Cartesia Ink 2 is ahead on the Pipecat Dataset and the Ocular Dataset, and Inworld STT-1 on time to first text and final-text delay.
Every model's error rate on the public clips, against how long its transcript takes to settle.
Word error rate on 1,000 clips from Pipecat's STT benchmark dataset.
Cartesia Ink 2, 0.33 points lower
Word error rate on eight licensed recordings provided by Ocular.
Cartesia Ink 2, 7.52 points lower
Median delay before the first words arrive, from the first audio packet.
Inworld STT-1, 448ms sooner
Median delay after the speaker stops before the transcript is final.
Inworld STT-1, 82ms sooner
Lower is better on every metric. The small figure is each model's place in the whole field.
Both measured on the same 206 streamed Ocular Dataset turns. P90 and P95 say what a caller meets on a bad turn, which a median hides.
| Measure | Model | P50 | P90 | P95 |
|---|---|---|---|---|
| Time to first text | Cartesia Ink 2 | 1.51s | 2.04s | 3.23s |
| Inworld STT-1 | 1.06s | 1.07s | 1.07s | |
| Final-text delay | Cartesia Ink 2 | 123ms | 139ms | 145ms |
| Inworld STT-1 | 41ms | 60ms | 65ms |
Open on the metric where the two sit furthest apart, with the other three a tab away.
Word error rate on eight licensed recordings provided by Ocular. Lower is better.
| # | Model | Ocular Dataset | clips | |
|---|---|---|---|---|
| 1 | Reson8 | 2.93% | 8 | |
| 2 | Cartesia Ink 2 | 3.09% | 8 | |
| 3 | Smallest Pulse | 3.46% | 8 | |
| 4 | GPT Realtime Whisper | 3.53% | 8 | |
| 5 | Google Chirp 2 | 3.80% | 8 | |
| 6 | Speechmatics Linden | 3.93% | 8 | |
| 7 | Deepgram Flux English | 4.18% | 8 | |
| 8 | Google Chirp 3 | 4.19% | 8 | |
| 9 | AssemblyAI Universal 3.5 Pro | 4.23% | 8 | |
| 10 | Deepgram Nova-3 | 4.39% | 8 | |
| 11 | Gradium | 4.45% | 8 | |
| 12 | GPT-4o Mini Transcribe | 4.45% | 8 | |
| 13 | Deepgram Flux Multilingual | 4.53% | 8 | |
| 14 | Inworld STT-1 | 10.60% | 8 | |
| 15 | GPT-4o Transcribe | 12.45% | 8 |
Counts are the clips each model returned usable text for, so they differ between models and between datasets.
Every model's error rate on the public clips, against the same model on real conversations.
How soon each model returns its first words, against how soon its transcript is final.
| Model | Pipecat clips | Ocular recordings | Turns timed | Failed |
|---|---|---|---|---|
| Cartesia Ink 2 | 1,000 | 8 | 198 | 0 |
| Inworld STT-1 | 1,000 | 8 | 206 | 0 |
A clip counts only where the model returned usable text, so counts differ between models. Word error rate is measured the same way throughout: incorrect, missing, and extra words against the reference transcript.
On the 1,000 public Pipecat clips, Cartesia Ink 2 scores 2.39% and Inworld STT-1 2.72%, so Cartesia Ink 2 makes fewer errors by 0.33 points. On the eight licensed Ocular recordings, Cartesia Ink 2 scores 3.09% and Inworld STT-1 10.60%, so Cartesia Ink 2 makes fewer errors by 7.52 points. Word error rate counts incorrect, missing, and extra words against the reference transcript, so lower is better.
Inworld STT-1 returns first text 448ms sooner, at 1.06s against 1.51s at the median. Inworld STT-1 has the final transcript 82ms sooner, 41ms after the speaker stops against 123ms. Both are measured on the same 206 streamed turns.
Both ran the same two datasets under the same conditions as every other model in the benchmark: 1,000 public clips from Pipecat's STT benchmark dataset and eight licensed conversation recordings from Ocular. Cartesia Ink 2 returned usable text for 1,000 of the public clips and Inworld STT-1 for 1,000. Latency uses each provider's own supported finalization contract.