What is Gemini 3.1 Flash TTS Preview best suited for?

It is best for real-time or near-real-time text-to-speech use cases like voice responses, assistants, and interactive applications where speed matters.

Which modalities does Gemini 3.1 Flash TTS Preview support via LLM.API?

Through LLM.API, Gemini 3.1 Flash TTS Preview takes text as input and returns generated audio as output.

How fast is Gemini 3.1 Flash TTS Preview in terms of latency?

It is optimized for low latency streaming-style speech generation, making it suitable for responsive conversational experiences.

What context window or input length limits apply to Gemini 3.1 Flash TTS Preview?

The model accepts typical TTS-length prompts; very long texts should be chunked by the client before sending for synthesis.

How is Gemini 3.1 Flash TTS Preview priced on LLM.API?

Pricing is usage-based on characters or tokens of text-to-speech generation; check your LLM.API dashboard or pricing page for current rates.

How do I call Gemini 3.1 Flash TTS Preview through LLM.API?

You select the model name in the LLM.API request and send text input, receiving audio bytes or a URL depending on your integration options.

How does Gemini 3.1 Flash TTS Preview compare to non-Flash Gemini models?

Compared to larger, general-purpose Gemini models, it trades broad multimodal reasoning for faster, more efficient text-to-speech generation.

What are the main limitations of Gemini 3.1 Flash TTS Preview?

It focuses on speech synthesis only, so it does not perform general reasoning, code generation, or image understanding tasks.

Can I fine-tune Gemini 3.1 Flash TTS Preview via LLM.API?

Fine-tuning is not available; you use the base Google-provided TTS voices and control style primarily via prompt parameters.

Which endpoint do I call for Gemini 3.1 Flash TTS (Preview)?

POST https://api.llmapi.ai/v1/chat/completions with model "gemini-3.1-flash-tts-preview" and your LLM.API key. The request and response bodies are OpenAI-compatible.

How do I switch from OpenAI to Gemini 3.1 Flash TTS (Preview) on LLM.API?

Change two lines: set base_url to https://api.llmapi.ai/v1 and api_key to your LLM.API key. The rest of your OpenAI SDK code stays the same.

Does Gemini 3.1 Flash TTS (Preview) support function calling and structured outputs?

Function calling: not supported. Structured outputs: not supported.

Gemini 3.1 Flash TTS (Preview) API: Endpoint & Pricing

Gemini 3.1 Flash TTS Preview is Google’s low-latency text‑to‑speech model that generates natural, expressive speech with fine-grained control via style prompts and audio tags.

Start Using API

What is Gemini 3.1 Flash TTS Preview?

Gemini 3.1 Flash TTS Preview is a Google text-to-speech model that converts input text into natural-sounding audio with controllable style and delivery. It is mainly used for real-time voice experiences such as conversational assistants, interactive apps, and accessibility tools that require low-latency, high-quality speech output. It is also suited for content production workflows like audiobooks, podcasts, and voiceovers where expressive, multilingual narration is needed. The model is part of the Gemini 3.1 Flash family and succeeds earlier Gemini Flash TTS variants such as Gemini 2.5 Flash TTS.

Can you self-host Gemini 3.1 Flash TTS Preview?

No. We found no public weights for Gemini 3.1 Flash TTS Preview on Hugging Face, so there is nothing to download, quantize or serve on your own GPUs: no VRAM budget, no vLLM or GGUF build to plan for. The only way to run it is over an API.

Skip the deploy — use LLM.API as your endpoint

No GPUs, no quantization trade-offs. Same model, OpenAI-compatible, up to 30% below list price.

Get API key See API code ↓

Providers

Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick — Balanced (price + speed), Nitro (fastest), or Exacto (highest tool-calling accuracy).

List price by provider

InputOutput

google-ai-studio$0 in

$0 out

Provider list prices; the LLM.API discount applies on top.

Provider	Input	Output	Cache read /M	Latency	Throughput	Uptime
google-ai-studio	in $0; out — per 1M characters	—	—

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 8 Oct 2026.

Try this model

Test Gemini 3.1 Flash TTS Preview right here — free to start.

Gemini 3.1 Flash TTS Preview

Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call the model through the OpenAI-compatible API.

Switching from OpenAI? Change 2 lines.

- base_url="https://api.openai.com/v1"
- api_key="YOUR_OPENAI_KEY"
+ base_url="https://api.llmapi.ai/v1"
+ api_key="YOUR_LLMAPI_KEY"

Everything else stays the same — SDK, message format, tools, streaming. Set model to gemini-3.1-flash-tts-preview and the call runs unchanged.

bash

curl https://api.llmapi.ai/v1/chat/completions \
  -H "Authorization: Bearer $LLMAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-3.1-flash-tts-preview",
    "messages": [
      {"role": "user", "content": "Give me three crisp launch checklist items."}
    ]
  }'

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_LLMAPI_KEY",
    base_url="https://api.llmapi.ai/v1",
)

resp = client.chat.completions.create(
    model="gemini-3.1-flash-tts-preview",
    messages=[
        {"role": "system", "content": "You are a precise product assistant."},
        {"role": "user", "content": "Give me three crisp launch checklist items."},
    ],
)
print(resp.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.LLMAPI_KEY,
  baseURL: "https://api.llmapi.ai/v1",
});

const resp = await client.chat.completions.create({
  model: "gemini-3.1-flash-tts-preview",
  messages: [
    { role: "user", content: "Give me three crisp launch checklist items." },
  ],
});

console.log(resp.choices[0].message.content);

stream = client.chat.completions.create(
    model="gemini-3.1-flash-tts-preview",
    messages=[{"role": "user", "content": "Write a haiku about latency."}],
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta.content
    if delta:
        print(delta, end="", flush=True)

{
  "model": "gemini-3.1-flash-tts-preview",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ]
}

ENDPOINTS

Supported endpoints & features

Base URL https://api.llmapi.ai/v1 · model ID gemini-3.1-flash-tts-preview · OpenAI-compatible.

POST /v1/chat/completionsChat Completions — supported

Feature	Status	How to set it
Function calling (tools)	Not supported	`tools, tool_choice`
Parallel tool calls	Not supported	`parallel_tool_calls`
JSON mode	Supported	`response_format: {"type": "json_object"}`
Structured outputs	Not supported	`response_format: {"type": "json_schema", …}`
Streaming	Not supported	`stream: true`
Vision (image input)	Not supported	`image_url content parts`
Web search	Not supported	`web_search`

ERRORS

Errors & fallback routing

Code	What it means	What to do
`401`	Missing or invalid API key.	Send `Authorization: Bearer YOUR_LLMAPI_KEY` and check the key is active.
`429`	Rate limited, or the upstream provider is throttling the request.	Back off and retry with jitter; the gateway also retries the request on another provider where one is available.
`5xx`	Upstream provider error or timeout.	Retry; fallback routing sends the retry to the next healthy provider for this model.

When a provider fails or throttles, the request is routed to the next provider serving this model — the ones listed in the providers table above.

Uptime

30-Day Uptime

100.00%

Past Incidents (30d)

Error rate (24h)

0.00%

Last 30 days

30/30 days operational | 100.00% uptime

30 days ago Today

Operational Degraded Outage Maintenance

See All Incidents

Gemini 3.1 Flash TTS (Preview) in production

Best workloadsLLM.API features

What the model is good at.

Conversational TTS

Generates natural, conversational speech audio from text prompts, suitable for interactive agents and real-time spoken dialogue applications.
Multilingual Speech

Supports speech output in multiple languages and accents, enabling localized voice experiences across diverse global user audiences.
Screen Reader Output

Produces clear spoken renderings of on-screen content, assisting in accessibility scenarios like screen readers and reading aids.
Image Prompt Narration

Turns model-generated or provided image descriptions into spoken narration, enabling voiceover experiences for visual content pipelines.
Text From Images

Reads OCR-extracted text aloud from images or documents, turning recognized visual text into accessible spoken audio output.

What you get when you call Gemini 3.1 Flash TTS (Preview) through LLM.API.

Intelligent Model Routing

Automatically route each request to the optimal model across providers based on latency, cost, and quality so you keep one integration while strategies evolve.
One endpoint, any model.
Cost-Aware Orchestration

Mix premium and budget models, apply dynamic caps, and analyze per-request spend so you can scale usage without surprise bills or manual tuning.
Optimize tokens, not hacks.
Resilient Fallback Flows

Define automatic cross-provider fallbacks and retries so outages, rate limits, or timeouts degrade gracefully instead of breaking your production workloads.
Stay up when APIs don’t.
End-to-End Observability

Trace every call across providers with logs, metrics, and latency breakdowns so you can debug incidents, tune prompts, and prove reliability to stakeholders.
See every token’s journey.
Task-Level Abstractions

Describe tasks like chat, extraction, or generation once and let LLM.API pick the right tools and models, avoiding provider-specific boilerplate.
Think tasks, not endpoints.
High-Throughput Batch Jobs

Send large batches across providers with automatic chunking, concurrency control, and retries so you can process millions of items without custom pipelines.
Batch at platform scale.

When to Use — When NOT to Use

Use it if...

You need fast, low-cost text-to-speech for interactive apps, games, or chatbots.
Your use case involves prototyping voice features and you can tolerate preview-level stability.
You need to generate spoken feedback or instructions from short text prompts on-demand.
Your use case involves turning UI messages or notifications into natural-sounding speech quickly.
You need a cloud TTS service that integrates easily with other Gemini-family models.
Your use case involves adding basic voice output to existing web or mobile workflows.

Avoid if...

You need a fully production-hardened TTS service with strong long-term backward compatibility guarantees.
Your workload requires strict enterprise compliance certifications or audited data-handling guarantees today.
You need ultra-low-latency, on-device text-to-speech where cloud round-trips are unacceptable.
Your workload requires fine-grained control over phonemes, prosody, or custom voice cloning.
You need guaranteed stable pricing, quotas, and SLAs beyond typical preview-stage offerings.
Your workload requires multilingual TTS coverage beyond the languages currently supported in preview.

LLM API rating for Gemini 3.1 Flash TTS (Preview)

★ 3.9/ 5.0LLM API rating
50% production adoption · 30% category benchmark · 20% tooling & valueEstimated from published specs

4.8Production adoptionVery high production volume

3.0Benchmark qualityScored on published audio support

2.9Developer ecosystemContext window, tool and JSON support, price against the category median

Voice & audio 3.9

How is this rating calculated?

Every model gets a separate 1.0–5.0 score for each category it can be used in, so a model can rate high for coding and lower for vision. The score combines three parts:

Production adoption — 50%. How widely the model is actually used in production: provider traffic, LiteLLM and gateway usage, open-weight downloads and its LMArena rank. Proven, widely used models score highest.
Category benchmark — 30%. The public benchmark that matters for that category — the Artificial Analysis Intelligence Index and LMArena Elo for reasoning, LMArena coding Elo for coding, image and document support plus vision Elo for vision, tool and JSON support for function calling, measured speed for voice.
Tooling & value — 20%. Context window, support for tools, structured outputs and streaming, and price compared with the category median.

Scores of 4.0 and above are shown in green, 3.0–3.9 in yellow, below 3.0 in red. When a model has no public data for a part, it gets a neutral value instead of an invented number.

A “Verified benchmark” mark (✓) means an independent lab — LMArena or Artificial Analysis — has actually tested that model, and the source figure is shown. Models without that mark are labelled “Estimated from published specs”: the score comes from the published context window, tool and schema support, price and adoption only, because no independent lab has tested them yet.

One exception: a brand-new top-tier model that scores very high on the independent benchmarks but has no usage history yet is rated on the benchmark (60%) and tooling & value (40%) alone, so a missing adoption figure can't hold it back. All inputs come from public sources and are refreshed weekly.

Adoption and benchmark inputs come from public sources — LMArena, Artificial Analysis Intelligence Index and published model specs. Updated weekly.

COMMUNITY

What developers say about Gemini 3.1 Flash TTS Preview

LMArena—Speed—Sources: LMArena, Artificial Analysis

Summarised from public developer discussions and write-ups. Opinions are the sources’, not LLM.API’s.

Developers on Simon Willison's blog report successful experimentation with the model's high steerability, noting its ability to follow complex natural-language "director's notes" and specific regional accent instructions.
Developers on Nipun Batra's blog observe that the model's new bracketed "audio tags" allow for granular, inline control over delivery style and emotion without needing separate voice configuration schemas.
Analysis from ChatForest highlights that while the model is excellent for LLM-native workflows, it lacks support for the bidirectional Live API, making it unsuitable for unstructured real-time voice assistants.

SOURCES

Frequently Asked Questions

What is Gemini 3.1 Flash TTS Preview?

Gemini 3.1 Flash TTS Preview is a Google model that converts text into speech with a focus on low latency and efficient generation.
What is Gemini 3.1 Flash TTS Preview best suited for?

It is best for real-time or near-real-time text-to-speech use cases like voice responses, assistants, and interactive applications where speed matters.
Which modalities does Gemini 3.1 Flash TTS Preview support via LLM.API?

Through LLM.API, Gemini 3.1 Flash TTS Preview takes text as input and returns generated audio as output.
How fast is Gemini 3.1 Flash TTS Preview in terms of latency?

It is optimized for low latency streaming-style speech generation, making it suitable for responsive conversational experiences.
What context window or input length limits apply to Gemini 3.1 Flash TTS Preview?

The model accepts typical TTS-length prompts; very long texts should be chunked by the client before sending for synthesis.
How is Gemini 3.1 Flash TTS Preview priced on LLM.API?

Pricing is usage-based on characters or tokens of text-to-speech generation; check your LLM.API dashboard or pricing page for current rates.
How do I call Gemini 3.1 Flash TTS Preview through LLM.API?

You select the model name in the LLM.API request and send text input, receiving audio bytes or a URL depending on your integration options.
How does Gemini 3.1 Flash TTS Preview compare to non-Flash Gemini models?

Compared to larger, general-purpose Gemini models, it trades broad multimodal reasoning for faster, more efficient text-to-speech generation.
What are the main limitations of Gemini 3.1 Flash TTS Preview?

It focuses on speech synthesis only, so it does not perform general reasoning, code generation, or image understanding tasks.
Can I fine-tune Gemini 3.1 Flash TTS Preview via LLM.API?

Fine-tuning is not available; you use the base Google-provided TTS voices and control style primarily via prompt parameters.

COMPARE

Competitive Models

Gemini 3.5 Flash is Google’s natively multimodal reasoning model optimized for very low latency and cost while maintaining frontier‑level performance, particularly for coding and agentic workflows.

Nano Banana Pro (Gemini 3 Pro Image Preview)

Vision-Language

Nano Banana Pro (Gemini 3 Pro Image Preview) is Google’s preview-stage image generation and editing model built on the Gemini 3 Pro family, optimized for complex, multi-turn visual creation tasks.

Gemini 3 Flash Preview

Instruction Following

Gemini 3 Flash Preview is a Google multimodal large language model optimized for high speed and cost‑effective performance in complex reasoning tasks. It offers long‑context understanding and strong support for agents, coding, and retrieval‑augmented applications.

Get one key to every model

Swap your API key. Keep your code.

Start for free Talk to sales

LLM API sells to AI agents - live prices and self-serve payment

Gemini 3.1 Flash TTS (Preview) API: Endpoint & Pricing

What is Gemini 3.1 Flash TTS Preview?

Can you self-host Gemini 3.1 Flash TTS Preview?

Providers

Try this model

Code snippet

Supported endpoints & features

Errors & fallback routing

Uptime

Gemini 3.1 Flash TTS (Preview) in production

Conversational TTS

Multilingual Speech

Screen Reader Output

Image Prompt Narration

Text From Images

Intelligent Model Routing

Cost-Aware Orchestration

Resilient Fallback Flows

End-to-End Observability

Task-Level Abstractions

High-Throughput Batch Jobs