whatmodel.sh / stt / Zoom Scribe v1

Zoom Scribe v1

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Zoom Scribe v1 is a closed-weights speech-to-text model from Zoom, released March 2026. Licence: proprietary.

Catalogue identity notes

Zoom Scribe API (POST https://api.zoom.us/v2/aiservices/scribe/transcribe) as run by the leaderboard's zoom_provider.py, which sets experimental_feature {model_pro: true}. The public API names no model version; 'scribe_v1' is the leaderboard label.

Model information sources

Release date ↗

63.6±5.1#12 of 162 speech-to-text modelsfrom 1 benchmark result across 1 source* thinly tested, treat the rank as provisional

Score within the Open ASR English short-form WER (March 2026) comparison set only.

$ how it compares

The speech-to-text models ranked either side of Zoom Scribe v1, out of 176 with a score.

#modelscorewhereRAMreleased
12Scribe v2 ElevenLabs66±1api $0.0037/audio min2026-01
13GPT Transcribe OpenAI64±4api $0.0045/audio min2026-07
14Zoom Scribe v1 Zoom64±5api price unknown2026-03
15Granite Speech 5.0 470M TurboCTC NC IBM64±4local~0.5GB2026-08
16Cohere Transcribe Arabic (07-2026) Cohere63±3local~4.1GB2026-07

see all speech-to-text models · find the ones that fit your machine

$ what it's good at

other tests1 not in a task · 90% of its score
testresultvs the others in its rundifficultycountswhy
Open ASR English short-form WER (March 2026)5.5% ✎▁▁▁▃··▁▁▂██▆ beats 85/86avg 8.2%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 90%blunt

$ where to run it

Through an API

api · price unknown · developers.zoom.us ↗

$ how the score was worked out · Open ASR Leaderboard 90%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
Open ASR Leaderboard1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 90%Apache-2.0
prior · pull toward the middle▇▇▇ 10%
weighted average= 63.6

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json