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.

Multi-Speaker Dialogue: Native, Not Hacked Together

One persistent limitation of TTS APIs has been multi-speaker content. To generate a conversation between two distinct voices, you typically split the dialogue, make separate API calls for each speaker, stitch the audio files together, and hope the timing sounds natural. The seams are usually audible, and the workflow adds significant engineering overhead.

Gemini 3.1 Flash TTS handles multi-speaker dialogue natively. You define speaker roles in the prompt, and the model generates a unified audio stream with distinct, consistent voices for each speaker — including natural overlap timing, appropriate pauses, and conversational rhythm.

The format for multi-speaker prompts uses speaker labels directly in the text:

Speaker 1 (Alex): [excited] We just hit a million users.
Speaker 2 (Jordan): [skeptical] Is that monthly actives or just signups?
Speaker 1 (Alex): [pauses] ...signups.
Speaker 2 (Jordan): [sigh] Right. Same as last quarter.

The model maintains voice consistency for each speaker label across the full audio output. Voices are assigned automatically by default, or you can specify named voices from Google’s voice library per speaker role. This makes Gemini 3.1 Flash TTS the most practical option available today for generating podcast content, interactive story experiences, customer service dialogue simulations, or language learning conversations — without additional audio engineering work on your end.

Language Support: 70+ Languages and Regional Accents

Gemini 3.1 Flash TTS supports over 70 languages at launch, with regional accent variants that go beyond what most competitors offer. For English specifically, supported accents include American (multiple regional styles), British RP, Brixton, and additional UK variants. Spanish, French, Portuguese, German, Hindi, Japanese, Korean, Mandarin, and Arabic are among the best-supported non-English languages.

The multilingual capability matters for two different use cases. The obvious one is building globally-deployed products where the TTS voice needs to match the user’s language and locale. The less obvious one is code-switching — content that intentionally mixes languages, which is common in certain markets (Indian English with Hindi code-switching, or Spanglish in Latin American markets). Gemini 3.1 Flash TTS handles code-switching more naturally than single-language-optimized models because of its underlying multilingual architecture.

Google has not published a full per-language quality matrix, and performance will vary. For non-English production deployments, running your own quality evaluation on representative samples before committing to a production integration is the right approach. English, Spanish, French, and Hindi are the most battle-tested at launch.

Pricing: Free Tier and Competitive Paid Rates

Gemini 3.1 Flash TTS pricing follows the Gemini API standard structure:

• Free tier: Available through Google AI Studio with standard API rate limits for prototyping
• Paid text input: $1.00 per million tokens
• Paid audio output: $20.00 per million audio tokens

To put the audio output pricing in practical terms: a typical 10-minute audio segment at standard speech rate (~130 words per minute) contains roughly 1,300 words, or approximately 1,700 text tokens. Depending on the voice and output format, a 10-minute audio clip at these rates costs approximately $0.02–0.05 — meaningfully less than ElevenLabs for comparable output quality.

For comparison: ElevenLabs’ Creator plan at $22/month provides roughly 100,000 characters per month, and OpenAI’s TTS pricing runs $15.00 per million characters for tts-1-hd. For high-volume applications — podcasts, audiobooks, large-scale IVR systems, or language learning platforms generating thousands of audio clips per day — Gemini 3.1 Flash TTS offers a meaningfully more cost-effective path, particularly with the native multi-speaker capability that eliminates the need for multiple API calls per conversation.

Where to Access It

Google AI Studio is the fastest starting point for individual developers. The TTS playground in AI Studio lets you test audio tags, switch between speaker configurations, and export audio without writing any code. The free tier applies here, making it accessible for prototyping and evaluation at zero cost.

Vertex AI is the enterprise path. Gemini 3.1 Flash TTS is available in preview on Vertex AI for organizations that need SLA guarantees, VPC service controls, data residency, and the governance infrastructure that enterprise deployments require. If you are building a production voice product for an enterprise customer, Vertex AI is the right integration target even during preview, since the API contract there is more stable than the general-access preview endpoint.

Google Vids ships Gemini 3.1 Flash TTS as a built-in narration option for Workspace users. This is the non-developer path — if your organization uses Google Workspace and wants to add AI voiceover to presentations and video content, the capability is already in your workflow without any API integration.

A Quick Python API Walkthrough

Getting started requires the Google Generative AI Python client. Here is a minimal working example for single-voice generation with audio tags:

import google.generativeai as genai
import base64

genai.configure(api_key="YOUR_API_KEY")

model = genai.GenerativeModel("gemini-3.1-flash-tts-preview")

response = model.generate_content(
    "[enthusiasm] Welcome to the future of AI voice generation. "
    "[calm] Here is what you need to know. "
    "[determination] Let's build something remarkable.",
    generation_config=genai.GenerationConfig(
        response_mime_type="audio/mp3",
        speech_config=genai.SpeechConfig(
            voice_config=genai.VoiceConfig(
                prebuilt_voice_config=genai.PrebuiltVoiceConfig(
                    voice_name="Puck"
                )
            )
        )
    )
)

audio_data = response.candidates[0].content.parts[0].inline_data.data
with open("output.mp3", "wb") as f:
    f.write(base64.b64decode(audio_data))

The voice_name parameter selects from Google’s library of named voices. Voice names include options like Puck, Charon, Kore, and Fenrir, each with distinct tonal characteristics. The audio tags modify whichever voice you select rather than replacing it — so you can maintain voice consistency while varying emotional delivery throughout a long piece of content.

For multi-speaker output, pass your speaker-labeled dialogue as the prompt text and omit the voice_config block; the model will auto-assign distinct voices per speaker label. You can override this by specifying a voice_name per speaker role in an extended config block available in the official documentation.

SynthID Watermarking: Built-In Authenticity

Every audio file generated by Gemini 3.1 Flash TTS is automatically watermarked with SynthID — Google’s imperceptible audio watermark technology. SynthID embeds an inaudible signal directly into the audio waveform, invisible to listeners but detectable by Google’s verification tools.

This matters for two distinct audiences. For enterprises using the model to generate branded voice content, SynthID provides a chain of authenticity — generated content can be verified as AI-produced rather than a recording of a real person, which is increasingly important for legal and compliance purposes. For the broader information ecosystem, SynthID provides a technical mechanism to flag AI-generated audio in contexts where provenance matters: political content, news narration, or any setting where synthetic speech might be mistaken for an authentic recording.

Developers cannot disable SynthID — it applies to all output as a deliberate policy decision from Google. For most production use cases, watermarking is a feature rather than a constraint: it protects both the developer and the end user, and it positions Gemini TTS output well relative to emerging AI content disclosure requirements in the EU AI Act and proposed US legislation.

How It Compares to ElevenLabs, OpenAI TTS, and Mistral Voxtral

The TTS market in April 2026 has three meaningful tiers, and Gemini 3.1 Flash TTS fits distinctly into each competitive comparison:

vs. ElevenLabs: ElevenLabs remains the quality benchmark for voice cloning and extreme naturalness on English content. For products where you need a cloned voice — a specific public figure, a brand voice modeled on a real person — ElevenLabs is still the right call. Where Gemini 3.1 Flash TTS wins decisively is controllability (200+ audio tags vs. ElevenLabs’ more limited emotion presets), multilingual breadth, and pricing at high volume.

vs. OpenAI TTS (tts-1 and tts-1-hd): OpenAI’s TTS is competent but limited in expressiveness. Six available voices and basic speed controls are sufficient for many applications, but there are no emotion tags, no multi-speaker native support, and no regional accent variants. At $15/million characters for tts-1-hd, it is also more expensive than Gemini 3.1 Flash TTS for equivalent audio output.

vs. Mistral Voxtral: Voxtral is the open-source option in this tier — if you need to self-host your TTS infrastructure or want full model control, Voxtral is the strongest available model. For cloud-API use cases where you are paying per token anyway, Gemini 3.1 Flash TTS has a clearer quality and expressiveness advantage, particularly for non-English languages where open models tend to lag behind proprietary ones.

The practical recommendation: If you are building a new voice product today and do not have a specific voice cloning requirement, Gemini 3.1 Flash TTS is the strongest starting point. The audio tag system, multi-speaker capability, and multilingual support together eliminate three categories of engineering workarounds that currently add significant complexity to voice pipelines.

What to Build With Gemini 3.1 Flash TTS

The use cases that benefit most from its specific capabilities:

• AI podcast generation — Native multi-speaker support plus audio tags mean you can generate natural-sounding two- or three-person conversations from a transcript without audio splicing or manual timing adjustment.
• E-learning narration — Audio tags let you encode appropriate emotional delivery per section: [enthusiastic] for introductions, [calm] for technical explanations, [encouraging] for practice prompts.
• Interactive voice response (IVR) — Replace robotic IVR prompts with contextually appropriate voice delivery without the per-voice cost structure of premium TTS vendors.
• Multilingual content scaling — Generate narration in 70+ languages from the same pipeline without switching TTS providers or managing multiple vendor API keys.
• Accessibility tools — Screen readers and read-aloud features that adapt emotional delivery to content type (news vs. fiction vs. technical documentation) rather than reading everything in the same flat voice.
• AI companion apps — Voice agents that respond with contextually appropriate emotion rather than robotic monotone, dramatically improving perceived intelligence and user retention.

What to Watch During Preview

A few things to track before committing to a full production architecture on Gemini 3.1 Flash TTS:

Pricing stability: Preview pricing may change at GA. The current $1/$20 per million token structure is strong, but enterprise contracts negotiated during preview may differ from public pricing at GA. Build your cost models with a 20–30% pricing buffer for the GA transition.

API surface changes: The gemini-3.1-flash-tts-preview model ID will change at GA. Build your integration with the model ID as a configuration variable rather than a hardcoded string to handle the transition without an emergency refactor.

Voice library expansion: The current named voice library is limited at launch. Google has indicated it will expand during preview. If the specific voice you need is not available today, check back monthly before ruling out the model for your use case.

EU and UK availability: Gemini 3.1 Flash TTS is not available in the European Economic Area, UK, or Switzerland during preview, pending regulatory review. If your user base is primarily in these regions, plan your timeline accordingly — Google has indicated EU and UK availability is planned but has not given a specific date.

The Bottom Line

Gemini 3.1 Flash TTS is the most expressive and controllable TTS API available via public cloud as of April 2026. The 200+ audio tag system is genuinely differentiated — there is no equivalent in ElevenLabs, OpenAI, or any other tier-one provider at the API level. The native multi-speaker capability is meaningfully more convenient than splicing-based alternatives. And the pricing, with a free tier for development and competitive paid rates for production, removes a meaningful barrier for builders who have been priced out of premium TTS services.

For developers building voice-enabled products — podcasts, e-learning, IVR, accessibility tools, or AI companions — Gemini 3.1 Flash TTS is worth integrating into your next sprint. The free tier in Google AI Studio means your first experiment costs nothing. Start there, test the audio tags that matter for your specific use case, and evaluate whether the quality and control justify moving toward a production integration on Vertex AI.

Voice is becoming a first-class output modality for AI applications. Products that generate audio content with flat, emotionless delivery will feel dated compared to those that use tools like Gemini 3.1 Flash TTS to deliver contextually appropriate, expressive narration. That gap will only widen as the audio tag system matures and the voice library expands. The time to build familiarity with this API is now.