Gemini 3.1 Flash TTS: The Most Controllable Text-to-Speech Model Yet
Google's Gemini 3.1 Flash TTS lets you control emotion, pacing, and tone with inline tags. Here's what it can do and how to try it free in AI Studio.
What Makes Gemini 3.1 Flash TTS Different
Text-to-speech models have gotten impressively good at sounding human. The problem is that most of them stop there. You can pick a voice, maybe adjust the speed, and that’s it. What the model does with emphasis, pauses, and emotional coloring is entirely up to it.
Gemini 3.1 Flash TTS changes that. Google’s new dedicated text-to-speech model lets you control emotion, pacing, stress, and tone directly from your prompt — using inline tags that sit right inside the text. No post-processing. No separate audio editor. You write what you want the voice to say, you annotate how you want it to sound, and the model follows.
That level of expressive control is what sets Gemini 3.1 Flash TTS apart from the current generation of neural voice models. This article covers what the model is, how the tagging system works, what you can actually build with it, how it compares to alternatives, and how to try it for free.
What Gemini 3.1 Flash TTS Actually Is
Gemini 3.1 Flash TTS is a standalone text-to-speech model in Google’s Gemini 3.1 family. It’s purpose-built for speech synthesis rather than general language tasks — which means it’s optimized specifically for audio output quality, prosody control, and latency.
The “Flash” designation signals its position in the model lineup: fast, cost-efficient, and practical for production use. If you’ve followed the broader Gemini 3.1 lineup, you know Flash models are designed to be the workhorses — capable enough to handle real tasks without the compute overhead of Pro-tier models. The Gemini 3.1 Flash Live multimodal voice AI follows the same philosophy for real-time conversational use cases.
The TTS variant takes a different path. It’s not conversational. It’s generative audio from text — think audiobooks, voiceovers, IVR systems, accessibility tools, and AI-narrated content. The model accepts a text string, processes your inline control annotations, and returns a high-quality audio file.
It’s available in Google AI Studio now, and accessible via the Gemini API for developers who want to integrate it into their own products.
The Inline Tag System: How Expressive Control Works
This is the feature that most people focus on when they first encounter Gemini 3.1 Flash TTS, and for good reason. Most TTS models treat the text you give them as flat input. The model infers prosody on its own, and you just hope it sounds right.
Gemini 3.1 Flash TTS uses a structured inline tagging syntax that lets you annotate text with specific instructions before the model processes it. The tags are embedded directly in the input string and tell the model things like: slow down here, sound excited at this point, add a longer pause, or emphasize this word.
Emotion Tags
Emotion tags let you specify the affective quality of the speech. You can mark a sentence as [cheerful], [sad], [urgent], [calm], or [serious], and the model adjusts its delivery accordingly.
This isn’t a simple pitch-and-rate adjustment. The model has been trained to understand what cheerful speech actually sounds like versus what urgent speech sounds like — the micro-variations in cadence, breathiness, and emphasis that signal emotional state. The result is noticeably different from a model that just speeds up to simulate urgency.
Pacing and Pause Tags
You can control the rhythm of speech explicitly. Tags like [slow], [fast], and [pause] let you shape the timing of delivery without editing the audio waveform. The [pause] tag accepts duration values, so you can specify a half-second break before a key point or a longer pause after a section header in a narration.
For anyone who’s ever produced audio content manually, this is significant. Controlling pacing through text annotations rather than a waveform editor saves real time.
Stress and Emphasis Tags
The [emphasis] tag lets you mark specific words or phrases for stronger delivery. Combined with the model’s natural prosody engine, this produces speech that sounds like a human deliberately stressing a point — not like a robot bolding a word.
This is useful for things like:
- Instructional content where key terms matter
- Marketing copy where specific claims need to land
- Customer service scripts where certain phrases (like “no charge” or “immediately”) should carry weight
Voice Style Tags
Beyond emotion, you can also specify broader stylistic modes: [newscast], [documentary], [conversational], [formal]. These shift the overall register of the voice rather than a single phrase.
The newscast style, for example, produces the clipped precision and measured pacing of broadcast journalism. Conversational mode loosens things up — contractions, natural rhythm, the occasional upward inflection on rhetorical questions.
Available Voices and Language Support
Gemini 3.1 Flash TTS ships with a set of curated voices rather than voice cloning. At launch, Google provides a range of options covering different genders, ages, and regional accents. Each voice has been trained to handle the full range of the tagging system — not every voice responds identically to every emotion tag, which is intentional. A voice character described as warm and mid-range will interpret [urgent] differently than one built for authoritative delivery.
Language support is broad. The model covers major world languages including English, Spanish, French, German, Portuguese, Japanese, Korean, Hindi, Arabic, and Mandarin Chinese. For multilingual deployment, this matters — you’re not limited to a strong English-only model with degraded quality on other languages.
If you’re building multilingual products and want to understand how AI handles language diversity at scale, there’s a useful breakdown in this guide to AI-powered multilingual support that covers the infrastructure side of the problem.
What You Can Build With It
Audiobook and Long-Form Narration
The pacing and pause controls make Gemini 3.1 Flash TTS practical for long-form audio content. A chapter of a business book reads differently than a thriller, and the tagging system lets you match the voice to the material. You can add chapter pauses, slow down for complex passages, and push emphasis on key arguments.
This is a real workflow for content creators who want to publish audio versions of written content without recording it manually.
IVR and Customer Service Voice Flows
Interactive voice response systems have historically used robotic-sounding synthesized speech because natural prosody was expensive or required professional recording. With Gemini 3.1 Flash TTS, you can generate phrases that sound genuinely human — calm, clear, and emotionally appropriate.
A customer calling about a billing dispute hears a different tone than one calling to upgrade their plan. The tagging system lets you script those differences directly.
AI Voice Agents
This is where the model fits alongside Google’s broader Gemini voice ecosystem. If you’re building a voice agent that handles conversation, Gemini 3.1 Flash Live handles the real-time interaction side. TTS handles the scripted or generated speech that the agent delivers. See this guide on building a voice agent with Gemini 3.1 Flash Live for the full architecture picture.
For TTS specifically, the expressive control means your agent doesn’t have to sound like a robot when it reads back a confirmation or explains a policy. You can write the response in a [calm] and [conversational] register and have it delivered that way consistently.
Accessibility Tools
Screen readers and assistive technology have long relied on flat, robotic TTS. Gemini 3.1 Flash TTS could improve this significantly — especially for users who depend on synthesized voice for extended periods. Better prosody reduces cognitive load. Emotional modulation makes content easier to follow.
Content Creation Pipelines
Video producers, podcast creators, and social media teams are increasingly using AI-generated voice for narration. The tagging system gives them editorial control without audio engineering skills. Write the script, annotate it for delivery, generate the audio, drop it into the timeline.
How It Compares to Other TTS Models
vs. ElevenLabs
ElevenLabs has been the benchmark for high-quality neural TTS, particularly for voice cloning. It produces excellent audio and has a large voice library. But its expressive control is primarily handled through voice selection and tone settings at the account level — not inline per sentence.
Gemini 3.1 Flash TTS’s inline tag system is more granular. You can shift emotion mid-paragraph without switching voices or making separate API calls. For content that needs dynamic variation, that’s a meaningful difference. On pure audio quality for a single voice, ElevenLabs is still competitive. The comparison between Gemini 3.1 Flash Live and ElevenLabs for voice agent deployment covers their strengths in more depth.
vs. Mistral’s Open-Weight TTS
Mistral’s open-weight TTS model takes a different approach: local deployment and voice cloning. If privacy and self-hosting are priorities, that’s a real advantage. Gemini 3.1 Flash TTS is cloud-based and doesn’t support voice cloning at launch. Mistral’s open-weight TTS model is worth looking at if those constraints matter for your use case.
vs. Smallest.ai Lightning V3.1
Smallest.ai’s Lightning V3.1 is optimized for low-latency conversational use — the kind of sub-200ms response you need for live voice interactions. Gemini 3.1 Flash TTS isn’t targeting that use case. It’s batch-style generation for content that doesn’t need real-time delivery. The Smallest.ai Lightning V3.1 model sits in a different category.
vs. OpenAI TTS
OpenAI’s TTS offering is solid but has limited expressive control. You pick a voice and a speed, and that’s the extent of it. There’s no inline annotation system. For simple use cases, it’s fine. For anything that needs dynamic emotional variation, Gemini 3.1 Flash TTS is more capable.
How to Try It in Google AI Studio
Google AI Studio gives you free access to Gemini 3.1 Flash TTS to test before committing to API usage. Here’s how to get started:
- Go to Google AI Studio at aistudio.google.com and sign in with your Google account.
- Select the model from the model picker. Look for
gemini-3.1-flash-ttsin the model list. - Open the Speech output mode — AI Studio has a dedicated audio generation interface. Select “Text to Speech” from the output options.
- Write your text in the input field and add inline tags where you want expressive control. For example:
Welcome back. [pause=0.5] [cheerful] We're glad you're here. - Select a voice from the available options. Preview a few to find one that fits your use case.
- Generate and download the audio file. AI Studio lets you preview in-browser before downloading.
For API access, you’ll use the Gemini API with the standard authentication flow. The endpoint accepts your text input with tags in the request body and returns an audio file in your chosen format (MP3 or WAV are the standard options).
Pricing via API is per character of input text. Google’s standard Gemini Flash pricing applies, making it cost-competitive for production volumes.
Where Remy Fits
If you’re building an application that uses Gemini 3.1 Flash TTS — an audiobook generator, a voice agent content pipeline, an accessibility tool, an IVR script builder — you need more than just the API call. You need a backend to handle the input, authentication, storage for generated files, and a frontend for whoever’s using it.
That’s where Remy comes in. Remy compiles annotated markdown specs into full-stack applications: backend, database, auth, and deployment. You describe what your app does — including something like “users input text and receive a generated audio file using Gemini TTS with selectable voice styles” — and Remy builds the application from that spec.
The underlying infrastructure runs on MindStudio, which already has 200+ AI models available, including Gemini models. So you’re not wiring up a new API integration from scratch. You’re describing what you want the app to do, and the code follows.
For teams shipping voice features, content creation tools, or any product that generates audio, Remy handles the scaffolding so you can focus on the product logic. Try it at mindstudio.ai/remy.
Gemini 3.1 Flash TTS in the Broader Gemini Ecosystem
It’s worth placing this model in context. Google’s Gemini 3.1 family is one of the most capable across modalities. Beyond TTS, the ecosystem includes Gemini Embedding 2 for multimodal search, Flash Live for real-time voice conversations, and the broader Pro tier for complex reasoning tasks.
For builders who want to understand how Gemini fits into agentic AI workflows, the TTS model is a natural component in any voice-enabled agent stack. A model like Gemini Pro or Flash reasons about what to say. Flash TTS says it — with the right emotional register.
Google is clearly building out a full audio stack. The Google Lyria 3 Pro music generation model handles music synthesis, Flash TTS handles speech. These aren’t isolated products — they’re part of a coherent bet on generative audio as a production-grade capability.
And if you’re looking at the competitive picture across AI platforms, Google’s investment in audio puts them ahead of most on this axis. OpenAI and Anthropic have voice features, but neither has a comparable inline expressive control system in TTS.
Limitations to Know About
Before committing Gemini 3.1 Flash TTS to a production workflow, there are a few limitations worth knowing:
No voice cloning. The model works with curated voices only. If you need to clone a specific person’s voice, you’ll need a different tool. Mistral’s open-weight TTS and ElevenLabs both support this.
Cloud-only. There’s no local deployment option at launch. All inference happens on Google’s infrastructure. For applications with strict data residency requirements, this may rule it out.
Tag parsing sensitivity. The inline tag system is powerful, but malformed tags can produce unexpected results. If you’re generating tags programmatically (e.g., from an LLM that’s writing the script), test edge cases carefully.
Long-form coherence. Like most TTS models, very long inputs (tens of thousands of characters) may produce inconsistencies in voice character or pacing across the document. For book-length content, batch processing by chapter produces better results than submitting the full text at once.
Latency isn’t real-time. This model is not designed for sub-100ms response times. For real-time conversational voice, Flash Live is the right choice. For generated content where you can afford a few seconds of processing, Flash TTS is fine.
Frequently Asked Questions
What is Gemini 3.1 Flash TTS?
Gemini 3.1 Flash TTS is Google’s dedicated text-to-speech model in the Gemini 3.1 model family. It generates high-quality speech audio from text input and supports inline annotation tags that let you control emotion, pacing, emphasis, and tone at the sentence and word level. It’s available via Google AI Studio and the Gemini API.
How do I control emotion and pacing in Gemini 3.1 Flash TTS?
You add inline tags directly in your text input. For example, [cheerful], [slow], [pause=1.0], and [emphasis] are inserted at the relevant points in the string. The model reads these annotations during synthesis and adjusts its delivery accordingly. Tags can be applied to individual words, phrases, or full sentences.
Is Gemini 3.1 Flash TTS free to use?
Yes, you can try it for free in Google AI Studio without any API key costs. Production API usage is billed per character of input text at standard Gemini Flash pricing, which is competitive with other cloud TTS providers.
How does Gemini 3.1 Flash TTS compare to ElevenLabs?
ElevenLabs has strong audio quality and a large voice library, including voice cloning capabilities. Gemini 3.1 Flash TTS offers more granular in-prompt expressive control through its inline tag system, which lets you shift emotion and pacing mid-sentence without separate API calls. ElevenLabs is better if you need voice cloning; Gemini 3.1 Flash TTS is better if you need dynamic, annotated expressive control in generated scripts.
Does Gemini 3.1 Flash TTS support multiple languages?
Yes. The model supports a broad set of languages including English, Spanish, French, German, Portuguese, Japanese, Korean, Hindi, Arabic, and Mandarin Chinese, among others. Quality varies by language, with English receiving the most optimization.
Can I use Gemini 3.1 Flash TTS for real-time voice applications?
Not directly — this model is optimized for batch audio generation, not sub-100ms real-time response. For real-time conversational voice, Gemini 3.1 Flash Live is the appropriate model. Many applications use Flash Live for the conversation layer and Flash TTS for pre-generated audio segments like announcements or narration.
Key Takeaways
- Gemini 3.1 Flash TTS is Google’s dedicated speech synthesis model with an inline tag system for controlling emotion, pacing, emphasis, and voice style.
- The tagging system is what distinguishes it from most TTS models — you annotate your text directly rather than relying on the model’s own prosody inference.
- It’s available free to test in Google AI Studio, with API access billed per character at standard Flash pricing.
- It’s not a replacement for voice cloning tools or real-time conversational voice models — it’s best suited for content generation, scripted voice, and production audio pipelines.
- For teams building full-stack applications around this model, Remy handles the backend, auth, database, and deployment so you can ship the product rather than just the API call.
