whatmodel.sh / stt / Fun-ASR-Flash

Fun-ASR-Flash

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Fun-ASR-Flash is a closed-weights speech-to-text model from Alibaba.

Catalogue identity notes

Exact API label 'Fun-ASR-Flash' in Table 1 Panel B of the Qwen-Audio-3.0-ASR report (Fun-ASR is that team's predecessor family). API build/date not stated.

59.4±4.2comparison only: no current run option verifiedfrom 2 benchmark results 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 Fun-ASR-Flash, out of 176 with a score, then more from Alibaba.

#modelscorewhereRAMreleased
42Speechmatics Enhanced Speechmatics60±2api $0.0067/audio min2023-03
43Deepgram Nova 2 Deepgram60±4api $0.0058/audio min2023-09
44Fun-ASR-Flash Alibaba59±4comparison only
45Granite Speech 3.3 8B IBM59±5local~21GB2025-06
46Voxtral Mini Transcribe V2 Mistral59±3api $0.0030/audio min2026-02
more from Alibaba
24Qwen-Audio-3.0-ASR Alibaba62±4comparison only2026-09

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

$ what it's good at

other tests2 not in a task · 93% of its score
testresultvs the others in its rundifficultycountswhy
Qwen-Audio-3.0-ASR report · Chinese 4-set CER mean (own API runs) lab3.0% ✎▄········▄·█ beats 1/3avg 6.7%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 55%sharp
Qwen-Audio-3.0-ASR report · English 3-set WER mean (own API runs) lab2.9% ✎█···█··█···█ beats 2/3avg 5.6%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 38%typical

$ where to run it

No verified API or download recorded yet · add one

$ how the score was worked out · Qwen-Audio-3.0-ASR Technical… 93%

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
Qwen-Audio-3.0-ASR Technical Report (arXiv:2609.07549v2) · lab-reported2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 93%CC BY 4.0
prior · pull toward the middle▇▇ 7%
weighted average= 59.4

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

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data updated · sources and licences · json