Cartesia Ink 2 vs GPT-4o Mini Transcribe
Cartesia Ink 2 is ahead of GPT-4o Mini Transcribe on the Pipecat Dataset, the Ocular Dataset and final-text delay.
Quality and speed frontier
Every model's error rate on the public clips, against how long its transcript takes to settle.
The four metrics, side by side
Word error rate on 1,000 clips from Pipecat's STT benchmark dataset.
- Cartesia Ink 2
- 2.39%5th
- GPT-4o Mini Transcribe
- 3.35%9th
Cartesia Ink 2, 0.97 points lower
Word error rate on eight licensed recordings provided by Ocular.
- Cartesia Ink 2
- 3.09%2nd
- GPT-4o Mini Transcribe
- 4.45%11th
Cartesia Ink 2, 1.36 points lower
Median delay before the first words arrive, from the first audio packet.
- Cartesia Ink 2
- 1.51s9th
- GPT-4o Mini Transcribe
- No text until the speaker stops
Not comparable
Median delay after the speaker stops before the transcript is final.
- Cartesia Ink 2
- 123ms5th
- GPT-4o Mini Transcribe
- 630ms14th
Cartesia Ink 2, 507ms sooner
Lower is better on every metric. The small figure is each model's place in the whole field.
First text and final text, at three percentiles
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 |
| GPT-4o Mini Transcribe | No text until the speaker stops | |||
| Final-text delay | Cartesia Ink 2 | 123ms | 139ms | 145ms |
| GPT-4o Mini Transcribe | 630ms | 1.34s | 1.53s | |
Both against every other model
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.
Both on the other two views
Pipecat vs Ocular accuracy
Every model's error rate on the public clips, against the same model on real conversations.
Transcription response time
How soon each model returns its first words, against how soon its transcript is final.
What the numbers rest on
| Model | Pipecat clips | Ocular recordings | Turns timed | Failed |
|---|---|---|---|---|
| Cartesia Ink 2 | 1,000 | 8 | 198 | 0 |
| GPT-4o Mini Transcribe | 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.
Frequently asked questions
Is Cartesia Ink 2 or GPT-4o Mini Transcribe more accurate?
On the 1,000 public Pipecat clips, Cartesia Ink 2 scores 2.39% and GPT-4o Mini Transcribe 3.35%, so Cartesia Ink 2 makes fewer errors by 0.97 points. On the eight licensed Ocular recordings, Cartesia Ink 2 scores 3.09% and GPT-4o Mini Transcribe 4.45%, so Cartesia Ink 2 makes fewer errors by 1.36 points. Word error rate counts incorrect, missing, and extra words against the reference transcript, so lower is better.
Which is faster, Cartesia Ink 2 or GPT-4o Mini Transcribe?
GPT-4o Mini Transcribe returns no text while the speaker is talking, so time to first text cannot be compared. Cartesia Ink 2 has the final transcript 507ms sooner, 123ms after the speaker stops against 630ms. Both are measured on the same 206 streamed turns.
How were Cartesia Ink 2 and GPT-4o Mini Transcribe compared?
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 GPT-4o Mini Transcribe for 1,000. Latency uses each provider's own supported finalization contract.