whatmodel.sh / tts / ElevenLabs v3 Alpha

ElevenLabs v3 Alpha is a closed-weights text-to-speech model from ElevenLabs, released June 2025.

Catalogue identity notes

Exact figure legend Eleven-v3 (Alpha). Do not relabel Alpha as production v3.

Model information sources

Release date ↗

54.8±8.7comparison only: no current run option verifiedfrom 1 benchmark result across 1 source* thinly tested, treat the rank as provisional

Score within the Vapi Humanness · Bradley–Terry rating, 2026-09-25 snapshot comparison set only.

$ how it compares

The text-to-speech models ranked either side of ElevenLabs v3 Alpha, out of 148 with a score, then more from ElevenLabs.

#modelscorewhereRAMreleased
63ZONOS2 8B Zyphra57±2local~5.2GB2026-06
64Supertonic 3 Supertone55±10local~0.4GB2026-05
65ElevenLabs v3 Alpha ElevenLabs55±9comparison only2025-06
66Higgs Audio V2 Boson AI55±3local~7GB2025-07
67Fasih-TTS-V1 NightPrince (Yahya Elnawasany)55±10local~1.9GB2026-07
more from ElevenLabs
21Eleven v3 ElevenLabs65±1api $100.00/M chars2026-02
30Eleven Turbo v2.5 ElevenLabs64±4api $50.00/M chars2024-07
38Eleven Flash v2.5 ElevenLabs63±2api $50.00/M chars2024-12

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

$ what it's good at

other tests1 not in a task · 69% of its score
testresultvs the others in its rundifficultycountswhy
VibeVoice podcast · preference MOS (MIT figure)3.37 / 5 ✎█·█········█ beats 0/2medium · avg 3.08 / 5▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 69%at its level · small run

$ where to run it

No verified API or download recorded yet · add one

$ how the score was worked out · Microsoft VibeVoice own subje… 69%

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
Microsoft VibeVoice own subjective evaluation figure1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 69%MIT
prior · pull toward the middle▇▇▇▇▇▇▇▇▇ 31%
weighted average= 54.8

$ improve this page

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