Google DeepMind releases Gemini 3.1 Flash TTS with audio tags for precise speech control across 70+ languages

Google DeepMind launched Gemini 3.1 Flash TTS, a text-to-speech model that achieved an Elo score of 1,211 on the Artificial Analysis TTS leaderboard based on thousands of blind human preferences. The model is now available in preview via the Gemini API, Google AI Studio, Vertex AI for enterprises, and Google Vids for Workspace users.

Audio tags for granular control

The defining feature of 3.1 Flash TTS is audio tags — natural language commands embedded directly into text input that control vocal style, pace, and delivery. According to Google, these tags provide "improved levels of granularity" for steering AI speech output.

Google AI Studio offers three levels of control:

• Scene direction: Developers can define the environment and provide dialogue instructions to help characters remain "in-character" across multiple turns
• Speaker-level specificity: Unique Audio Profiles can be assigned to characters, with Director's Notes to adjust pace, tone, and accent
• Inline tags: Speakers can change expression mid-sentence, pivoting from high-level settings

Once configured, these parameters can be exported as Gemini API code for consistent voice reproduction across projects.

Performance and availability

Artificial Analysis positioned Gemini 3.1 Flash TTS in its "most attractive quadrant" for combining high-quality speech generation with low cost, though specific pricing was not disclosed. The model supports 70+ languages with what Google describes as "high-fidelity speech" and native multi-speaker dialogue.

All audio generated by the model includes SynthID watermarking — an imperceptible watermark embedded in the audio output designed to enable detection of AI-generated content.

What this means

The introduction of audio tags represents a shift toward programmatic control of AI speech synthesis through natural language rather than complex parameter tuning. The 1,211 Elo score suggests competitive performance against existing TTS models, though direct comparisons to specific competitors weren't provided. The emphasis on multi-speaker dialogue and scene-level control indicates Google is targeting use cases beyond simple text reading — particularly interactive applications, content creation, and localized media production. The mandatory SynthID watermarking addresses growing concerns about audio deepfakes, though the effectiveness of such watermarks against determined adversaries remains an open question in the field.