The Fastest Text-to-Speech API.
Ranked Among the World’s Most Natural.
Compare Murf Falcon 2 with leading TTS APIs using real-world benchmarks. Explore end-to-end
latency, global consistency, naturalness, and cost to evaluate performance under real production
conditions.
Real-time Text-to-speech runs on trade-offs. A model has to sound human, respond in real time, and stay cheap enough to run at scale. Push on one and the others give. Naturalness often costs latency. Low latency is not always consistent at scale. And achieving both can make inference expensive. Delivering naturalness, speed, and cost efficiency together takes more than tuning; it requires a different architecture, data pipeline, and inference strategy.
Falcon 2 is built for that.
Compute-efficient architecture - A proprietary neural model that outperforms far larger systems in context awareness, while delivering the speed benefits of a smaller model.
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Edge-level deployment - Inference close to the user, so network variability stays out of the latency budget.
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Dynamic infrastructure selection - The most cost-efficient GPUs available in each region, chosen per request, for performance and cost together.
1. Latency
This study measures time-to-first-audio (TTFA) for Falcon 2 and leading TTS APIs across global regions, to test raw speed and consistency under network variability. TTFA is the delay between a synthesis request and the first audio frame. We report TTFA because it is a better measure of real-world responsiveness: it reflects what customers experience and, unlike model latency, can be independently verified.
Using apiping.io, a neutral geo-distributed API relay, over 1,500 identical streaming requests were triggered for Falcon 2, ElevenLabs Flash v2.5, Cartesia Sonic 3.5, Deepgram Aura 2, and Sarvam Bulbul V3. All models were tested from 32 global edge locations, except Sarvam Bulbul V3, which was tested from India only.The relay recorded DNS resolution, connection setup, TLS handshake, TTFA, and total response time. Median TTFA was calculated for each region because median better represents the response time experienced on a typical request.
Voice agents serve users everywhere, so latency has to hold across regions, not just at one test point. Across regions, Falcon 2 returns first audio in under 100ms, from 61ms to 97ms. It ranks first in most regions tested, ahead of ElevenLabs, Cartesia, Deepgram, and Sarvam.
Low latency only counts if it is consistent. To assess both latency and consistency, we examined the distribution of latency across repeated requests rather than relying on a single number. Results are presented as box plots: lower medians indicate faster typical responses, while tighter boxes and shorter whiskers indicate more consistent performance.
Falcon 2 recorded the tightest central distribution among the models tested. This indicates that, for the large majority of requests, Falcon 2 was not only faster but also more consistently responsive.
2. Naturalness
2.1 Study Overview
Naturalness scores are based on the Artificial Analysis Speech Arena, an independent benchmark that ranks TTS models through blind, head-to-head listener preference on an Elo scale. We use its results to compare Falcon 2 with comparable real-time models on a neutral, independently verifiable basis.
2.2 Methodology
Listeners compare paired speech samples and select the output they prefer. Artificial Analysis aggregates these crowdsourced preferences into a relative Quality Elo score using a linear-regression model. Methodology and benchmark results are credited to Artificial Analysis.
2.3 Results and Analysis
On the Artificial Analysis Speech Arena, Falcon 2 ranks among the world’s top real-time models. It scores above the real-time voices from ElevenLabs and OpenAI.
3. Efficiency Quadrants
Most TTS models win on one axis: naturalness, latency, or cost. Voice agents need all three. Using the benchmark data above, with naturalness taken from Artificial Analysis Elo scores, we plotted efficiency quadrants to separate the models that deliver across the board from the ones that force a trade-off.
3.1 Naturalness vs. Price
Plotting Artificial Analysis Elo naturalness against cost per generated minute, Falcon 2 sits in the efficient zone: top-tier naturalness at 1 cent a minute, roughly a third the cost of comparable models.
3.2 Latency vs. Naturalness
Plotting TTFA against Elo naturalness, Falcon 2 again lands in the efficient quadrant: sub-100ms latency with top-tier naturalness. Other models trade one for the other.
This is why Falcon 2 is the fastest, most efficient text-to-speech API in production: sub-100ms time-to-first-audio, naturalness ranked above the real-time voices from ElevenLabs and OpenAI on the independent Artificial Analysis Speech Arena, and consistency proven across regions, all at 1 cent a minute.
Ready to Build with Falcon 2 ?
Falcon 2 brings more natural speech, sub-100ms time-to-first-audio, enterprise voice
cloning, and 1 cent per minute pricing to production voice agents.
