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Cartesia has released Sonic-3.6, the newest version of its real-time text-to-speech model. It arrives roughly three months after Sonic-3.5. The new change is naturalness, and this one is independently checkable. Sonic 3.6 now holds #1 on both Artificial Analysis speech leaderboards — 1,283 Elo on the Provider Voice board and 1,123 on the Controlled Voice board. The second result matters more. That board clones every model onto the same eight reference voices, which isolates the synthesis engine from the voice catalog. Sonic-3.6 leads it, with Sonic-3.5 second and ElevenLabs Eleven v3 third. The model runs on state space models rather than transformers, and Cartesia states sub-90ms time-to-first-audio. It is available in beta.
Is it deployable?
YES, it is available in beta and as a hosted API. Not as self-hosted weights.
Sonic is a closed, commercial model. There are no open weights and no Hugging Face repo. You rent it.
- Company level: Solo developers and startups (Free/Pro $5 tiers), scaleups running contact centers (Startup $49 / Scale $299), and regulated enterprises needing DPAs, BAAs, and SSO.
- Industries: Financial services, healthcare, retail and e-commerce, logistics, recruiting, SaaS support, consumer companion apps, media localization
- Applications: Inbound support agents, outbound qualification calls, IVR replacement, appointment reminders, sales-training simulators, audio localization, in-product voice UI
The Architecture
Sonic runs on state space models rather than transformers. Cartesia’s launch page frames the usual tradeoffs — speed versus naturalness, accuracy versus cost — as architectural, not inevitable.
The practical output is time-to-first-audio. Cartesia states sub-90ms TTS latency, and 100ms transcript latency for its Ink-2 speech-to-text model. Both are vendor-stated model latency, not measured end-to-end round trips.
Interactive explainer
Features that matter in production
Sonic exposes controls built for agent transcripts rather than narration:
- Inline expression tags. Non-verbal expressions like
[laughter]go directly in the transcript. - Instant voice cloning from about 10 seconds of audio.
- Custom pronunciation dictionaries, including IPA overrides such as
<<s|ə|ˈ|p|i|n|ə>>for subpoena. - Speed, volume, and emotion parameters exposed through the API and integrations like the LiveKit Agents plugin.
- Native alphanumerics. Order numbers, phone numbers, and confirmation codes read correctly without preprocessing.
Cartesia’s launch demos show English with natural pauses and filler words, plus Hinglish code-switching between Hindi and English.
Pricing reality
Artificial Analysis normalizes Sonic 3.6 at $49.00 per 1M characters. That is half of ElevenLabs Eleven v3 at $100.00, and well above Speechify Simba 3.2 at $10.00 for a 1,240 Elo.
Cartesia sells credits, not characters. Scale at $299 per month includes roughly 10,667 TTS minutes and 15 concurrent requests. Line voice agents bill separately at $0.06 per minute.
Key Takeaways
- Sonic-3.6 is #1 on both Artificial Analysis speech arenas — 1,283 Elo Provider Voice, 1,123 Controlled Voice.
- Winning the Controlled board means the engine improved, not just the voice catalog.
- It is beta on Cartesia’s API only; docs still list Sonic 3.5 as stable, and partners carry 3.5.
- Deployable as a hosted API, not self-hosted weights; commercial use starts at the $5 Pro tier.
- Latency claims (sub-90ms TTFA) are vendor-stated model latency, so benchmark your own round trip.
Check out the Project Page-Cartesia Sonic, Cartesia launch page, Cartesia pricing, Cartesia docs, Artificial Analysis Speech Arena and @cartesia on X. All figures verified August 18, 2026.. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
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Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.
