Reson8 vs Speechmatics Linden
Reson8 is ahead on the Pipecat Dataset and the Ocular Dataset, and Speechmatics Linden on time to first text 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.
- Reson8
- 2.10%3rd
- Speechmatics Linden
- 2.46%6th
Reson8, 0.36 points lower
Word error rate on eight licensed recordings provided by Ocular.
- Reson8
- 2.93%1st
- Speechmatics Linden
- 3.93%6th
Reson8, 1.00 points lower
Median delay before the first words arrive, from the first audio packet.
- Reson8
- 2.40s11th
- Speechmatics Linden
- 1.19s7th
Speechmatics Linden, 1.21s sooner
Median delay after the speaker stops before the transcript is final.
- Reson8
- 235ms10th
- Speechmatics Linden
- 147ms6th
Speechmatics Linden, 88ms 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 | Reson8 | 2.40s | 7.97s | 10.20s |
| Speechmatics Linden | 1.19s | 1.56s | 2.52s | |
| Final-text delay | Reson8 | 235ms | 357ms | 398ms |
| Speechmatics Linden | 147ms | 192ms | 213ms |
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 |
|---|---|---|---|---|
| Reson8 | 1,000 | 8 | 201 | 0 |
| Speechmatics Linden | 998 | 8 | 194 | 12 |
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 Reson8 or Speechmatics Linden more accurate?
On the 1,000 public Pipecat clips, Reson8 scores 2.10% and Speechmatics Linden 2.46%, so Reson8 makes fewer errors by 0.36 points. On the eight licensed Ocular recordings, Reson8 scores 2.93% and Speechmatics Linden 3.93%, so Reson8 makes fewer errors by 1.00 points. Word error rate counts incorrect, missing, and extra words against the reference transcript, so lower is better.
Which is faster, Reson8 or Speechmatics Linden?
Speechmatics Linden returns first text 1.21s sooner, at 1.19s against 2.40s at the median. Speechmatics Linden has the final transcript 88ms sooner, 147ms after the speaker stops against 235ms. Both are measured on the same 206 streamed turns.
How were Reson8 and Speechmatics Linden 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. Reson8 returned usable text for 1,000 of the public clips and Speechmatics Linden for 998. Latency uses each provider's own supported finalization contract.