Deepgram Flux Multilingual
Deepgram Flux Multilingual is 3rd of 13 on time to first text and 14th of 15 on the Pipecat Dataset.
- 4.96%
- Pipecat WER · 14th
- 4.53%
- Ocular WER · 13th
- 793ms
- First text · 3rd
- 114ms
- Final text · 4th
Quality and speed frontier
Every model's error rate on the public clips, against how long its transcript takes to settle.
Deepgram Flux Multilingual against every other model
Open on the metric Deepgram Flux Multilingual places highest on, with the other three a tab away. Lower is better on all of them, so the order runs best first.
Time to first text, measured from the first audio packet. Lower is better.
| # | Model | First text | turns | |
|---|---|---|---|---|
| 1 | AssemblyAI Universal 3.5 Pro | 489ms | 205 | |
| 2 | Deepgram Flux English | 791ms | 199 | |
| 3 | Deepgram Flux Multilingual | 793ms | 175 | |
| 4 | Smallest Pulse | 1057ms | 206 | |
| 5 | Inworld STT-1 | 1063ms | 206 | |
| 6 | Deepgram Nova-3 | 1064ms | 203 | |
| 7 | Speechmatics Linden | 1190ms | 194 | |
| 8 | GPT Realtime Whisper | 1379ms | 206 | |
| 9 | Cartesia Ink 2 | 1511ms | 198 | |
| 10 | Gradium | 1705ms | 198 | |
| 11 | Reson8 | 2400ms | 201 | |
| 12 | Google Chirp 3 | 4480ms | 206 | |
| 13 | Google Chirp 2 | 4497ms | 205 |
Not ranked: GPT-4o Mini Transcribe and GPT-4o Transcribe return no text until the speaker stops, so first-text time is not comparable.
Median across the same 206 Ocular Dataset turns. Providers finalize differently, so compare models with similar finalization contracts before treating small gaps as meaningful.
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 | 793ms | 2.00s | 3.41s |
| Final-text delay | 114ms | 135ms | 141ms |
Deepgram Flux Multilingual on the other two views
The same charts the benchmark page carries, with Deepgram Flux Multilingual ringed. Consistency across the two datasets is one reading; how fast a model starts against how fast it commits is the other.
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
- 999
- Pipecat clips scored
- 8
- Ocular recordings scored
- 175
- turns timed
- 31
- 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.
Frequently asked questions
Where does Deepgram Flux Multilingual place in the benchmark?
Deepgram Flux Multilingual places highest on time to first text, 3rd of 13 at 793ms. Its weakest placing is the Pipecat Dataset, 14th of 15 at 4.96%.
How accurate is Deepgram Flux Multilingual?
Deepgram Flux Multilingual transcribes the 1,000 public Pipecat clips at 4.96% word error rate and the eight licensed Ocular recordings at 4.53%. Word error rate counts incorrect, missing, and extra words against the reference transcript, so lower is better.
How fast is Deepgram Flux Multilingual in a live conversation?
First text arrives after 793ms at the median, and the transcript is final 114ms after the speaker stops. Both are measured on the same 206 streamed turns as every other model, and the page shows P90 and P95 as well, which is what a caller meets on a bad turn.
How was Deepgram Flux Multilingual 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. Deepgram Flux Multilingual returned usable text for 999 of the public clips.