---
title: "Cartesia vs Deepgram: TTS latency & word error rate — independent benchmark | Openbenchmarks"
description: "Cartesia vs Deepgram text-to-speech, compared on measured Time to First Audio (TTFA) and Word Error Rate — independent benchmark data on identical inputs, refreshed daily. Not vendor claims."
url: https://openbenchmarks.com/text-to-speech-benchmark-by-coval/cartesia-vs-deepgram
---

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[benchmarks](https://openbenchmarks.com/) / [tts by coval](https://openbenchmarks.com/text-to-speech-benchmark-by-coval) / cartesia vs deepgram 

text-to-speech head-to-head · data by Coval 

# Cartesia vs Deepgram: Text-to-Speechlatency & accuracy

Measured head-to-head, not marketing: the verdict is **split**: **Deepgram** is faster on median TTFA (198 ms vs 284 ms), while **Cartesia** is more accurate (1.4% WER vs 1.6%). Independent data, refreshed daily, on identical inputs. Voice quality and price are not measured.

\[00\] verdict

## Cartesia or Deepgram: which is better?

Each provider's best model on each measured axis, last 7d. Lower is better on both:

| Axis | Cartesia (best model) | Deepgram (best model) | Measured winner |
| --- | --- | --- | --- |
| **Speed**: median TTFA | sonic-3.5 · **284 ms** | flux-haley-en · **198 ms** | **Deepgram** |
| **Accuracy**: avg WER | sonic-3.5 · **1.4%** | flux-haley-en · **1.6%** | **Cartesia** |

Not measured here: **voice quality / naturalness and price**. Treat those as vendor claims until measured. Full leaderboard context: [the independent text-to-speech benchmark →](https://openbenchmarks.com/text-to-speech-benchmark-by-coval) 

\[01\] every model

## Every Cartesia and Deepgram model, measured

All benchmarked models from both providers on the same pinned dataset, ranked fastest-first (median TTFA), last 7d:

| Model | Provider | TTFA median | TTFA p95 | WER | Samples |
| --- | --- | --- | --- | --- | --- |
| **flux-haley-en** | Deepgram | **198 ms** | 264 ms | 1.6% | 643 |
| **aura-2-thalia-en** | Deepgram | **268 ms** | 524 ms | 1.7% | 672 |
| **sonic-3.5** | Cartesia | **284 ms** | 425 ms | 1.4% | 672 |
| **sonic-3.6** | Cartesia | **288 ms** | 372 ms | 1.4% | 672 |

\[02\] source

## Data by Coval

Coval sells voice-agent evaluation infrastructure. Numbers are a rolling 7d aggregate on a pinned dataset under production-realistic conditions. Openbenchmarks mirrors the results with attribution; methodology and runner are open-source.

synced from Coval 2026-10-11 11:51 UTC · [full TTS benchmark →](https://openbenchmarks.com/text-to-speech-benchmark-by-coval) · [methodology →](https://github.com/coval-ai/benchmarks) 

\[03\] faq

## Cartesia vs Deepgram: common questions

### Is Cartesia faster than Deepgram for text-to-speech?

On the current 7d window, Cartesia's fastest model (sonic-3.5) has a median TTFA of 284 ms, vs 198 ms for Deepgram's fastest (flux-haley-en), so Deepgram is faster on measured median latency. Distributions matter too: p95 is 425 ms for Cartesia vs 264 ms for Deepgram.

### Which is more accurate, Cartesia or Deepgram?

By Word Error Rate: Cartesia's best model (sonic-3.5) averages 1.4%, vs 1.6% for Deepgram's best (flux-haley-en). Cartesia leads on measured accuracy. Lower is better; WER is measured on identical inputs under production-realistic conditions.

### Which should I pick, Cartesia or Deepgram?

The measured verdict is split: Deepgram is faster (median TTFA), Cartesia is more accurate (WER). Pick by your constraint: realtime workloads care about latency; fidelity-critical workloads care about WER. Note voice quality / naturalness and price are not measured here.

### Where does this data come from?

Coval sells voice-agent evaluation infrastructure. Every model runs the same pinned dataset under production-realistic conditions. The table is a rolling 7d aggregate, not a single job; Openbenchmarks mirrors it with attribution. Methodology and runner are open-source (Apache-2.0).