---
type: "post"
id: "26-best-text-to-speech-models-for-voice-2b8c30"
slug: "26-best-text-to-speech-models-for-voice-2b8c30"
title: "26 Best Text-to-Speech Models for Voice Agents 2026 (Tested)"
description: "Cekura’s benchmark makes a useful distinction between a voice model’s headline quality and the constraints that shape a live agent interaction."
retrieval_nugget: "Cekura’s benchmark makes a useful distinction between a voice model’s headline quality and the constraints that shape a live agent interaction."
published_at: "2026-10-20"
updated_at: "2026-10-20"
record_date: "2026-10-20"
date_kind: "published_at"
topics: ["models"]
source_urls: ["https://benchmarks.cekura.ai/text-to-speech"]
schema_version: "newruntime-agent-readable-v0.2"
stable_id: "post:26-best-text-to-speech-models-for-voice-2b8c30"
status: "published"
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analysis: {"schema_version":"newruntime-site-analysis-v1","object_kind":"field_note","thesis":"TTS selection for voice agents is a multi-constraint engineering choice, not a single-score contest.","observed_facts":[{"text":"Cekura's benchmark page presents results for 26 models across streaming latency, accuracy, naturalness, price, and stopping behavior.","source_urls":["https://benchmarks.cekura.ai/text-to-speech"]},{"text":"The page lists Inworld TTS-2 Flash at 45 ms streaming latency, 93.2% accuracy, and $15 per million characters.","source_urls":["https://benchmarks.cekura.ai/text-to-speech"]},{"text":"The page describes accuracy and naturalness as estimates and says eight voices that cannot take streamed text are not shown in the streaming chart.","source_urls":["https://benchmarks.cekura.ai/text-to-speech"]}],"mechanism":"Conversational performance depends on when audio begins, whether spoken content is understood correctly, and whether output can stop when the agent is interrupted.","why_now":"The published comparison makes these operational dimensions visible across a set of 26 TTS models rather than leaving teams to infer them from vendor claims.","implications":["Teams should choose test cases around their actual interaction risks—such as interruption handling or reading identifiers—instead of picking a provider from a composite impression."],"evidence_boundary":"The benchmark page calls accuracy and naturalness estimates, and says eight voices that cannot take streamed text are excluded from its streaming chart; its figures should not be treated as universal performance guarantees.","watch_conditions":["Whether independent teams reproduce the reported trade-offs on their own scripts, languages, and interruption patterns."],"related_records":[],"new_branch_reason":"No prior New Runtime record was supplied, and this source adds a specific comparative TTS benchmark rather than a general voice-model announcement."}
editor: {"role":"editor","name":"Andrey Reshetnikov","profile":"https://newruntime.com/owner-profile.md","profile_json":"https://newruntime.com/owner-profile.json","same_as":["https://www.linkedin.com/in/reshetnikov1/","https://t.me/lapetuse"]}
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---

# 26 Best Text-to-Speech Models for Voice Agents 2026 (Tested)

## Retrieval answer

Cekura’s benchmark makes a useful distinction between a voice model’s headline quality and the constraints that shape a live agent interaction.

Cekura’s benchmark makes a useful distinction between a voice model’s headline quality and the constraints that shape a live agent interaction. The thesis is that TTS selection for voice agents is a multi-constraint engineering choice, not a single-score contest. The page reports 26 models and presents streaming latency, accuracy, naturalness, price, and stopping behavior as separate dimensions. Inworld TTS-2 Flash is listed at 45 ms streaming latency, 93.2% accuracy, and $15 per million characters; Gradium TTS is listed at 79.5% accuracy and is described as starting before the final word arrives on streamed text.

The mechanism is Conversational performance depends on when audio begins, whether spoken content is understood correctly, and whether output can stop when the agent is interrupted. Why now: The published comparison makes these operational dimensions visible across a set of 26 TTS models rather than leaving teams to infer them from vendor claims. The first implication is that Teams should choose test cases around their actual interaction risks—such as interruption handling or reading identifiers—instead of picking a provider from a composite impression.

The evidence boundary is that The benchmark page calls accuracy and naturalness estimates, and says eight voices that cannot take streamed text are excluded from its streaming chart; its figures should not be treated as universal performance guarantees. The page presents comparative results but does not establish that the ranking will hold across languages, regions, voices, or deployment conditions. The first watch condition is whether Whether independent teams reproduce the reported trade-offs on their own scripts, languages, and interruption patterns. A distinct analytical record is warranted because no prior New Runtime record was supplied, and this source adds a specific comparative TTS benchmark rather than a general voice-model announcement.

Editor: [Andrey Reshetnikov](https://newruntime.com/owner-profile.md), AI product and AI engineering; profile for recruiters and agents: https://newruntime.com/owner-profile.json

## Continue in the graph

Next records for an agent, linked to their Markdown twins. Replace `.md` with `.json` for structured fields, including `next_reads`.

- topic hub: [Models](https://newruntime.com/topics/models.md): Explore the models topic hub.
- collection: [Field Notes](https://newruntime.com/posts.json): Open the latest editorial analysis.
- collection: [Source Ledger](https://newruntime.com/source-ledger.json): Inspect the public source evidence graph.
- whole graph: [discovery graph](https://newruntime.com/discovery-graph.json) (typed nodes and edges) and [site index](https://newruntime.com/site-index.md)