whatmodel.sh / tts / Neuphonic neu_hq

Neuphonic neu_hq

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Neuphonic neu_hq is a closed-weights text-to-speech model from Neuphonic, released August 2025. Licence: proprietary.

Catalogue identity notes

Vapi registry neuphonic-neu-hq, apiModelId neu_hq. Registry notes no model-specific release date; API v1.0.0 August 2025. Rating-only comparison identity; no price, API or run metadata asserted.

51.8±3.2comparison 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 Neuphonic neu_hq, out of 148 with a score, then more from Neuphonic.

#modelscorewhereRAMreleased
73Kokoro 82M v1.0 hexgrad52±3api $0.62/M chars +local~0.3GB2025-01
74LEMAS-TTS (w/ prosody, multilingual_prosody) LEMAS Project52±6local~1.5GB2026-01
75Neuphonic neu_hq Neuphonic52±3comparison only2025-08
76Spark-TTS 0.5B SparkAudio52±3local~2.9GB2025-02
77Llasa-8B HKUST Audio52±3local~20GB2025-01
more from Neuphonic
122NeuTTS Air Neuphonic36±10local~4GB2025-10

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

$ what it's good at

other tests1 not in a task · 96% of its score
testresultvs the others in its rundifficultycountswhy
Vapi Humanness · Bradley–Terry rating, 2026-09-25 snapshot1102.8 ✎▁···▁▃··█▄▁▇ beats 2/18avg 1078▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 96%typical

$ where to run it

No verified API or download recorded yet · add one

$ how the score was worked out · The Humanness Index by Vapi ·… 96%

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
The Humanness Index by Vapi · September 2026 public standings1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 96%CC BY 4.0
prior · pull toward the middle▇ 4%
weighted average= 51.8

$ 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