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.

Bench

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Deepgram Flux Multilingual

Every modelDeepgram

Accuracy vs. speed

Lower and further left is better · Nemotron 3.5 Transcribe doesn't support streaming, so it is not shown

Best quadrantBest picks

Final-text delay P50 (ms)

Overall WER (%)

Deepgram Flux Multilingual is 5th of 16 on time to first text and 18th of 19 on the Pipecat Dataset.

4.81% Overall WER (16th) · 4.96% Pipecat WER (18th) · 4.53% Ocular WER (17th) · 793ms First text (5th) · 114ms Final text (6th)Likely range, 95% confidence: Pipecat WER 4.49 to 5.46%, Ocular WER 3.29 to 5.69%Published price $0.47 per hour of streamed audio. Pay as you go. Source, checked Oct 8, 2026.

Last updated Sep 16, 2026Methodology by Dileep Chagam

Deepgram Flux Multilingual against every other model

Lower is better on every measure. Hover a column for that model's full numbers.

NVIDIA Nemotron 3.5 Transcribe and GPT-4o Mini Transcribe and GPT-4o Transcribe send no text until the speaker stops, so TTFT is not comparable.

Glossary: WER, TTFT, TTFS, P50

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.
Likely range · 95% range: Where the model's true score probably falls, given how much audio was tested. Overlapping ranges are too close to call.
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.
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.

Where Deepgram Flux Multilingual struggled

Wrong words are its biggest source of error on both real calls and clean clips. For costs, see our breakdown of Deepgram pricing.

43%

of its errors on the Ocular calls are wrong words.

Median across all 19 models: 34%

Mix of errors

Real calls (Ocular Dataset)

Clean clips (Pipecat Dataset)

TTFT and TTFS at P50, P90 and P95

Timed on the same 206 streamed Ocular turns as every other model. P90 and P95 show what a caller meets on a slow turn.

TTFS, final text

How long after the caller stops until the full transcript arrives. A voice agent waits for this before it replies.

20ms50ms100ms200ms500ms1s2s

Typical, P50: 114ms; 6th fastest of 18
Slow, P90: 135ms; 4th fastest of 18
Slowest, P95: 141ms; 3rd fastest of 18

TTFT, first text

How long after the audio starts streaming until the first words appear.

200ms500ms1s2s5s10s20s

Typical, P50: 793ms; 5th fastest of 16
Slow, P90: 2.00s; 10th fastest of 16
Slowest, P95: 3.41s; 12th fastest of 16

Every other model's typical turn, on the same axis.

999 Pipecat clips · 8 Ocular recordings · 175 turns timed · 31 failed requests

Deepgram Flux Multilingual FAQ

Where does Deepgram Flux Multilingual place in the benchmark?

Deepgram Flux Multilingual places highest on time to first text, 5th of 16 at 793ms. Its weakest placing is the Pipecat Dataset, 18th of 19 at 4.96%.

Deepgram Flux Multilingual places highest on time to first text, 5th of 16 at 793ms. Its weakest placing is the Pipecat Dataset, 18th of 19 at 4.96%.
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.
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.
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.

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