Gemini 3.1 Flash TTS: Developer Guide to Google's Most Controllable AI Voice Model (2026)

On April 15, 2026, Google launched Gemini 3.1 Flash TTS — and it quietly redraws the map for AI voice generation. Most text-to-speech APIs give you a voice, a speed slider, and maybe a handful of presets. Gemini 3.1 Flash TTS gives you 200+ audio tags that let you direct a voice like a film director on set: [whispers] before a reveal, [determination] before a call to action, [nervous laughter] before an awkward admission. Combined with native multi-speaker dialogue, support for 70+ languages with regional accent variants, and pricing that undercuts established competitors at volume, this is a model worth understanding in depth if you build products that speak.

What Is Gemini 3.1 Flash TTS?

Gemini 3.1 Flash TTS Preview is Google’s newest text-to-speech model, available through the Gemini API (Google AI Studio), Vertex AI, and Google Vids. It is built on the same Gemini 3.1 Flash architecture used for text and multimodal generation, which means it understands context, nuance, and intent — not just phonemes and pronunciation rules. The model ID for API calls is gemini-3.1-flash-tts-preview.

On the Artificial Analysis TTS leaderboard, Gemini 3.1 Flash TTS achieved an Elo score of 1,211 at launch — placing it among the highest-quality open-access TTS systems available today. For context, ElevenLabs’ top models typically score in the 1,180–1,200 range. On naturalness and expressiveness metrics, Gemini 3.1 Flash TTS opens ahead of the established pack.

The “Preview” label means the API surface may evolve before GA, but the core features — audio tags, multi-speaker, multilingual — are stable enough for production pilots today. Google’s track record with Gemini preview releases suggests a GA window within the next two quarters.

The Audio Tags System: 200+ Expressive Controls

The single most differentiated capability in Gemini 3.1 Flash TTS is its audio tag system. Standard TTS models let you control speed, pitch, and volume. Audio tags let you control performance — the emotional and physical delivery of the voice, not just its mechanics.

Tags are simple square-bracket annotations embedded directly in your input text. The model reads the tag and adjusts its vocal delivery accordingly for the text that follows. A few examples of how this works in practice:

[determination] We will ship this by end of quarter.
[whispers] Nobody else in the room knows what we know.
[enthusiasm] This is the fastest model we have ever built.
[awe] I have never seen benchmark numbers like these before.
[nervous laughter] So... that deploy went a little differently than planned.

The full tag library covers emotional states (adoration, interest, disappointment, pride, awe), physical delivery styles (breathes deeply, pauses, clears throat), pacing markers (speeds up, slow deliberate), and tonal modes (sarcastic, sincere, matter-of-fact). According to Google’s documentation, there are over 200 supported tags, and they can be stacked within a single passage for complex vocal performances.

The practical implication is significant for content-heavy applications. Podcast generation, audiobook production, interactive voice response (IVR) systems, e-learning narration, and AI-driven customer support calls all have very different vocal requirements within a single conversation. Audio tags let you encode those requirements directly into the text you pass to the API — no need to split content into separate API calls with different voice settings, no need to manually edit audio timing in post-production.

This is a genuinely new capability class for a public TTS API. None of the major competitors — OpenAI TTS, ElevenLabs, or Mistral Voxtral — offer an emotion tag system with this granularity or breadth at the API level.