Aura-2 API pricing
Aura-2 by Deepgram, priced on Deepgram API (pay as you go).
Aura-2 API pricing starts at $30.00/1M characters (official pricing page, verified Oct 9, 2026).
| Voice type | Price | 100,000 characters | 1,000,000 characters |
|---|---|---|---|
| Single price | $30.00/1M characters | $3.00 | $30.00 |
Verified against the official pricing page on Oct 9, 2026. Official pricing page
Prices here are converted to one unit per category so models can be compared; the official figures are below. The official list price is $0.03 per 1K characters.
Examples are unit price × length or count. They do not include retries, free allowances, or inputs and options that the official page prices separately.
Other text-to-speech models
- Google Cloud Text-to-Speech · $4.00/1M characters
- Aura-1 · $15.00/1M characters
- TTS-1 · $15.00/1M characters
- TTS-1 HD · $30.00/1M characters
- Eleven Flash / Turbo · $40.00/1M characters
- Eleven v3 Conversational · $40.00/1M characters
- Eleven v4 Turbo · $40.00/1M characters
- Flux TTS · $45.00/1M characters
- MiniMax Speech 2.8 Turbo · $60.00/1M characters
- Eleven Multilingual v2 · $80.00/1M characters
- Eleven v3 · $80.00/1M characters
- Eleven v4 · $80.00/1M characters
- MiniMax Speech 2.8 HD · $100.00/1M characters
Starting prices from each model's official pricing page, verified Oct 9, 2026. Every tier and its source are on the model's own page.
Questions
- How much does Aura-2 cost per 1M characters?
- Aura-2: $30.00/1M characters.
- How much does it cost to turn 100,000 characters into speech with Aura-2?
- At the listed price, 100,000 characters with Aura-2 cost $3.00, before any separately priced inputs, options or retries.
- Are there lower-priced alternatives to Aura-2?
- Among the models listed on modelpick, these start lower: Google Cloud Text-to-Speech (Standard · legacy, $4.00/1M characters), Aura-1 ($15.00/1M characters), TTS-1 ($15.00/1M characters) (official pricing pages, verified Oct 9, 2026). Starting prices can be for a different tier, and output quality differs; compare the same tier and samples before switching.