Gemini 3.1 Flash TTS: The Most Controllable AI Text-to-Speech Model Yet
Google's Gemini 3.1 Flash TTS supports emotion tags, two-speaker mode, and dramatic pauses. Here's what it can do and how to try it free.
What Makes Gemini 3.1 Flash TTS Different From Other AI Voice Models
Most AI text-to-speech tools give you a voice and a slider. You pick a preset, adjust the speed, and hope the output doesn’t sound robotic. That’s been the ceiling for years.
Gemini 3.1 Flash TTS pushes past that ceiling. It’s Google’s most expressive and controllable TTS model yet, built on the same Gemini 3.1 Flash architecture that’s made waves across multimodal tasks. The difference here is what you can do with it: inject emotion mid-sentence, synthesize two distinct speakers in a single pass, and drop dramatic pauses with the kind of precision that used to require a professional audio editor.
This article covers what Gemini 3.1 Flash TTS actually does, what separates it from the competition, and how to try it free today through Google AI Studio.
What Is Gemini 3.1 Flash TTS?
Gemini 3.1 Flash TTS is Google’s dedicated text-to-speech model within the Gemini 3.1 Flash family. It’s accessed through the Gemini API and Google AI Studio, and it’s designed for production-grade voice output — not demo quality, not prototype audio.
The model supports over 30 distinct voices across multiple languages and accents. But the voices themselves aren’t the headline feature. What’s notable is how much control you have over how those voices perform.
Unlike older TTS systems that parse plain text and apply static prosody rules, Gemini 3.1 Flash TTS responds to natural language style instructions embedded directly in your prompt. You don’t need to learn a markup language. You describe what you want — “speak with quiet urgency,” “pause here for two seconds,” “shift to a warmer tone” — and the model interprets and executes it.
For context on where this fits in Google’s broader model lineup, Gemini 3.1 Flash Lite is optimized for speed and low cost, while the Flash TTS variant is specifically tuned for high-quality audio generation with expressive control.
The Three Features That Set It Apart
Emotion and Style Tags
This is what most people lead with when they talk about Gemini 3.1 Flash TTS, and for good reason.
You can annotate your input text with natural language emotion cues — either as inline instructions or as part of a system prompt that governs the whole output. The model understands and applies context like:
[excited]before a line to raise energy and pace[somber]to pull the tone down[whispering]for close, intimate delivery[authoritative]for a firm, grounded read
These aren’t rigid tags from a fixed library. The model understands intent, so variations like “speak with nervous energy” or “deliver this like you’re confiding in a friend” also work. You’re essentially directing a voice performance in plain English.
This is a real practical advantage for content creators. If you’re generating podcast intros, ad reads, or explainer narration, you can now get a production-ready take without manual audio editing to fix flat delivery.
Two-Speaker Mode
Gemini 3.1 Flash TTS supports native multi-speaker synthesis — what Google calls its multi-speaker audio generation capability. In a single API call, you can define two distinct speakers and have the model generate a back-and-forth dialogue with separate voice characteristics for each.
Each speaker can have:
- A different voice selection from the available voice library
- Independent style and emotion instructions
- Different pacing and energy levels
The output is a single audio file with the dialogue naturally woven together, timed correctly, and with appropriate speaker transitions. You don’t have to stitch together two separate TTS calls in post-production.
This is particularly valuable for podcast production, dialogue-driven content, and interactive voice applications. Compare that to something like Smallest.ai Lightning V3.1, which is built specifically for low-latency single-speaker conversation in voice agents. Gemini 3.1 Flash TTS takes a different approach — deeper expressivity over raw latency.
Dramatic Pauses and Pacing Control
Pacing is one of the hardest things to control in TTS. Models tend to either rush through content or insert awkward mechanical gaps. Gemini 3.1 Flash TTS handles this more naturally.
You can instruct the model to insert deliberate pauses — “pause for two beats before continuing,” “let that sentence breathe,” or by using explicit timing cues. The model treats pauses as part of the performance, not just silence filler.
This makes a real difference in narration. In audiobook production, documentary voiceover, or meditation guidance audio, the pause is as meaningful as the words. Having explicit control without manually splicing audio files is a significant workflow improvement.
Voice Selection and Customization Options
Gemini 3.1 Flash TTS ships with a substantial voice library. Available voices span multiple English accents (US, UK, Australian), and the model supports generation in over 24 languages, including Spanish, French, German, Japanese, Hindi, and Arabic.
Each voice has a name (Google uses a standardized naming convention in the API), and you can preview voices through AI Studio before committing them to a production workflow.
Beyond voice selection, you can apply style modifiers that affect:
- Pitch — relative adjustments, not raw Hz values
- Speed — described in natural terms (“slightly faster,” “slow and deliberate”)
- Energy level — calm through high intensity
- Tone — warm, cold, professional, conversational
These aren’t mutually exclusive. You can layer multiple descriptors to define a consistent narrator voice that persists across a long-form piece.
For teams building voice agent infrastructure, this pairs naturally with Gemini 3.1 Flash Live, which handles real-time multimodal voice conversations. The TTS model is more appropriate for asynchronous audio generation — scripted content, audiobooks, ads, and training materials — while Flash Live handles the live interaction layer.
How to Try Gemini 3.1 Flash TTS for Free
Google AI Studio is the fastest way in. It’s free to use within quota limits, and you don’t need to configure an API key to start experimenting.
Using Google AI Studio
- Go to Google AI Studio and sign in with a Google account.
- Select the Gemini 3.1 Flash model from the model picker.
- Navigate to the Speech or TTS playground (or use the API directly).
- Enter your text, add any style or emotion annotations, and generate.
The UI lets you listen to output immediately and tweak your prompt before committing to an API integration.
Using the Gemini API
For production use, you’ll call the API directly. The basic structure looks like this:
from google import genai
from google.genai import types
client = genai.Client(api_key="YOUR_API_KEY")
response = client.models.generate_content(
model="gemini-3.1-flash-tts",
contents="[calm, warm] Welcome back. Today we're going to cover something important. [pause] Really important.",
config=types.GenerateContentConfig(
response_modalities=["AUDIO"],
speech_config=types.SpeechConfig(
voice_config=types.VoiceConfig(
prebuilt_voice_config=types.PrebuiltVoiceConfig(voice_name="Kore")
)
),
),
)For multi-speaker output, you define separate speaker configs and the model handles voice switching within a single response.
Pricing
As of April 2026, Gemini 3.1 Flash TTS is priced at $0.50 per 1 million characters of input text — significantly cheaper than ElevenLabs’ professional tier and competitive with other cloud TTS providers. Free tier quota is available through AI Studio for experimentation.
Practical Use Cases
Podcast and Audiobook Production
The two-speaker mode and emotion control make Gemini 3.1 Flash TTS genuinely useful for scripted podcast content. You can feed it a transcript with speaker labels and style notes, and get back a listenable dialogue audio file in seconds.
For audiobooks, the ability to define a consistent narrator voice with emotional range across chapters — without re-prompting every time — cuts production time significantly.
If you’re already using AI tools for your content workflow, pairing this with a tool like 10 AI Agents for Content Creators and YouTubers makes the full production pipeline — from script generation to audio output — largely automated.
Ad Copy and Brand Voice Content
Marketing teams can use the emotion and pacing controls to produce ad reads that match specific brand tone guidelines. A luxury brand needs a different delivery than a DTC startup. With Gemini 3.1 Flash TTS, you can spec that out in natural language rather than hiring a new voice actor for every spot.
This also connects to broader AI agents for content marketing workflows — where audio production is just one layer of a multi-channel content engine.
E-Learning and Training Materials
Corporate training narration has historically been one of the worst use cases for TTS. Flat, robotic reads don’t hold attention. With emotion control, you can inject appropriate urgency into safety briefings, warmth into onboarding materials, and confident authority into compliance training — without recording a human narrator.
Voice Agent Development
While Gemini 3.1 Flash TTS isn’t optimized for real-time agent conversations (that’s what Flash Live is for), it’s useful for pre-generating voice responses, IVR menus, and fallback audio for voice agents. If you’re building voice-based products, it’s worth reading the comparison between Gemini 3.1 Flash Live and ElevenLabs to understand where each model fits in your stack.
How It Compares to Other TTS Models
Versus ElevenLabs
ElevenLabs has the strongest voice cloning and the deepest voice marketplace. Its Multilingual v2 model produces excellent natural speech. But ElevenLabs’ emotion control has historically required you to either use a specific voice trained for emotional range or manually splice audio clips.
Gemini 3.1 Flash TTS offers comparable naturalness with more direct control via natural language prompting — and it’s substantially cheaper at scale.
Versus Mistral’s Open-Weight TTS Model
Mistral’s open-weight TTS model takes a completely different approach: it’s designed to run locally, which makes it ideal for privacy-sensitive applications or edge deployments. It supports voice cloning from a short reference clip. Gemini 3.1 Flash TTS doesn’t offer that — it’s a cloud-only model with a fixed voice library.
If local inference or custom voice cloning is a hard requirement, Mistral’s model wins. If you need the best emotion control and two-speaker synthesis in a managed cloud offering, Gemini 3.1 Flash TTS is the stronger option.
Versus OpenAI TTS
OpenAI’s TTS models (available through the Audio API) are solid for general-purpose narration. But they don’t offer the same level of inline style annotation or multi-speaker output in a single call. Google has a clear edge on controllability at this point.
Building Voice Apps with Gemini TTS and Remy
If you want to go beyond generating one-off audio clips and actually build a full application around Gemini 3.1 Flash TTS — say, an automated podcast producer, a custom audiobook generator, or a voice-enabled content tool — that’s where Remy becomes useful.
Remy lets you describe what the app should do in a spec document, and compiles it into a full-stack application: backend, database, auth, deployment. You describe the audio generation pipeline in annotated prose — “the app takes a script input, splits it by speaker, calls Gemini TTS with appropriate voice and style configs for each speaker, and returns a downloadable audio file” — and Remy handles the implementation.
You’re not writing API wrappers by hand or scaffolding a Node backend from scratch. The spec is the source of truth; the code is derived output. As models improve, you recompile without rewriting the app.
For teams already using Gemini for AI agent workflows, this is a natural extension — you get the model capabilities plus a production-ready application layer around them.
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 within the Gemini 3.1 Flash family. It generates high-quality speech audio from text input, with support for emotion tags, natural language style instructions, multi-speaker output, and pacing control. It’s available via the Gemini API and Google AI Studio.
How do emotion tags work in Gemini 3.1 Flash TTS?
You embed natural language style cues either inline in your text or in a system prompt. The model interprets these instructions — things like [excited], [whispering], or “speak with quiet authority” — and adjusts its delivery accordingly. You don’t need to use a specific tag syntax; the model understands intent from natural descriptions.
Can Gemini 3.1 Flash TTS generate dialogue between two speakers?
Yes. The multi-speaker mode lets you define two distinct speakers with separate voice and style configurations in a single API call. The model generates a unified audio output with proper speaker transitions, removing the need to stitch together two separate TTS outputs.
Is Gemini 3.1 Flash TTS free to use?
You can experiment with it for free through Google AI Studio within the available quota limits. For production use via the API, pricing is approximately $0.50 per 1 million input characters as of April 2026. Check Google’s current AI Studio pricing page for the latest rates.
How does Gemini 3.1 Flash TTS compare to ElevenLabs?
ElevenLabs has stronger voice cloning and a larger voice marketplace. Gemini 3.1 Flash TTS has better inline emotion control via natural language prompting and native two-speaker synthesis in a single pass. It’s also significantly cheaper at scale. The right choice depends on whether voice cloning or expressive control matters more for your use case.
What languages does Gemini 3.1 Flash TTS support?
The model supports over 24 languages, including English (multiple accents), Spanish, French, German, Portuguese, Japanese, Korean, Hindi, and Arabic. Language support may vary by voice selection within the library.
Key Takeaways
- Gemini 3.1 Flash TTS is Google’s most expressive text-to-speech model yet, with natural language emotion control, two-speaker synthesis, and deliberate pacing tools.
- Emotion tags work through natural language prompting — no rigid markup required. You describe the performance you want and the model delivers it.
- Two-speaker mode generates full dialogue audio in a single API call, eliminating the need to splice separate voice tracks.
- Pricing is competitive — roughly $0.50 per 1 million input characters, with free experimentation available through Google AI Studio.
- For content creators, the model is most useful for podcast production, ad reads, e-learning narration, and any content requiring expressive, controlled voice output.
- If you want to build a full application around it — not just generate clips — Remy lets you spec and compile the whole thing without writing backend infrastructure from scratch.
