---
title: Cartesia's Sonic-3.6 Tops Speech Model Leaderboards
description: The streaming text-to-speech model hit #1 on both Artificial Analysis speech benchmarks with sub-90ms latency.
date: 2026-08-18T00:00:00.000Z
category: ai-news
tags: cartesia, text-to-speech
---

Cartesia shipped Sonic-3.6, a streaming text-to-speech model the company says now ranks first on
both of Artificial Analysis's speech leaderboards, with under 90 milliseconds of
time-to-first-audio.

## The benchmarks

Sonic-3.6 scored 1,283 Elo on Artificial Analysis's Provider Voice board and 1,123 on its
Controlled Voice board, according to
[Cartesia's own launch announcement](https://www.cartesia.ai/launch). The Controlled board clones
every competing model onto the same eight reference voices, isolating the synthesis engine itself
from any advantage a larger voice catalog might provide, which makes it the more objective of the
two rankings.

## What's under the hood

The model runs on state space models rather than the transformer architecture most competing TTS
systems use, and generates expressive speech, including laughter, across more than 40 languages.
It ships roughly three months after Cartesia's previous release, Sonic-3.5, which remains what the
company's own documentation still lists as its stable model while Sonic-3.6 is in beta.

## Why it matters

Sub-90ms latency is the detail that actually matters for real-time use, like a voice agent on a
phone call, more than the naturalness scores that dominate most TTS marketing, since a technically
excellent voice that lags is still unusable for a live conversation. Cartesia leading a benchmark
it also helped define the terms of, via its own state-space-model approach, is worth a note of
caution: Artificial Analysis is a third-party leaderboard, but a company's own launch post is
still the primary source citing these exact numbers here, not an independent re-test.

What to watch: independent latency and quality testing once Sonic-3.6 exits beta and becomes the
documented stable model.

More [AI News](/ai-news/) coverage, or everything tagged [cartesia](/tag/cartesia/).