Gradium vs GPT-4o Mini Transcribe
Gradium is ahead on final-text delay, and GPT-4o Mini Transcribe on the Pipecat Dataset. The two tie on the Ocular Dataset.
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.
- Gradium
- 6.48%15th
- GPT-4o Mini Transcribe
- 3.35%9th
GPT-4o Mini Transcribe, 3.12 points lower
Word error rate on eight licensed recordings provided by Ocular.
- Gradium
- 4.45%11th
- GPT-4o Mini Transcribe
- 4.45%11th
Level
Median delay before the first words arrive, from the first audio packet.
- Gradium
- 1.71s10th
- GPT-4o Mini Transcribe
- No text until the speaker stops
Not comparable
Median delay after the speaker stops before the transcript is final.
- Gradium
- 212ms9th
- GPT-4o Mini Transcribe
- 630ms14th
Gradium, 418ms 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 | Gradium | 1.71s | 4.05s | 7.32s |
| GPT-4o Mini Transcribe | No text until the speaker stops | |||
| Final-text delay | Gradium | 212ms | 266ms | 280ms |
| 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 1,000 clips from Pipecat's STT benchmark dataset. Lower is better.
| # | Model | Pipecat Dataset | clips | |
|---|---|---|---|---|
| 1 | AssemblyAI Universal 3.5 Pro | 1.93% | 1,000 | |
| 2 | Google Chirp 3 | 2.00% | 1,000 | |
| 3 | Reson8 | 2.10% | 1,000 | |
| 4 | GPT Realtime Whisper | 2.16% | 1,000 | |
| 5 | Cartesia Ink 2 | 2.39% | 1,000 | |
| 6 | Speechmatics Linden | 2.46% | 998 | |
| 7 | Google Chirp 2 | 2.59% | 1,000 | |
| 8 | Inworld STT-1 | 2.72% | 1,000 | |
| 9 | GPT-4o Mini Transcribe | 3.35% | 1,000 | |
| 10 | Deepgram Nova-3 | 3.46% | 1,000 | |
| 11 | Smallest Pulse | 3.66% | 1,000 | |
| 12 | GPT-4o Transcribe | 3.90% | 1,000 | |
| 13 | Deepgram Flux English | 3.90% | 999 | |
| 14 | Deepgram Flux Multilingual | 4.96% | 999 | |
| 15 | Gradium | 6.48% | 1,000 |
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 |
|---|---|---|---|---|
| Gradium | 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 Gradium or GPT-4o Mini Transcribe more accurate?
On the 1,000 public Pipecat clips, Gradium scores 6.48% and GPT-4o Mini Transcribe 3.35%, so GPT-4o Mini Transcribe makes fewer errors by 3.12 points. On the eight licensed Ocular recordings, Gradium scores 4.45% and GPT-4o Mini Transcribe 4.45%, which is a tie. Word error rate counts incorrect, missing, and extra words against the reference transcript, so lower is better.
Which is faster, Gradium 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. Gradium has the final transcript 418ms sooner, 212ms after the speaker stops against 630ms. Both are measured on the same 206 streamed turns.
How were Gradium 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. Gradium 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.