Realtime ranker

Streaming transcription, ranked.

Every realtime model. Same audio. Scored on responsiveness, stability, and accuracy — the axes that matter when words appear as you speak.

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1

Models Ranked

1

Total Benchmarks

11

Languages Tested

Jul 14, 2026

Last Updated

#ModelScoreTTFPFlickerDrainWERRuns

01

Flux

Deepgram

27.2

290 ms

3050.0%

0 ms

98.41%

1

01

Flux

27.2

290 ms TTFP

3050.0% flicker

0 ms drain

98.41% WER

How the realtime score is built

weighted composite

50%

Accuracy

WER vs. reference transcripts, streamed live

25%

Responsiveness

Median time to first partial transcript

20%

Stability

How often earlier partials get revised before finalizing

5%

Tail latency

Final-chunk drain time after audio stops

Streaming metrics

how responsiveness is measured

TTFP

Time to First Partial

Time from audio start to first partial transcript (P50)

How quickly the model starts showing words (lower is better)

Flicker

Flicker

Revised words ÷ total emitted words

How often earlier partial words change before the transcript finalizes (lower is better)

Cadence

Cadence

Partial emissions per second of audio

How often the model updates its partial transcript — descriptive context, not scored

RTF

Real-Time Factor

Processing time ÷ audio duration, at 1× playback pace

Reads ≈1.0 for a well-behaved realtime model since audio is streamed at real-time pace — not a 'lower is always better' metric like batch speed factor

Route to whichever model wins.

One endpoint, every provider. Pin the leader or let us auto-route to the best model under your accuracy and latency budget.

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