Deepgram Flux English vs Gradium
Deepgram Flux English is ahead of Gradium on the Pipecat Dataset, time to first text and final-text delay. The Ocular Dataset is too close to call.
Last updated Methodology by Dileep Chagam
Accuracy vs speed
Each dot is one model. Bottom-left is best: fewer errors and a faster final transcript.
Which to pick
Deepgram Flux English is the better pick over Gradium on every measure that separates them: accuracy on clean, scripted audio (2.57 points lower), showing words while the caller is still talking (914ms sooner) and quick turn-taking once the caller stops (104ms sooner). For accuracy on real phone conversations, the gap is inside the margin of error, so neither is ahead.
The four metrics, side by side
- Deepgram Flux English
- 3.90%15th
- Gradium
- 6.48%17th
Deepgram Flux English, 2.57 points lower
- Deepgram Flux English
- 4.18%8th
- Gradium
- 4.45%12th
Too close to call: inside the margin of error
- Deepgram Flux English
- 791ms3rd
- Gradium
- 1.71s12th
Deepgram Flux English, 914ms sooner
- Deepgram Flux English
- 108ms5th
- Gradium
- 212ms11th
Deepgram Flux English, 104ms sooner
Lower is better on every metric. The small figure is each model's place in the whole field. What each metric measures.
- 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.
- 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
Deepgram Flux English shows first text after 1.51s and final text after 141ms, and Gradium shows first text after 7.32s and final text after 280ms, on a bad turn (P95) across the same streamed turns.
| Measure | Model | P50 | P90 | P95 |
|---|---|---|---|---|
| Time to first text | Deepgram Flux English | 791ms | 1.23s | 1.51s |
| Gradium | 1.71s | 4.05s | 7.32s | |
| Final-text delay | Deepgram Flux English | 108ms | 132ms | 141ms |
| Gradium | 212ms | 266ms | 280ms |
- 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.
How each ranks among all 17 models
Place among the 17 models on each metric. Lower is better on all four.
| Metric | Flux English | Gradium |
|---|---|---|
| Pipecat Dataset WER | 3.90%15th of 17 | 6.48%17th of 17 |
| Ocular Dataset WER | 4.18%8th of 17 | 4.45%12th of 17 |
| Time to first text | 791ms3rd of 15 | 1.71s12th of 15 |
| Final-text delay | 108ms5th of 17 | 212ms11th of 17 |
How much audio each model was tested on
| Model | Pipecat clips | Ocular recordings | Turns timed | Failed |
|---|---|---|---|---|
| Deepgram Flux English | 999 | 8 | 199 | 7 |
| Gradium | 1,000 | 8 | 198 | 0 |
Only clips with usable text count, so the two totals can differ. How WER is scored.
Related comparisons
- Deepgram Flux English vs GPT-4o Transcribe
- Deepgram Flux English vs Smallest Pulse
- Deepgram Flux English vs Deepgram Nova-3
- Deepgram Flux English vs GPT-4o Mini Transcribe
- Deepgram Flux English vs Deepgram Flux Multilingual
- Deepgram Flux English vs Inworld STT-1
- Deepgram Flux English vs Soniox STT-RT v5
- Deepgram Flux English vs Speechmatics Linden
- Deepgram Flux English vs Cartesia Ink 2
- Deepgram Flux English vs GPT Realtime Whisper
- Deepgram Flux English vs Reson8
- Deepgram Flux English vs Google Chirp 3
- Deepgram Flux English vs AssemblyAI Universal 3.6 Pro
- Gradium vs Deepgram Flux Multilingual
- Gradium vs GPT-4o Transcribe
- Gradium vs Smallest Pulse
- Gradium vs Deepgram Nova-3
- Gradium vs GPT-4o Mini Transcribe
- Gradium vs Inworld STT-1
- Gradium vs Soniox STT-RT v5
- Gradium vs Speechmatics Linden
- Gradium vs Cartesia Ink 2
- Gradium vs GPT Realtime Whisper
- Gradium vs Reson8
- Gradium vs Google Chirp 3
- Gradium vs AssemblyAI Universal 3.6 Pro
Frequently asked questions
Which is more accurate, Deepgram Flux English or Gradium?
Deepgram Flux English is more accurate on the public clips. On the 1,000 public Pipecat clips, Deepgram Flux English scores 3.90% and Gradium 6.48%, so Deepgram Flux English makes fewer errors by 2.57 points. On the eight licensed Ocular recordings, Deepgram Flux English scores 4.18% and Gradium 4.45%, with likely ranges of 3.08 to 5.23% and 3.17 to 5.90%. The ranges overlap, so the difference is too close to call. Word error rate counts incorrect, missing, and extra words, so lower is better.
Is Deepgram Flux English faster than Gradium?
Yes. Deepgram Flux English finishes the transcript 104ms sooner. Deepgram Flux English has the final transcript 108ms after the speaker stops and Gradium 212ms, at the median. Both are measured on the same 206 streamed turns.
Is Deepgram Flux English or Gradium better for voice agents?
Deepgram Flux English finishes the transcript 104ms sooner, and the two are too close to call on real conversations. A voice agent cannot reply until the final transcript arrives, and the Ocular recordings are real conversations.
Do Deepgram Flux English and Gradium stream text while the speaker is talking?
Yes, both return partial text before the speaker stops. Deepgram Flux English shows first text after 791ms and Gradium after 1.71s, at the median, so Deepgram Flux English is 914ms sooner.