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TTS-1 HD

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TTS-1 HD is a closed-weights text-to-speech model from OpenAI, released November 2023. Licence: proprietary.

56.5±7.1#49 of 118 text-to-speech modelsfrom 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 TTS-1 HD, out of 148 with a score, then more from OpenAI.

#modelscorewhereRAMreleased
59Sonic 2 Cartesia58±3comparison only2025-03
60F5-TTS Base (v0) SWivid (SJTU X-LANCE)57±2local~1.4GB2024-10
61TTS-1 HD OpenAI57±7api $30.00/M chars2023-11
62Deepgram Aura 2 Deepgram57±3api $30.00/M chars2025-04
63ZONOS2 8B Zyphra57±2local~5.2GB2026-06
more from OpenAI
52TTS-1 OpenAI59±4api $15.00/M chars2023-11
58GPT-4o mini TTS OpenAI58±2comparison only2025-03

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

$ what it's good at

other tests1 not in a task · 80% of its score
testresultvs the others in its rundifficultycountswhy
RW-Voice-EQ TTS · Voice Identity (human)3.46 / 5 ✎█·····▄····█ beats 0/4medium · avg 3.08 / 5▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 80%at its level

$ where to run it

Through an API

api · $30.00/M chars · developers.openai.com ↗ · price sourceUSD per 1M input characters, direct character tariff; not derived from tokens. Product: Audio speech API. API model: tts-1-hd. Settings: endpoint=/v1/audio/speech, tier=standard. Price basis: published rate. Currency: USD.

$ how the score was worked out · RW-Voice-EQ Bench (Hume AI) 80%

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
RW-Voice-EQ Bench (Hume AI)1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 80%CC BY 4.0
prior · pull toward the middle▇▇▇▇▇▇ 20%
weighted average= 56.5

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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