Gemini 3.1 Flash TTS: The Most Controllable Text-to-Speech Model Yet

What Makes Gemini 3.1 Flash TTS Different

Most text-to-speech models give you one dial: pick a voice, generate audio, done. The output sounds fine until you need something specific — a pause after a punchline, a warmer tone for customer service, a tense delivery for a thriller audiobook. Then you’re stuck.

Gemini 3.1 Flash TTS is built around the idea that speech style should be as controllable as the text itself. Instead of fiddling with audio post-processing or re-recording takes, you describe what you want — in plain language or inline tags — and the model handles the rest.

It’s part of Google’s broader Gemini 3.1 Flash model family, which has been expanding steadily into audio territory. Where Gemini 3.1 Flash Live handles real-time conversational voice, the TTS variant is purpose-built for pre-generated, production-quality speech output.

Here’s what the model can do, how the control system works, and whether it’s worth switching to.

What Gemini 3.1 Flash TTS Actually Is

Gemini 3.1 Flash TTS is a text-to-speech model available through the Gemini API and directly in Google AI Studio. It generates speech from text input, but with an unusually deep set of controls for how that speech sounds.

The model is built on the same underlying architecture as the Gemini 3.1 Flash family, which means it benefits from the same instruction-following capabilities that make the text models useful. Instead of requiring a separate audio editing layer, you tell the model what you want as part of the prompt itself.

That has real consequences for what you can do with it:

Emotion and tone: Specify whether a passage should sound warm, urgent, playful, matter-of-fact, or somber.
Pacing and rhythm: Request slower delivery for instructional content, faster for excitement, deliberate pauses for emphasis.
Pitch and energy: Ask for a lower, more authoritative register or a brighter, higher-energy delivery.
Multi-speaker dialogue: Generate back-and-forth conversation with distinct voices for each speaker, labeled inline.

The model is also genuinely multilingual. It supports dozens of languages and can handle accent variations within a language — useful if you’re building for a global audience or need AI-powered multilingual support in your product.

How the Control System Works

This is the part that separates Gemini 3.1 Flash TTS from most competitors. There are two main ways to control the output: natural language style prompts and inline markup tags.

Natural Language Style Prompts

The simplest approach is to prepend or append a style instruction to your text. The model treats this as a direction for how the entire passage should be delivered.

Examples:

"Read this in a calm, reassuring tone, as if explaining to a worried patient."
"Deliver this with enthusiasm and a sense of urgency, like a sports commentator."
"Use a dry, deadpan style throughout. No dramatic inflection."

This works well for uniform passages — a single explainer video segment, a podcast intro, a notification message. The model stays consistent with the instruction across the full output.

Inline Markup Tags

For more granular control within a single block of text, the model supports inline tags. These wrap specific words or phrases and tell the model how to treat that span of speech.

Common tag types include:

<emotion>: Specify the emotional quality for a word or phrase. <emotion type="surprised">What?</emotion>
<pace>: Control reading speed for a section. <pace speed="slow">Take your time with this part.</pace>
<emphasis>: Stress a specific word. <emphasis>This</emphasis> is the critical point.
<pause>: Insert a deliberate pause. Thank you for listening.<pause duration="1s"/> Now let's get started.
<pitch>: Adjust the pitch for a word or phrase relative to baseline.

The tag system is readable and editable by humans, which matters if you’re building a workflow where non-technical team members write scripts and engineers wire up the API calls.

Multi-Speaker Dialogue

For content with multiple characters or speakers, you label each line with a speaker identifier and optionally assign a voice and style to each:

[SPEAKER: Alex, voice="Aoede", style="confident, friendly"]
Welcome back to the show.

[SPEAKER: Jamie, voice="Fenrir", style="skeptical, dry"]
I'll believe it when I see it.

The model keeps the voices distinct across the full dialogue without you needing to chain separate API calls and stitch audio together manually. This is particularly useful for audiobook production, e-learning scenarios, and chatbot dialogue previewing.

Available Voices and Languages

Gemini 3.1 Flash TTS ships with a library of named voices, each with distinct character. Voices have names (Google uses mythological and nature-inspired names like Aoede, Charon, Fenrir, Kore, and Puck) and are described in the documentation with their baseline characteristics — warm, neutral, expressive, deep, bright, and so on.

You pick a starting voice, then layer your style instructions on top. The model treats the named voice as a baseline and adjusts within that character. A voice labeled “warm and conversational” can still deliver urgency; it just does so within that warmth rather than flattening into a neutral broadcast register.

Language support covers the major global languages as you’d expect — English, Spanish, French, German, Portuguese, Japanese, Korean, Arabic, Hindi, Mandarin — plus a substantial set of regional languages and dialect variants. The model handles code-switching reasonably well, which is useful for content that mixes languages in a single passage.

One area where this model shows meaningful progress over earlier TTS systems is handling proper nouns, technical terminology, and abbreviations. You can include pronunciation guides inline using phonetic notation, and the model applies them correctly without garbling surrounding text.

Real-World Use Cases

Audiobook and Long-Form Narration

Gemini 3.1 Flash TTS can sustain consistent voice character across long inputs. For audiobook producers working with AI narration, this matters more than almost anything else — a voice that drifts in register or loses its character between chapters is unusable.

The multi-speaker capability means dialogue scenes can have genuine differentiation between characters without the flat, same-voice delivery that makes AI audiobooks feel artificial.

Voice Agents and Customer Service

For customer-facing voice applications, the emotion and pacing controls let you build a voice that feels appropriate for the brand and context. A healthcare bot should sound different from a retail assistant; a crisis line response should sound different from an account status update.

If you’re already exploring what Gemini 3.1 Flash Live can do for real-time voice agents, the TTS variant complements it for the pre-generated portion of voice workflows — hold messages, IVR prompts, outbound notification calls.

E-Learning and Educational Content

Instructional audio benefits enormously from pacing control. A fast-talking narrator loses learners; a flat, monotonous one loses them differently. Being able to specify slower delivery for complex concepts, emphatic delivery for key terms, and natural pausing between sections gives course producers real control over comprehension outcomes.

Accessibility and Screen Readers

For applications that need to generate speech for visually impaired users, the model’s ability to handle technical text, code snippets, and mixed-content passages correctly makes it more reliable than earlier TTS systems that would mispronounce variable names or stumble on markdown formatting.

Content Localization

For teams running AI-powered multilingual support workflows, having a single TTS model that handles dozens of languages at comparable quality levels is operationally much simpler than maintaining separate vendor relationships per region.

How It Compares to Other TTS Options

The TTS market has gotten crowded. Here’s how Gemini 3.1 Flash TTS sits relative to the main alternatives.

vs. ElevenLabs

ElevenLabs has strong voice cloning and a large voice marketplace. Its output quality on neutral speech is excellent. But its control model is largely limited to selecting voices and adjusting stability/similarity sliders — there’s no inline tag system, and style direction through natural language is less reliable.

For teams that need voice cloning specifically, ElevenLabs still has an edge. For teams that need programmatic, instruction-driven style control, Gemini 3.1 Flash TTS is more capable. See the Gemini 3.1 Flash Live vs ElevenLabs comparison for a broader look at how these ecosystems compare for voice agent deployment.

vs. OpenAI TTS

OpenAI’s TTS models offer good quality and a simple API, but limited style control. You pick a voice, optionally pass a system-level instruction, and get output. There’s no inline tagging and no multi-speaker dialogue in a single call.

vs. Mistral’s Open-Weight TTS

Mistral’s open-weight TTS model is interesting if you need local deployment or voice cloning with data sovereignty constraints. It doesn’t match Gemini 3.1 Flash TTS on style control granularity, but if running inference on your own hardware is a requirement, it’s worth knowing about.

vs. Smallest.ai Lightning V3.1

Smallest.ai Lightning V3.1 is optimized for low-latency conversational use — it’s fast to the first byte, which matters for real-time voice agents. Gemini 3.1 Flash TTS prioritizes quality and controllability over latency, so the right choice depends on whether you’re generating on-demand audio or running a live dialogue loop.

Summary Table

Model	Quality	Style Control	Multi-Speaker	Local Deployment	Free Tier
Gemini 3.1 Flash TTS	High	Excellent	Yes	No	Yes (AI Studio)
ElevenLabs	Very High	Moderate	Limited	No	Limited
OpenAI TTS	High	Basic	No	No	No
Mistral TTS	Good	Basic	No	Yes	Yes
Smallest.ai Lightning	Good	Moderate	No	No	Limited

How to Try It in Google AI Studio

Google AI Studio is the fastest way to test Gemini 3.1 Flash TTS without writing any code. Access is free with a Google account, and you don’t need API credits for basic experimentation.

Step 1: Go to AI Studio Navigate to Google AI Studio and select the audio/speech generation capability from the model menu. Choose Gemini 3.1 Flash TTS as your model.

Step 2: Write your text Paste in the text you want converted to speech. You can start plain — just text, no markup — to hear the baseline.

Step 3: Add a style prompt In the system instruction or prompt prefix area, add a style direction. Try something like: “Read this in a warm, unhurried tone, as if explaining to a friend.”

Step 4: Use inline tags Edit your text to add markup where you want specific control. Wrap a word in <emphasis> tags, insert a <pause> between paragraphs, or mark a phrase with <emotion type="excited">.

Step 5: Select a voice From the voice selector, pick one of the named voices. Try a few — they respond differently to the same style instructions based on their baseline character.

Step 6: Generate and compare Play the output, then adjust. This iteration loop is fast enough that you can meaningfully compare five or six style variations in a few minutes.

For API access, Google’s documentation covers the endpoint structure, authentication, and supported parameters. If you’re building on top of AI Studio’s broader infrastructure, the Firebase integration guide is worth reading for how to wire audio generation into a full-stack app.

Pricing

Gemini 3.1 Flash TTS pricing follows the same input/output token structure as other Gemini API calls, with audio output billed per character generated or per audio second — Google has published specific rates in their API documentation.

For high-volume production workloads, the pricing is competitive with ElevenLabs and OpenAI TTS at similar quality tiers. For development and moderate usage, the free tier through AI Studio covers a substantial amount of experimentation.

One practical note: multi-speaker dialogue generation counts each character spoken, not the total characters in the markup. The tag overhead doesn’t drive up your bill.

Building Voice-Enabled Apps with Remy

If you’re building an application that uses text-to-speech — a voice agent, an audiobook platform, an e-learning tool, an accessibility layer — you need TTS wired into a backend that can handle text input, make API calls, store audio output, and serve it to users. That’s a non-trivial amount of plumbing.

Remy handles this at the spec level. You describe what your application does — “receives text input, calls Gemini TTS with a configured voice and style, stores the audio output in a user’s library, and serves it back on demand” — and the compiled app includes the backend methods, database schema, and frontend to match. You’re not hand-coding API wrappers or managing audio file storage yourself.

The best AI models for agentic workflows in 2026 increasingly include audio components, and building those components as standalone integrations in traditional code stacks gets repetitive fast. Remy’s spec-driven approach means you describe the audio workflow once, the code is derived from that, and as Gemini TTS capabilities evolve, you update the spec rather than hunting through service files.

You can try Remy at mindstudio.ai/remy.

Frequently Asked Questions

What is Gemini 3.1 Flash TTS?

Gemini 3.1 Flash TTS is Google’s text-to-speech model in the Gemini 3.1 Flash family. It converts text to spoken audio with fine-grained control over voice style, emotion, pacing, and tone — either through natural language style prompts or inline markup tags.

How does Gemini 3.1 Flash TTS control tone and emotion?

You can specify tone and emotion in two ways: a natural language instruction applied to the full passage (e.g., “deliver this warmly and slowly”) or inline tags that wrap specific words or phrases. The <emotion>, <pace>, <emphasis>, and <pause> tags give you control at the word and phrase level within a single generation call.

Is Gemini 3.1 Flash TTS free to use?

Yes, you can use it free in Google AI Studio for experimentation and prototyping. API access for production use is billed per character or audio second, with rates published in Google’s API documentation. The free tier covers substantial development work.

How does Gemini 3.1 Flash TTS compare to ElevenLabs?

ElevenLabs has stronger voice cloning capabilities and a large marketplace of voices. Gemini 3.1 Flash TTS has more sophisticated inline style control and built-in multi-speaker dialogue support. For custom cloned voices, ElevenLabs leads. For programmatic control over speech style in generated audio, Gemini 3.1 Flash TTS is more capable.

What languages does Gemini 3.1 Flash TTS support?

The model supports dozens of languages including English, Spanish, French, German, Portuguese, Japanese, Korean, Arabic, Hindi, and Mandarin, plus many regional languages and dialect variants. It handles code-switching within a passage and supports phonetic pronunciation guides for technical terms.

Can Gemini 3.1 Flash TTS generate multi-speaker dialogue?

Yes. You label each line with a speaker identifier, optionally assign a named voice and style to each speaker, and the model generates a single audio output with distinct, consistent voices for each character. This avoids the need to chain separate API calls and manually stitch audio together.

Key Takeaways

Gemini 3.1 Flash TTS is differentiated primarily by its control model — inline tags and natural language style prompts let you direct tone, emotion, pacing, and emphasis at a granular level.
Multi-speaker dialogue generation in a single API call is a practical time-saver for audiobook, e-learning, and voice agent use cases.
The model is available free in Google AI Studio, making it accessible for experimentation without any setup overhead.
Compared to ElevenLabs, it leads on style control and loses on voice cloning; compared to OpenAI TTS, it leads on both.
For teams building voice agents or audio-enabled applications, Gemini 3.1 Flash TTS fits well alongside the broader Gemini ecosystem, especially if you’re already using other Gemini 3.1 Flash capabilities.

If you want to build a voice-enabled application without spending days wiring up TTS APIs, audio storage, and playback logic, try Remy — describe what the app does in a spec, and the full-stack implementation follows.