 2 · Aug 1: 5.5%Ink 2 · Aug 2: 6.4%Ink 2 · Aug 3: 4.9%Ink 2 · Aug 4: 6.3%Ink 2 · Aug 5: 6.1%Ink 2 · Aug 6: 5.9%Ink 2 · Aug 7: 6.6%Ink 2 · Aug 8: 6.9%Ink 2 · Aug 9: 6.3%Ink 2 · Aug 10: 4.7%Ink 2 · Aug 11: 6.6%Ink 2 · Aug 12: 5.6%Ink 2 · Aug 13: 5.3%Ink 2 · Aug 14: 5.9%Ink 2 · Aug 15: 4.9%Ink 2 · Aug 16: 6.6%Ink 2 · Aug 17: 5.7%Ink 2 · Aug 18: 7.6%Ink 2 · Aug 19: 5.9%Ink 2 · Aug 20: 4.9%Ink 2 · Aug 21: 5.9%Ink 2 · Aug 22: 6.3%Ink 2 · Aug 23: 6.1%Ink 2 · Aug 24: 5.1%Ink 2 · Aug 25: 5.9%Ink 2 · Aug 26: 4.9%Ink 2 · Aug 27: 5.5%Ink 2 · Aug 28: 6.1%

Time to Final Segment by dataset

Ink 2 by test conditionMilliseconds · lower is better · best condition firstInk 2's Time to Final Segment on each benchmark dataset.
Time to Final Segment per dataset, with the number of samples behind each figure.
DatasetTTFSSamples
LibriSpeech96 ms1
ProductionPipeCat108 ms14,924
AccentsWildASR110 ms1,437
CleanWildASR108 ms5,773
ClippingWildASR107 ms1,443
Far-fieldWildASR110 ms1,441
Noise gapsWildASR107 ms1,439
Phone codecWildASR111 ms1,439
ReverbWildASR110 ms1,391

Strongest condition: LibriSpeech at 96 ms · weakest: WildASR phone codec at 111 ms.

Limits of this comparison

Ink 2 is measured separately from Cartesia's Sonic synthesis models and ranked only against other STT models.

  • The benchmark does not combine Ink with Sonic, so end-to-end Cartesia agent latency must be evaluated separately.

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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