GPT-4o Transcribe
GPT-4o Transcribe is 14th of 17 on the Pipecat Dataset and 17th of 17 on final-text delay.
Last updated Methodology by Dileep Chagam
- 3.90%
- Pipecat WER · 14th
- 12.45%
- Ocular WER · 17th
- 652ms
- Final text · 17th
Likely range, with 95% confidence: Pipecat WER 3.43 to 4.42%, Ocular WER 7.23 to 19.58%.
Accuracy vs speed
Each dot is one model. Bottom-left is best: fewer errors and a faster final transcript.
GPT-4o Transcribe against every other model
Open on the metric GPT-4o Transcribe 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 1,000 clips from Pipecat's STT benchmark dataset. Lower is better.
| # | Model | Pipecat Dataset | clips | |
|---|---|---|---|---|
| 1 | AssemblyAI Universal 3.6 Pro | 1.77% | 1,000 | |
| 2 | AssemblyAI Universal 3.5 Pro | 1.93% | 1,000 | |
| 3 | Google Chirp 3 | 2.00% | 1,000 | |
| 4 | Reson8 | 2.10% | 1,000 | |
| 5 | GPT Realtime Whisper | 2.16% | 1,000 | |
| 6 | Cartesia Ink 2 | 2.39% | 1,000 | |
| 7 | Speechmatics Linden | 2.46% | 998 | |
| 8 | Soniox STT-RT v5 | 2.54% | 1,000 | |
| 9 | Google Chirp 2 | 2.59% | 1,000 | |
| 10 | Inworld STT-1 | 2.72% | 1,000 | |
| 11 | GPT-4o Mini Transcribe | 3.35% | 1,000 | |
| 12 | Deepgram Nova-3 | 3.46% | 1,000 | |
| 13 | Smallest Pulse | 3.66% | 1,000 | |
| 14 | GPT-4o Transcribe | 3.90% | 1,000 | |
| 15 | Deepgram Flux English | 3.90% | 999 | |
| 16 | Deepgram Flux Multilingual | 4.96% | 999 | |
| 17 | Gradium | 6.48% | 1,000 |
Counts are the clips each model returned usable text for, so they differ between models and between datasets.
- WER · Word error rate
- Share of words the model got wrong: replaced, missing or added. Lower is better.
- Pipecat Dataset
- 1,000 short public clips from Pipecat's STT benchmark dataset.
- Ocular Dataset
- 8 recordings from 4 real phone conversations, licensed from Ocular.
- Likely range · 95% range
- Where the model's true score probably falls, given how much audio was tested. Overlapping ranges are too close to call.
- TTFT · Time to first text
- How long after the audio starts streaming until the first words appear.
- TTFS · Final-text delay
- How long after the caller stops until the full transcript arrives. A voice agent waits for this before it replies.
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 | No text until the speaker stops | ||
| Final-text delay | 652ms | 1.73s | 1.97s |
- P50 / P90 / P95 · Typical, slow and slowest turns
- Half of turns are faster than P50. Only 1 in 10 is slower than P90, and 1 in 20 slower than P95.
GPT-4o Transcribe on the other two views
The same charts the benchmark page carries, with GPT-4o Transcribe ringed. Consistency across the two datasets is one reading; how fast a model starts against how fast it commits is the other.
Public clips vs real calls
Each dot is one model. Bottom-left is best on both datasets.
First words vs final transcript
Each dot is one model. Bottom-left is fastest at both.
How much audio it was tested on
- 1,000
- Pipecat clips scored
- 8
- Ocular recordings scored
- 206
- 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.
GPT-4o Transcribe head to head
- vs AssemblyAI Universal 3.6 Pro
- vs AssemblyAI Universal 3.5 Pro
- vs Google Chirp 3
- vs Reson8
- vs GPT Realtime Whisper
- vs Cartesia Ink 2
- vs Speechmatics Linden
- vs Soniox STT-RT v5
- vs Google Chirp 2
- vs Inworld STT-1
- vs GPT-4o Mini Transcribe
- vs Deepgram Nova-3
- vs Smallest Pulse
- vs Deepgram Flux English
- vs Deepgram Flux Multilingual
- vs Gradium
Frequently asked questions
Where does GPT-4o Transcribe place in the benchmark?
GPT-4o Transcribe places highest on the Pipecat Dataset, 14th of 17 at 3.90%. Its weakest placing is final-text delay, 17th of 17 at 652ms.
How accurate is GPT-4o Transcribe?
GPT-4o Transcribe transcribes the 1,000 public Pipecat clips at 3.90% word error rate and the eight licensed Ocular recordings at 12.45%. Word error rate counts incorrect, missing, and extra words against the reference transcript, so lower is better.
Why is there no time to first text for GPT-4o Transcribe?
GPT-4o Transcribe returns no text at all while the speaker is talking, so a measured time to first text would track how long each turn happened to be rather than anything about the model. Its final-text delay is still comparable and is reported in full.
How was GPT-4o Transcribe 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. GPT-4o Transcribe returned usable text for 1,000 of the public clips.