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audio model comparison

Eleven v3 vs Sonic 3.6

Eleven v3 and Sonic 3.6 are both current audio models, from ElevenLabs and Cartesia. Sonic 3.6 is 19% cheaper per character than Eleven v3, $0.000065 vs $0.00008. On Artificial Analysis Speech Arena, Eleven v3 scores 1177 Elo and Sonic 3.6 1286 Elo (Artificial Analysis, measured October 13, 2026).

Data verified October 13, 2026 · Dataset 2026-10-13

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Side by side

Standard-tier prices in effect today. The better value in each row is highlighted.

FactEleven v3Sonic 3.6
Price per character$0.00008$0.000065 (better)
Artificial Analysis Speech Arena1177 Elo (#7 of 11)1286 Elo (#3 of 11) (better)
ProviderElevenLabsCartesia
Release dateNot reportedAugust 27, 2026
AccessAPI onlyAPI only
Context windowNot reportedNot reported
Max outputNot reportedNot reported
Inputtexttext
Outputaudioaudio

Scores:Artificial Analysis · 2026-10-13

Open both in the interactive comparison

When to choose each

Eleven v3

Best for

  • Expressive multilingual speech

Watch out for

  • ElevenLabs now calls it a previous-generation model; Eleven v4 replaces it at the same $0.08 per 1,000 characters
Full Eleven v3 profile

Sonic 3.6

Best for

  • Multilingual speech for voice agents
  • Indian-language and Hinglish speech

Watch out for

  • Credit-based plans only (1 credit per character); the recorded rate is the Pro plan overage of $65 per 1M characters, falling to $38 on the Scale plan
  • Cartesia does not state latency figures for this version
Full Sonic 3.6 profile

Eleven v3 vs Sonic 3.6 FAQ

Which is cheaper, Eleven v3 or Sonic 3.6?

Sonic 3.6 is 19% cheaper per character than Eleven v3, $0.000065 vs $0.00008.

Which scores higher on independent benchmarks, Eleven v3 or Sonic 3.6?

Sonic 3.6 leads on Artificial Analysis Speech Arena, 1286 Elo to 1177 Elo. Scores are independent evaluations, not either vendor's launch figures.

Production evaluation

Choosing between Eleven v3 and Sonic 3.6?

Public benchmarks narrow the shortlist. We run both on your own tasks, data and cost limits, then build the routing and review controls to run the winner in production.

  • A head-to-head evaluation on your real workload
  • Routing between models by cost and difficulty