Deepgram Flux Multilingual vs Inworld STT-1
Deepgram Flux Multilingual is ahead on time to first text, and Inworld STT-1 on final-text delay. The Pipecat Dataset and The Ocular Dataset are 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
Pick Deepgram Flux Multilingual for showing words while the caller is still talking (270ms sooner). Pick Inworld STT-1 for quick turn-taking once the caller stops (73ms sooner). For accuracy on clean, scripted audio and 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
- Inworld STT-1
- 2.72%10th
Too close to call: inside the margin of error
- Deepgram Flux Multilingual
- 4.53%15th
- Inworld STT-1
- 10.60%16th
Too close to call: inside the margin of error
- Deepgram Flux Multilingual
- 793ms4th
- Inworld STT-1
- 1.06s6th
Deepgram Flux Multilingual, 270ms sooner
- Deepgram Flux Multilingual
- 114ms6th
- Inworld STT-1
- 41ms2nd
Inworld STT-1, 73ms 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 Inworld STT-1 shows first text after 1.07s and final text after 65ms, 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 |
| Inworld STT-1 | 1.06s | 1.07s | 1.07s | |
| Final-text delay | Deepgram Flux Multilingual | 114ms | 135ms | 141ms |
| Inworld STT-1 | 41ms | 60ms | 65ms |
- 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 | STT-1 |
|---|---|---|
| Pipecat Dataset WER | 4.96%16th of 17 | 2.72%10th of 17 |
| Ocular Dataset WER | 4.53%15th of 17 | 10.60%16th of 17 |
| Time to first text | 793ms4th of 15 | 1.06s6th of 15 |
| Final-text delay | 114ms6th of 17 | 41ms2nd 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 |
| Inworld STT-1 | 1,000 | 8 | 206 | 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 Gradium
- Deepgram Flux Multilingual vs GPT-4o Mini Transcribe
- 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
- Inworld STT-1 vs Soniox STT-RT v5
- Inworld STT-1 vs Speechmatics Linden
- Inworld STT-1 vs Cartesia Ink 2
- Inworld STT-1 vs GPT Realtime Whisper
- Inworld STT-1 vs Reson8
- Inworld STT-1 vs GPT-4o Mini Transcribe
- Inworld STT-1 vs Google Chirp 3
- Inworld STT-1 vs Deepgram Nova-3
- Inworld STT-1 vs Smallest Pulse
- Inworld STT-1 vs AssemblyAI Universal 3.6 Pro
- Inworld STT-1 vs GPT-4o Transcribe
- Inworld STT-1 vs Deepgram Flux English
- Inworld STT-1 vs Gradium
Frequently asked questions
Which is more accurate, Deepgram Flux Multilingual or Inworld STT-1?
Deepgram Flux Multilingual and Inworld STT-1 are too close to call on accuracy. On the 1,000 public Pipecat clips, Deepgram Flux Multilingual scores 4.96% and Inworld STT-1 2.72%, with likely ranges of 4.49 to 5.46% and 1.67 to 4.68%. The ranges overlap, so the difference is too close to call. On the eight licensed Ocular recordings, Deepgram Flux Multilingual scores 4.53% and Inworld STT-1 10.60%, with likely ranges of 3.29 to 5.69% and 3.85 to 22.76%. 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 Inworld STT-1?
No. Inworld STT-1 finishes the transcript 73ms sooner. Deepgram Flux Multilingual has the final transcript 114ms after the speaker stops and Inworld STT-1 41ms, at the median. Both are measured on the same 206 streamed turns.
Is Deepgram Flux Multilingual or Inworld STT-1 better for voice agents?
Inworld STT-1 finishes the transcript 73ms 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 Inworld STT-1 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 Inworld STT-1 after 1.06s, at the median, so Deepgram Flux Multilingual is 270ms sooner.