Elise AI
We didn't switch to Sonic 3.5 because it was incrementally better, we switched because nothing else came close… we've seen a 2.9% lift in our conversion and a 12.2% increase in customer engagement.
Updated February 14, 2025
Explore the differences between ElevenLabs and Hume AI voice models. Compare features, pricing, and performance.
Eleven Labs offers highly realistic voices with emotional range but requires more computing power. Hume AI focuses on emotional intelligence and natural prosody but has fewer voice options.
Cartesia's Sonic model achieves a remarkable 40ms time-to-first-audio, ensuring rapid voice responses.
With just 3 seconds of audio, Cartesia can create high-fidelity voice clones that sound lifelike and authentic.
Cartesia's voices are rated #1 in quality, providing natural and expressive speech for various applications.
Enterprise-grade reliability with 99.9% uptime, SOC2 compliance, and full on-premises support.
When evaluating voice quality between ElevenLabs and Hume AI, we focused on metrics like speech naturalness, pronunciation accuracy, and noise levels. ElevenLabs excelled with a speech naturalness score of 89.60%, while Hume AI scored 78.50%. In terms of pronunciation accuracy, ElevenLabs achieved 87.13%, outperforming Hume AI's 80%. Additionally, ElevenLabs demonstrated minimal noise, with 92.29% of outputs rated as having no detectable noise, compared to Hume AI's 85%. These results indicate that ElevenLabs provides a more natural and clear voice quality, making it a preferred choice for applications requiring high fidelity.
In our latency evaluation, we measured the Time to First Audio (TTFA) for both ElevenLabs and Hume AI. We conducted 100 TTFA measurements for each provider and calculated the 90th percentile score. ElevenLabs showcased a remarkable TTFA of 120ms, indicating its ability to deliver audio quickly. Hume AI, while competitive, recorded a TTFA of 150ms. This evaluation highlights ElevenLabs' advantage in low-latency performance, making it suitable for real-time applications where immediate audio feedback is crucial.
To assess the hallucination rate of ElevenLabs and Hume AI, we analyzed the frequency of incorrect or nonsensical outputs during voice generation. ElevenLabs reported a hallucination rate of 5%, indicating that 5% of generated outputs contained inaccuracies. In comparison, Hume AI exhibited a higher rate of 8%. This evaluation underscores ElevenLabs' strength in maintaining accuracy and coherence in generated speech, making it a more reliable choice for applications that demand high fidelity and correctness in voice outputs.
In our evaluation of voice cloning capabilities, we compared ElevenLabs and Hume AI using key metrics such as Word Error Rate (WER) and speech naturalness. ElevenLabs achieved an impressive WER of 2.83%, indicating high accuracy in reproducing text as speech. In contrast, Hume AI's performance was slightly lower, showcasing a WER of 3.5%. When it comes to speech naturalness, ElevenLabs scored high in 44.98% of cases, while Hume AI was rated high in 40% of instances. This evaluation highlights ElevenLabs' edge in producing lifelike and accurate voice clones, making it a strong contender in the voice AI landscape.
In our evaluation of voice design controllability, we examined how well ElevenLabs and Hume AI allow users to customize voice attributes such as pitch, tone, and speed. ElevenLabs scored highly with 85% of users reporting satisfaction with the customization options available, while Hume AI received a score of 75%. Additionally, ElevenLabs demonstrated superior context awareness, adapting voice characteristics effectively in 63.37% of cases compared to Hume AI's 55%. This evaluation highlights ElevenLabs' robust capabilities in voice design, providing users with greater flexibility and control over voice outputs.
Elise AI
We didn't switch to Sonic 3.5 because it was incrementally better, we switched because nothing else came close… we've seen a 2.9% lift in our conversion and a 12.2% increase in customer engagement.
ServiceNow
Cartesia's state-space models bring enterprise-grade speed and quality to our AI Voice Agents… making it possible for businesses to deploy secure, scalable voice agents that can understand, act, and adapt in real time.
Sierra
Cartesia Sonic 3.5 has become one of the top-performing models for us by combining low latency with natural pacing… helping us deliver strong voice quality across a growing set of languages where other models often fall short.
Callers
Sonic 3.5 has been a meaningful upgrade for Callers… latency and naturalness directly impact conversational flow and user success, and the new model noticeably improves both. We've seen more human interactions — especially in high-volume customer conversations where every millisecond and every turn matters.
Take2 AI
We moved from an incumbent TTS provider to Cartesia because of the support experience. After repeated roadblocks with our previous provider, the difference with Cartesia has been transformative — responsive, technical, and genuinely invested in our success.
Cresta
Sonic 3.5 represents a significant evolution over previous TTS models, delivering refined prosodic rhythm, natural intonation, superior pacing and wider emotional range for more “human” sounding voices.
Bolna
Indian voice agents live or die on whether order IDs, alphanumerics, and multilingual code-switching come out right on a phone line. Sonic 3.5 handles alphanumerics natively… and lands first audio at 100ms p90.
Goodcall
Sonic is the only product in existence with model latency of less than 100 ms, outperforming its next best alternative by a factor of four. This level of performance represents a quantum leap forward.
Quora
Sonic powers audio on Poe across 100+ voices and 14 languages, supporting Quora's millions of users with SOC 2 compliance and unlimited concurrency for enterprise customers.
Fundamento
We run 20M+ outbound calls per month on Cartesia, with peak concurrency of 5,000 calls in a single minute, and 100ms time-to-first-byte — 2x faster than every other voice provider we tested.
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