Reson8
Reson8 is 1st of 15 on the Ocular Dataset and 11th of 13 on time to first text.
- 2.10%
- Pipecat WER · 3rd
- 2.93%
- Ocular WER · 1st
- 2.40s
- First text · 11th
- 235ms
- Final text · 10th
Quality and speed frontier
Every model's error rate on the public clips, against how long its transcript takes to settle.
Reson8 against every other model
Open on the metric Reson8 places highest on, with the other three a tab away. Lower is better on all of them, so the order runs best first.
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.
First text and final text, at three percentiles
Measured on the same 206 streamed Ocular Dataset turns as every other model. P90 and P95 say what a caller meets on a bad turn, which a median hides.
| Measure | P50 | P90 | P95 |
|---|---|---|---|
| Time to first text | 2.40s | 7.97s | 10.20s |
| Final-text delay | 235ms | 357ms | 398ms |
Reson8 on the other two views
The same charts the benchmark page carries, with Reson8 ringed. Consistency across the two datasets is one reading; how fast a model starts against how fast it commits is the other.
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
- 1,000
- Pipecat clips scored
- 8
- Ocular recordings scored
- 201
- turns timed
- 0
- requests that failed
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
Where does Reson8 place in the benchmark?
Reson8 places highest on the Ocular Dataset, 1st of 15 at 2.93%. Its weakest placing is time to first text, 11th of 13 at 2.40s.
How accurate is Reson8?
Reson8 transcribes the 1,000 public Pipecat clips at 2.10% word error rate and the eight licensed Ocular recordings at 2.93%. Word error rate counts incorrect, missing, and extra words against the reference transcript, so lower is better.
How fast is Reson8 in a live conversation?
First text arrives after 2.40s at the median, and the transcript is final 235ms after the speaker stops. Both are measured on the same 206 streamed turns as every other model, and the page shows P90 and P95 as well, which is what a caller meets on a bad turn.
How was Reson8 measured?
Every model runs the same two datasets under the same conditions: 1,000 public clips from Pipecat's STT benchmark dataset for accuracy at scale, and eight licensed conversation recordings from Ocular for real speech. Latency comes from 206 streamed turns, using each provider's own supported finalization contract. Reson8 returned usable text for 1,000 of the public clips.