Text-to-SpeechAnonymized

ElevenLabs Turbo v2.5

High-quality, low-latency text-to-speech model supporting 32 languages with fast response times for real-time applications.

Maker
ElevenLabs
Modality
Speech
License
Proprietary
Open weights
No — proprietary

Overview

What is ElevenLabs Turbo v2.5

ElevenLabs Turbo v2.5 is a high-quality, low-latency text-to-speech model released in July 2024. It supports 32 languages, including Vietnamese, Hungarian, and Norwegian, and delivers speech in as little as 250–300ms — ideal for real-time voice agents, chatbots, and interactive applications.

Running it privately on Venice

On Venice, ElevenLabs Turbo v2.5 runs under an anonymized privacy tier — your text prompts are not stored, profiled, or used for training. This enables uncensored, permissionless use of a leading TTS model while preserving voice quality and low latency, all without building a personal history.

AnonymizedNo prompt trainingTEE · hardware enclaveEnd-to-end encrypted

Assessment

Strengths and limitations

Strengths
  • Ultra-low latency (~250–300ms) makes it ideal for real-time conversational AI and voice agents.
  • Supports 32 languages including Vietnamese, Hungarian, and Norwegian — broad multilingual coverage.
  • Balances high voice quality with speed, outperforming Multilingual v2 by 3x in most languages.
  • Processes up to 40,000 characters per request, suitable for long-form narration and dynamic content.
Limitations
  • Proprietary model: not open-source or self-hostable.
  • Higher cost per character compared to some Venice-hosted rivals like Kokoro and Inworld.
  • No support for voice cloning or custom voice training in this model variant.

Samples

Sample outputs

Generated on Venice with our standard prompt suite — the same scripts we run through every model of this type, so you can judge it like-for-like.

Narration

On Venice, your prompts are processed privately and never stored, profiled, or used to train anyone's model.

Conversational

Wait — so I can run a private voice model with zero data retention, and pay only for what I use? That's genuinely useful.

Expressive range

Three… two… one… liftoff! The rocket roared into the night sky as the crowd erupted in cheers.

Compare every speech model on these scripts

Specifications

Datasheet

Maker
ElevenLabs
Released
July 19, 2024
Modality
Text-to-speech
Languages supported
32
Latency
~250–300ms
Max input length
40,000 characters
Open weights
No — proprietary
Privacy on Venice
Anonymized — prompts not stored
Available on Venice since
Apr 2026
License
Proprietary

API

Call it from your code

Venice exposes an OpenAI-compatible API. Point your base URL at Venice and pass the model id.

curl https://api.venice.ai/api/v1/audio/speech \
  -H "Authorization: Bearer $VENICE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "tts-elevenlabs-turbo-v2-5",
    "input": "On Venice, your prompts are processed privately.",
    "voice": "af_sky",
    "response_format": "mp3"
  }' --output speech.mp3

Pricing

What it costs on Venice

Billed per character on Venice: $62.50 per 1M characters of synthesized speech.

Characters / 1M
$62.50
Per 1M characters

New Venice accounts include a free daily allowance and 500 welcome credits — no credit card required.

Alternatives

How it compares

ModelBest forPrice (Venice)LanguagesLatencyOpen weights
ElevenLabs Turbo v2.5Balances speed and quality across 32 languages with low latency.$62.50 / 1M chars32~250–300msNo
Gradium TTSLower cost alternative on Venice, but lacks public latency and language details.$47.50 / 1M charsNo
Inworld TTS-1.5 MaxMost cost-efficient on Venice, ideal for budget-sensitive, high-volume use.$12.50 / 1M charsNo
Kokoro Text to SpeechCheapest and open-source option on Venice — good for experimentation.$3.50 / 1M charsYes

Balances speed and quality across 32 languages with low latency.

Use cases

What it is good for

  1. 01Real-time voice assistants and chatbots requiring natural, responsive speech.
  2. 02Interactive applications such as gaming NPCs, virtual tutors, or customer service bots.
  3. 03Multilingual content delivery for global audiences.
  4. 04Applications needing fast TTS turnaround without sacrificing audio quality.

Prompting

Getting better results

Use SSML-style emotion tags where supported to add expressiveness to speech output.

Break long texts into segments under 40,000 characters to ensure reliable processing.

Leverage the low latency for streaming scenarios — ideal for back-and-forth dialogue systems.

Version history

Turbo v2.5
2024-07

Current version — low latency, 32 languages.

FAQ

Frequently asked questions

ElevenLabs Turbo v2.5 is a high-quality, low-latency text-to-speech model released in July 2024. It supports 32 languages and delivers speech in about 250–300ms, making it ideal for real-time applications like voice agents and interactive chatbots.

On Venice, ElevenLabs Turbo v2.5 costs $62.50 per 1 million characters of synthesized text. You are billed only for what you use, with no subscription required.

No. ElevenLabs Turbo v2.5 is a proprietary model — it is neither free nor open source. You cannot self-host or modify it, and usage is billed per character on platforms like Venice.

Yes. ElevenLabs Turbo v2.5 supports 32 languages including English, Spanish, French, Hindi, Mandarin, Vietnamese, Hungarian, and Norwegian — making it suitable for global applications.

ElevenLabs Turbo v2.5 has a latency of approximately 250–300ms, enabling near-instantaneous speech synthesis for responsive, real-time conversational AI systems.

Yes. On Venice, your prompts are anonymized and not stored, profiled, or used for training. This ensures private, uncensored access to ElevenLabs Turbo v2.5 without linking speech to your identity.

ElevenLabs Turbo v2.5 offers higher voice quality and broader language support, while Inworld TTS-1.5 Max is significantly cheaper at $12.50 per 1M chars. Choose ElevenLabs for quality and realism, Inworld for cost-sensitive, high-volume use.

Run ElevenLabs Turbo v2.5 privately

No prompt logging. No data used for training.