Text to Speech Benchmarking Methodology

Scope & Background

Artificial Analysis performs benchmarking on Text to Speech models delivered via serverless API endpoints. This page describes our Text to Speech benchmarking methodology, including both our quality benchmarking and performance benchmarking. We consider Text to Speech endpoints to be serverless when customers only pay for usage, not a fixed rate for access.

For both our performance benchmarking and within the Speech Arena, our focus is reflecting the end-user experience of users using the serverless APIs. We focus on benchmarking the time to receive the audio file locally. Where the API response is a URL rather than bytes, we include the time of downloading the file in our response time measurement. Our approach is to use the standard implementation of provider APIs as suggested by each provider's documentation. Where an option on the provider's API, we standardize the sample rate of audio to 22.05 kHz.

Key Metrics

We use the following metrics to track quality, performance and price for Text to Speech models.

Quality Elo

Relative Elo score of the models as determined by responses from users in the Artificial Analysis Text to Speech Arena.

Some models may not be shown due to not yet having enough votes. We use a similar Linear Regression model, similar to how LMSYS calculates Elo scores for Chatbot Arena.

Price per 1M Characters

All TTS models are reported as price per 1M characters of input text. Where providers do not price per character directly, we derive an equivalent:

Per character: Listed rate used directly
Subscription plans: Effective rate from the lowest-cost plan, annual where available, that includes at least 1M characters, assuming 80% utilization
Token-based: Derived using batch pricing, converting characters to input tokens and estimating audio output duration
Output duration: Converted using an assumed speaking rate of 150 words per minute, approximately 825 characters per minute
Per byte: 1 UTF-8 byte is approximately 1 character for English text
Inference time: Estimated from benchmark runs using ~25 texts of ~500 characters
Open weights: No commercial API pricing; listed without price
S2S models: Derived from the cost to generate speech for 20 fixed TTS arena prompts with text input and audio-only output. We include reported text input, output audio, output text, and separately exposed reasoning/thinking tokens. Output text tokens are included when they are reported as part of the audio response. Cached-token discounts are ignored.

When reporting price, we do not include temporary discounts.

Generation Time

Median time the provider takes to generate a single audio clip with ~500 input characters, calculated over the past 14 days of measurements.

Generation Time includes downloading the audio clip from the provider where a URL is provided rather than an audio response. This is to reflect the end-user latency of receiving a generated audio clip and as URLs can be generated prior to audio completion. Audio clips are generated at batch size of 1 where relevant.

Benchmarking is conducted 4 times daily at random times each day. For each benchmarking evaluation we select a single random voice for each model. A unique prompt of ~500 characters is used for each generation.

Model Voices

For each model tested, we test multiple voices to ensure that our comparison between models is representative and fair. Voice characteristics such as accent, gender and style are typically aspects of the voices that each model can generate speech for, not the underlying model. For each model we select 2 voices of each combination of Male and Female, and US and UK accents (8 combinations in total). Where a gender and accent is not available, we exclude this combination from evaluation in the Speech Arena.

Where voices are not provided, as is typically the case for open source models, we use voice clips from professional voice actors as source files for generating speech. All voice clips have been licensed for commercial use.

Select a model creator below to see the voices currently used in the Speech Arena for each of their models.

Model and Provider Inclusion Criteria

Our objective is to analyze and compare popular and high-performing Text to Speech models and providers to support end-users in choosing which to use. As such, we apply an 'industry significance' and competitive performance test to evaluate the inclusion of new models and providers. We are in the process of refining these criteria and welcome any feedback and suggestions. To suggest models or providers, please contact us via the contact page.

Statement of Independence

Benchmarking is conducted with strict independence and objectivity. No compensation is received from any providers for listing or favorable outcomes on Artificial Analysis.