Deepgram Flux Multilingual vs Gradium
Deepgram Flux Multilingual 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 Multilingual is the better pick over Gradium on every measure that separates them: accuracy on clean, scripted audio (1.52 points lower), showing words while the caller is still talking (912ms sooner) and quick turn-taking once the caller stops (98ms 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 Multilingual
- 4.96%16th
- Gradium
- 6.48%17th
Deepgram Flux Multilingual, 1.52 points lower
- Deepgram Flux Multilingual
- 4.53%15th
- Gradium
- 4.45%12th
Too close to call: inside the margin of error
- Deepgram Flux Multilingual
- 793ms4th
- Gradium
- 1.71s12th
Deepgram Flux Multilingual, 912ms sooner
- Deepgram Flux Multilingual
- 114ms6th
- Gradium
- 212ms11th
Deepgram Flux Multilingual, 98ms 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 Multilingual shows first text after 3.41s 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 Multilingual | 793ms | 2.00s | 3.41s |
| Gradium | 1.71s | 4.05s | 7.32s | |
| Final-text delay | Deepgram Flux Multilingual | 114ms | 135ms | 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 Multilingual | Gradium |
|---|---|---|
| Pipecat Dataset WER | 4.96%16th of 17 | 6.48%17th of 17 |
| Ocular Dataset WER | 4.53%15th of 17 | 4.45%12th of 17 |
| Time to first text | 793ms4th of 15 | 1.71s12th of 15 |
| Final-text delay | 114ms6th of 17 | 212ms11th of 17 |
How much audio each model was tested on
| Model | Pipecat clips | Ocular recordings | Turns timed | Failed |
|---|---|---|---|---|
| Deepgram Flux Multilingual | 999 | 8 | 175 | 31 |
| 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 Multilingual vs Deepgram Flux English
- Deepgram Flux Multilingual vs GPT-4o Transcribe
- Deepgram Flux Multilingual vs Smallest Pulse
- Deepgram Flux Multilingual vs Deepgram Nova-3
- Deepgram Flux Multilingual vs GPT-4o Mini Transcribe
- Deepgram Flux Multilingual vs Inworld STT-1
- Deepgram Flux Multilingual vs Soniox STT-RT v5
- Deepgram Flux Multilingual vs Speechmatics Linden
- Deepgram Flux Multilingual vs Cartesia Ink 2
- Deepgram Flux Multilingual vs GPT Realtime Whisper
- Deepgram Flux Multilingual vs Reson8
- Deepgram Flux Multilingual vs Google Chirp 3
- Deepgram Flux Multilingual vs AssemblyAI Universal 3.6 Pro
- Gradium vs Deepgram Flux English
- 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 Multilingual or Gradium?
Deepgram Flux Multilingual is more accurate on the public clips. On the 1,000 public Pipecat clips, Deepgram Flux Multilingual scores 4.96% and Gradium 6.48%, so Deepgram Flux Multilingual makes fewer errors by 1.52 points. On the eight licensed Ocular recordings, Deepgram Flux Multilingual scores 4.53% and Gradium 4.45%, with likely ranges of 3.29 to 5.69% 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 Multilingual faster than Gradium?
Yes. Deepgram Flux Multilingual finishes the transcript 98ms sooner. Deepgram Flux Multilingual has the final transcript 114ms after the speaker stops and Gradium 212ms, at the median. Both are measured on the same 206 streamed turns.
Is Deepgram Flux Multilingual or Gradium better for voice agents?
Deepgram Flux Multilingual finishes the transcript 98ms 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 Multilingual and Gradium stream text while the speaker is talking?
Yes, both return partial text before the speaker stops. Deepgram Flux Multilingual shows first text after 793ms and Gradium after 1.71s, at the median, so Deepgram Flux Multilingual is 912ms sooner.