Gemini 3.8 Flash-Lite TTS

Gemini 3.8 Flash-Lite TTS (gemini-3.8-flash-lite-tts) is Google's fast, cost-efficient workhorse text-to-speech model built to replace gemini-3.1-flash-tts-preview for high-throughput production workloads.

Overview and capabilities

Gemini 3.8 Flash-Lite TTS combines low latency with expressive, natural speech:

  • High-throughput efficiency: Optimized for bulk production, conversational voice agent cascades, read-aloud features, and everyday single-speaker speech generation across major languages.
  • Drop-in schema compatibility: Shares the exact same API schema and structured prompting format as gemini-3.8-flash-tts, allowing you to switch models with a single parameter change.
  • Full voice ecosystem support: Supports prebuilt voices, the Extended Voice Library (GET /v1beta/voices), custom Voice design personas, and Voice replication (persistent stored voices by default, plus optional stateless keys). You can also design and replicate voices interactively in Google AI Studio.

Visit the Text-to-speech guide for full coverage of features, prompting best practices, and code examples.

When to use which TTS model

Both Gemini 3.8 TTS models share the same API schema and prompting structure. Choose the model that fits your workload:

Feature / workload Gemini 3.8 Flash-Lite TTS (gemini-3.8-flash-lite-tts) Gemini 3.8 Flash TTS (gemini-3.8-flash-tts)
Primary strength High throughput, low latency, and cost efficiency Maximum voice fidelity, acting nuance, and dialect coverage
Best use cases High-volume production, real-time voice agent cascades, read-aloud features, voice replication, everyday single-speaker generation Audiobooks, studio narration, complex multi-speaker dialogue, heavy vocal-burst acting, difficult pronunciation, regional dialects
Supported languages 101 languages 130 languages
Recommended replacement for gemini-3.1-flash-tts-preview New flagship creative tier

Migration guide

If you are upgrading from gemini-3.1-flash-tts-preview to gemini-3.8-flash-lite-tts, update your requests for the Gemini 3.8 TTS schema:

  1. Move turn-level directions into speech_metadata: Gemini 3.8 TTS treats input text strictly as a verbatim transcript. Inline text directions like "Say cheerfully: Hello!" or "Speaker 1: Hello!" may be spoken aloud. Move sustained delivery instructions (style) and speaker labels (speaker) into structured metadata:
    • Interactions API: Attach an annotation with "type": "speech_metadata", "speaker", and "style" to each text content block.
    • GenerateContent API: Attach "speech_metadata": {"speaker": "...", "style": "..."} to each part.
  2. Use angle-bracket inline tags only for point-in-time vocal events: Keep momentary non-speech vocalizations and pauses inline in the transcript using angle brackets (such as <laugh>, <sigh>, <cough>, <breath>, or <short pause>). Put delivery styles like whispering in speech_metadata.style.
  3. Specify speaker on every turn in multi-speaker requests: Every turn in a multi-speaker request must explicitly include speaker inside speech_metadata matching one of the configured speakers.
  4. Design personas upfront with Voice design: Replace long legacy Audio Profile / Director's Notes blocks with a custom voice created in Voice design, then carry that voice_... ID through your TTS requests with minimal or empty style strings.
  5. Account for default WAV (audio/wav) output on unary requests: Unlike gemini-3.1-flash-tts-preview and earlier TTS models (which returned headerless raw PCM audio/l16 by default), Gemini 3.8 TTS returns WAV audio (audio/wav / AUDIO_WAV) with a standard RIFF header by default for unary requests.
    • If your code previously wrapped raw PCM bytes in a WAV header (for example, using Python's wave module or ffmpeg), remove the manual header wrapper and write the returned bytes directly to a .wav file.
    • If your pipeline requires headerless raw PCM, mu-law, or A-law audio, explicitly set response_format to "audio/l16" ("AUDIO_L16"), "audio/mulaw" ("AUDIO_MULAW"), or "audio/alaw" ("AUDIO_ALAW"). See Audio output formats.

gemini-3.8-flash-lite-tts

Property Description
Model code gemini-3.8-flash-lite-tts
Supported data types

Inputs

Text

Output

Audio

Token limits[*]

Input token limit

8,192

Output token limit

16,384

Capabilities

Audio generation

Supported

Caching

Supported

Code execution

Not supported

File search

Not supported

Function calling

Not supported

Grounding with Google Maps

Not supported

Image generation

Not supported

Live API

Not supported

Search grounding

Not supported

Structured outputs

Not supported

Thinking

Not supported

URL context

Not supported

Consumption options

Batch API

Supported

Flex inference

Supported

Priority inference

Supported

Versions
Read the model version patterns for more details.
  • gemini-3.8-flash-lite-tts
Latest update July 2026

Supported languages

gemini-3.8-flash-lite-tts detects the input language automatically and supports 101 languages:

Language Language Language
Acehnese (Arab script) Georgian Maltese
Afrikaans German Manipuri
Akan Greek Marathi
Amharic Gujarati Minangkabau (Arab script)
Armenian Haitian Creole Mizo
Assamese Halh Mongolian Nepali (individual language)
Awadhi Hausa Nigerian Fulfulde
Balinese Hebrew North Azerbaijani
Bangla Hindi Northern Sotho
Banjar (Latn script) Hungarian Northern Uzbek
Basque Icelandic Norwegian Bokmål
Belarusian Iloko Norwegian Nynorsk
Bhojpuri Indonesian Nyanja
Bosnian Iranian Persian Odia (individual language)
Buginese Italian Persian (Afghanistan)
Bulgarian Japanese Polish
Cantonese Javanese Portuguese
Catalan Kamba Punjabi
Cebuano Kannada Romanian
Central Kurdish Kashmiri (Arab script) Russian
Chhattisgarhi Kashmiri (Deva script) Santali
Chinese (Hans script) Kazakh Serbian
Chinese (Hant script) Khmer Sinhala
Croatian Kikuyu Slovak
Czech Kinyarwanda South Azerbaijani
Danish Kongo Southern Pashto
Dutch Korean Spanish
Egyptian Arabic Kyrgyz Standard Arabic (Arab script)
English Lao Standard Arabic (Latn script)
Estonian Lingala Standard Latvian
Filipino Macedonian Standard Malay
French Magahi Tamil
Galician Maithili Telugu
Ganda Malayalam