Next-gen low-latency neural speech generation supporting inline emotion tags and conversational interruptions.
Technical Specifications
- Provider
- Category
- flagship
- Release Date
- 2026-04
- Batch API Discount
- 20% off async batch
- Consumer Subscription
- $20 / month
- Reasoning Effort Control
- Fixed
- Structured Outputs
- No
- Output Modalities
- audio
- Official Documentation
- Visit Official Docs ↗
Data Privacy, Licensing & Deployment Governance
Official Provider Release Intelligence, API Quotas & Agentic Specs
- Official API Model ID (SDK String)
- models/gemini-3-1-flash-tts
- Supported SDK & API Protocol
- Google Gemini API (generateContent) + OpenAI-Compatible SDK
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2028+)
- Production Rate Limits (RPM & TPM)
- 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
- Long-Context Pricing Tier (>128K / >200K)
- Flat rate ($0.75/1M in) across full 8K window
- Prompt Cache Write & TTL Policy
- Implicit + Explicit Context Caching at $0/1M with configurable TTL
- Computer Use & Browser GUI Agent
- Text-only completion (No GUI action loop)
- MCP & Parallel Tool Calling
- Prompt-based JSON extraction only
- Web Search Grounding & Code Sandbox
- Built-in Google Search Grounding + Managed Python Code Execution Sandbox
- Fine-Tuning & Distillation Support
- Vertex AI Supervised Adapter Tuning & Context Distillation
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token)
- Weights Precision & Quantization
- Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster)
- Minimum GPU VRAM Requirement
- Zero Local VRAM Required (100% Serverless Cloud API)
- Supported Inference Engines
- Managed Google Cloud API
- System Card & Safety Framework
- Google DeepMind Frontier Safety Framework Verified
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Gemini 3.1 Flash TTS Cost, Context, Hardware & API Integration
For single-user chat, a $20/mo subscription caps your bill below this API volume.
Break-even point: ~49 requests/day equals $20/month.
Same Monthly Workload Cost Across Popular Reference Models
Live Prompt Paste Tokenizer & Cost Counter
Paste real system instructions, JSON schemas, or code to measure BPE token counts across tokenizers.
Hybrid Prompt Routing Cascade Simulator
Route routine queries to a fast lightweight model ($0.15/1M) and escalate only complex reasoning turns to Gemini 3.1 Flash TTS:
Benchmark Scores & 6-Axis Capability Radar
- Chatbot Arena Elo
- 1360pts
Strengths
- Sub-250ms time-to-first-audio chunk
- Supports inline audio tags like [laughter] and [sigh]
- Ultra-natural conversational cadence
Limitations & Trade-offs
- Restricted 8K context window
- Higher output pricing than text-only models
Editorial Assessment
State-of-the-art conversational voice synthesis for latency-critical agents.
Best for: Real-time voice bots, live telephonic agents, and gaming NPCs
Compare Gemini 3.1 Flash TTS
Frequently Asked Questions about Gemini 3.1 Flash TTS
How much does the Gemini 3.1 Flash TTS API cost per 1M tokens?+
Gemini 3.1 Flash TTS by Google is priced at $0.75 per 1M input tokens and $12 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0 per 1M tokens, and asynchronous Batch API requests receive a 20% discount.
Should I use a $20/month subscription or the Gemini 3.1 Flash TTS pay-as-you-go API?+
A flat $20/month consumer subscription is predictable for daily interactive chat and manual coding. However, if you are building software, running automated workflows, or sending low-to-moderate volume with prompt caching enabled, the pay-as-you-go API is typically far more cost-effective and offers higher rate limits without manual UI caps.
How many words and pages fit in Gemini 3.1 Flash TTS's context window?+
Gemini 3.1 Flash TTS features a 8K tokens context window, which equals approximately 6,144 English words or ~16 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 4,096 tokens.
Can I run Gemini 3.1 Flash TTS locally on my own GPU (Open Weights)?+
No, Gemini 3.1 Flash TTS is a proprietary closed-weights model hosted by Google. It cannot be downloaded for local GPU execution and must be accessed via Google's official API or cloud platform.
Does Google train on data sent to the Gemini 3.1 Flash TTS API, and is commercial use allowed?+
API payloads are excluded from model training by default. License tier: Proprietary Commercial API (Google Terms). Full commercial ownership of generated outputs. Supported deployment channels include: Google AI Studio API, Google Cloud Vertex AI.
How does Gemini 3.1 Flash TTS perform on coding and reasoning benchmarks?+
Gemini 3.1 Flash TTS achieves a composite Intelligence Index of 72/100, scoring N/A% on SWE-bench Verified, N/A% on GPQA Diamond, N/A% on MMLU-Pro, and 1360 pts on Chatbot Arena Elo.
What is Gemini 3.1 Flash TTS best used for?+
Gemini 3.1 Flash TTS is best suited for: Real-time voice bots, live telephonic agents, and gaming NPCs. Assessment: State-of-the-art conversational voice synthesis for latency-critical agents.
Google Family Lineage, Routing Tiers & Upgrade Path
Step down to a lighter sibling for high-volume routing or step up to Google's flagship reasoning tier:
Gemini 4 Argon
Gemini 3.8 Flash
Gemini 3.7 Flash
Gemini 3.6 Flash
Gemini 3.5 Flash-Lite
Gemini Omni Flash
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to Gemini 3.1 Flash TTS:
The basic unit of text that a language model processes, typically representing about 3 to 4 English characters or roughly 0.75 words. For example, the sentence "Artificial intelligence is fast" breaks down into about 5 tokens.
A standardized evaluation suite used to objectively measure and rank an AI model's capabilities across specific tasks — such as SWE-bench Verified for software engineering, AIME for competitive math, or MMLU for general knowledge.
The maximum number of tokens a model can consider at once — both the input it reads and the output it writes. A 1,000,000-token context window can hold roughly 750,000 English words in a single request.
The neural network architecture introduced in 2017 that powers modern LLMs, using self-attention mechanisms to process all tokens in a sequence concurrently rather than sequentially.
Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.