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Eleven Multilingual v2

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Eleven Multilingual v2 is a closed-weights text-to-speech model from ElevenLabs, released August 2023. Licence: proprietary.

50.5±4.5#65 of 118 text-to-speech modelsfrom 3 benchmark results across 2 sources

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 Eleven Multilingual v2, out of 148 with a score, then more from ElevenLabs.

#modelscorewhereRAMreleased
78Gradium TTS Gradium51±3api $38.00/M chars2025-12
79OpenVoice V1 MyShell51±14local~0.3GB2024-01
80Eleven Multilingual v2 ElevenLabs51±5api $100.00/M chars2023-08
81Azure Ameha Neural Microsoft50±16comparison only2021-10
82Azure Mekdes Neural Microsoft50±16comparison only2021-10
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 tests3 not in a task · 92% of its score
testresultvs the others in its rundifficultycountswhy
EmergentTTS · 6-category preference33.6% ✎▅▅▃▃█▅▅▃·▃·▅ beats 10/16hard · avg 30%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 59%at its level
EmergentTTS · v1 paper automatic MOS (WV-MOS)3.55 / 5 ✎▄·█·█▄····█▄ beats 3/8easy · avg 3.93 / 5▇▇▇▇▇▇▇ 18%blunt · at its level
EmergentTTS · 6-category WER11.2% ✎▂▄··▂▄▄··█▄▆ beats 13/16avg 16%▇▇▇▇▇▇ 15%blunt

$ where to run it

Through an API

api · $100.00/M chars · elevenlabs.io ↗ · price sourceUSD per 1M input characters: ElevenLabs lists v2 Multilingual Text to Speech at $0.10 per 1K characters (x1000). Product: ElevenAPI text to speech. API model: eleven_multilingual_v2. Settings: standard API rate (same per-1K rate on pay-as-you-go and plan tiers shown). Price basis: published list rate, per-1K to per-1M scaling only. Exclusions: subscription fees, taxes. Currency: USD. ElevenLabs official API pricing page, observed 2026-09-25.

$ how the score was worked out · EmergentTTS-Eval six-category… 74%, EmergentTTS-Eval v1 paper (ar… 18%

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
EmergentTTS-Eval six-category leaderboard2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 74%Apache-2.0
EmergentTTS-Eval v1 paper (arXiv:2505.23009v1) · Table 1 automatic MOS1▇▇▇▇▇ 18%CC BY 4.0
prior · pull toward the middle▇▇ 8%
weighted average= 50.5

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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