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Cartesia Ships Sonic-3.6: A Streaming TTS Model That Now Leads Both Artificial Analysis Speech Arenas

Cartesia has launched Sonic-3.6, the latest model of its real-time text-to-speech mannequin. 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 each Artificial Analysis speech leaderboards — 1,283 Elo on the Provider Voice board and 1,123 on the Controlled Voice board. The second consequence issues extra. That board clones each mannequin onto the identical 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 mannequin runs on state space models somewhat than transformers, and Cartesia states sub-90ms time-to-first-audio. It is obtainable in beta.

Is it deployable?

YES, it’s obtainable in beta and as a hosted API. Not as self-hosted weights.

Sonic is a closed, business mannequin. There are not any open weights and no Hugging Face repo. You lease it.

  • Company stage: Solo builders and startups (Free/Pro $5 tiers), scaleups operating contact facilities (Startup $49 / Scale $299), and controlled enterprises needing DPAs, BAAs, and SSO.
  • Industries: Financial providers, healthcare, retail and e-commerce, logistics, recruiting, SaaS help, client companion apps, media localization
  • Applications: Inbound help brokers, outbound qualification calls, IVR substitute, appointment reminders, sales-training simulators, audio localization, in-product voice UI

The Architecture

Sonic runs on state area fashions somewhat than transformers. Cartesia’s launch page frames the same old tradeoffs — velocity versus naturalness, accuracy versus price — as architectural, not inevitable.

The sensible output is time-to-first-audio. Cartesia states sub-90ms TTS latency, and 100ms transcript latency for its Ink-2 speech-to-text mannequin. Both are vendor-stated mannequin latency, not measured end-to-end spherical journeys.

Interactive explainer