Google: Gemini 2.5 Pro Preview TTS
Gemini 2.5 Pro Preview TTS is a text to speech model from Google released on May 1, 2025. It reads text and writes audio, with a 8K token context window.
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It costs $1 per million input tokens and $20 per million output tokens.
- Modalities
- In / out price
- $1 / $20 per 1M
- Context
- 8K
- Max output
- 16K
- Released
- May 1, 2025
- Knowledge cutoff
- Jan 2025
- Tool calling
- Structured output
- Reasoning
- Prompt caching
- Open weights
- Free to use
Gemini 2.5 Pro Preview TTS pricing
USD, as listed on Oct 15, 2026. Most bills are input heavy, so the cached input rate matters as much as the headline price.
| Input | $1 / 1M tokens | |
|---|---|---|
| Output | $20 / 1M tokens |
What Gemini 2.5 Pro Preview TTS costs per month
Pick a workload close to yours or type your own volumes.
Supported request parameters
What you can set when you call Gemini 2.5 Pro Preview TTS through an OpenAI compatible API.
- Temperature
temperature
Alternatives to Gemini 2.5 Pro Preview TTS
Models that take the same inputs, closest in price, cheaper ones first.
- 8K context
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by googleApr 15, 2026$1/M input tokens$20/M output tokens2 providers
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- 8K context
Turns text into spoken audio.
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Gemini 2.5 Pro Preview TTS questions
How much does the Gemini 2.5 Pro Preview TTS API cost?
$1 per million input tokens and $20 per million output tokens. The estimator on this page turns that into a monthly bill for your own workload.
What is the context window of Gemini 2.5 Pro Preview TTS?
8,192 tokens of input, and it can write up to 16,384 tokens in one reply. As a rough guide, a million tokens is around 750,000 English words.
Is Gemini 2.5 Pro Preview TTS open source?
No. Google serves it through an API only, so you cannot download the weights or host it yourself.
When was Gemini 2.5 Pro Preview TTS released?
May 1, 2025, with training data up to Jan 2025.
Catalog data from models.dev (MIT licensed), refreshed daily, last on Oct 15, 2026. The summary is written from these facts.
Building on Gemini 2.5 Pro Preview TTS?
I test your real tasks on it and the two closest alternatives, then ship the agent on whichever wins on cost and accuracy.
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