Voice AI Showdown: VAPI vs Synthflow vs Retell - Which Has the Lowest Latency?
Most businesses deploying voice AI struggle with robotic delays that ruin customer experience. We built identical agents on three leading platforms to measure real latency differences. Discover which solution delivered sub-600ms response times and learn expert techniques to optimize your voice AI performance.
Understanding Voice AI Latency
Voice AI latency - the delay between when you speak and when you hear a response - makes or breaks user experience. While humans naturally converse with 200-400ms gaps between speakers, achieving this with AI requires overcoming five technical hurdles:
First, your voice must travel to the speech-to-text model (initial transport). The transcriber then converts your words to text, which gets sent to the reasoning model (typically an LLM like GPT-4). The LLM's text output goes to a voice engine (like ElevenLabs) before the audio finally returns to your device.
Latency sweet spot: Below 600ms feels nearly real-time, 600-900ms is noticeable but acceptable, while anything over 1.2 seconds becomes frustrating. Most voice agents currently operate in the 600-900ms range.
Test Methodology
To compare platforms fairly, we standardized all variables possible: ElevenLabs Turbo 2.5 for voice, GPT-4.1 for reasoning, and DeepGram Flux for transcription. We ran three test types (simple repetition, math problems, and factual questions) five times each to calculate average latency.
This approach isolates performance differences attributable to the platforms themselves rather than model choices. We measured both total round-trip latency and breakdowns by component (transcription, LLM, voice generation) where available.
Retell AI Performance
Retell delivered respectable performance with an average 714ms total latency. The platform's standout feature was blazing-fast transcription at just 30ms using DeepGram Flux. However, the LLM reasoning time dominated at 380ms - nearly half the total latency.
During testing, responses felt slightly delayed but not unnatural. The platform provided detailed latency breakdowns, making it easy to identify the LLM as the primary bottleneck. This transparency is valuable for optimization efforts.
VAPI Performance
VAPI emerged as the clear winner with an impressive 539ms average latency. While its transcription was slower than Retell's (118ms), VAPI's optimized LLM processing at just 161ms made the difference. The platform also provided the most granular latency analytics.
Notably, VAPI maintained consistent performance across test types, suggesting robust infrastructure. Conversations felt nearly real-time, with only slight pauses noticeable during complex queries. The platform also offers advanced optimization options for technical users.
Synthflow Performance
Synthflow struggled in our tests with approximately 2-second latency - well above the acceptable threshold. The platform lacked built-in latency analytics, requiring manual audio analysis to measure delays. Response times varied significantly between tests.
While Synthflow may excel in other areas, latency appears to be a current weakness. The delays were noticeable enough to disrupt conversation flow, making it less suitable for real-time applications where quick responses matter.
Optimization Techniques
For VAPI (our top performer), we experimented with several optimizations. Switching to GPT-4 real-time models reduced LLM latency to near-zero, but endpointing delays offset gains. The optimal balance came from using GPT-4O mini with VAPI's native voices, maintaining sub-600ms latency.
Key lessons: transcription model choice impacts latency significantly (DeepGram Nova for phone calls outperformed Flux), and voice model changes often simply shift latency between components rather than reducing it overall. True sub-500ms performance requires API-level customizations.
Watch the Full Tutorial
See the latency differences in action - at 3:45 in the video we demonstrate the noticeable gap between VAPI's 539ms response and Synthflow's 2-second delay. The side-by-side comparison reveals why latency matters for user experience.
Key Takeaways
Our testing revealed significant latency differences between platforms that directly impact user experience. VAPI's 539ms average response time sets the current benchmark, with Retell being a respectable alternative at 714ms. Synthflow's 2-second latency makes it unsuitable for real-time applications.
In summary: For latency-sensitive voice AI applications, VAPI currently delivers the best performance out-of-the-box, while Retell offers better transcription speeds. Optimization potential exists on both platforms, but achieving sub-500ms consistently requires advanced technical implementation.
Frequently Asked Questions
Common questions about voice AI latency
Human conversation typically has 200-400ms gaps between speakers. Voice AI agents currently average 600-900ms latency.
Below 600ms is excellent and feels nearly real-time, 600-900ms is noticeable but acceptable, while anything over 1.2 seconds becomes problematic for natural conversations. The ideal target depends on your specific use case and user expectations.
- Excellent: Below 600ms
- Acceptable: 600-900ms
- Problematic: Over 1.2 seconds
VAPI achieved the lowest average latency at 539ms, making it our top performer. Retell came in second at 714ms, while Synthflow was significantly slower at approximately 2 seconds response time.
VAPI's advantage came from optimized LLM processing at just 161ms, despite slightly slower transcription than Retell. The platform also provided the most detailed latency analytics, helping identify optimization opportunities.
- VAPI: 539ms average
- Retell: 714ms average
- Synthflow: ~2000ms
Voice AI latency has five key components that add up to the total response time you experience. Understanding these helps identify optimization opportunities.
The chain starts with initial transport (voice to STT model), followed by speech-to-text transcription. The text then goes to the LLM for reasoning, gets converted to speech, and finally travels back to your device. The LLM typically contributes the most to total latency.
- Initial audio transport
- Speech-to-text transcription
- LLM reasoning time
- Text-to-speech conversion
- Final audio transport
Yes, with advanced optimizations some platforms can achieve sub-500ms latency. However, this typically requires technical expertise beyond standard configurations.
Techniques include using real-time model clusters, optimized transcription models like DeepGram Nova, voice caching, and API-level customizations. The tradeoff is often increased complexity and potentially higher costs.
- Real-time model clusters
- Optimized transcription models
- Voice response caching
- API-level customizations
Not necessarily. While lower latency improves conversation flow, some optimizations that reduce latency may impact response quality.
Simpler models or cached responses can reduce latency but may produce less nuanced answers. The ideal balance depends on your specific use case - sales calls may prioritize quality while simple Q&A can favor speed.
- Latency affects conversation flow
- Quality affects response accuracy
- Balance depends on use case
DeepGram Flux provided the fastest transcription times in our standardized testing. Retell achieved just 30ms transcription latency using this model.
Interestingly, VAPI's transcription was slower at 118ms despite using the same DeepGram Flux model, suggesting platform overhead affects performance. For phone-specific applications, DeepGram Nova may offer better optimized performance.
- DeepGram Flux fastest overall
- Retell: 30ms transcription
- VAPI: 118ms with same model
The LLM typically contributes 40-60% of total latency, making it the most significant factor. Platform optimizations can dramatically impact LLM response times.
In our tests, GPT-4.1 averaged 380ms on Retell but just 161ms on VAPI with the same model. This shows how platform-level optimizations can more than halve LLM latency without changing the underlying model.
- 40-60% of total latency
- Retell: 380ms
- VAPI: 161ms (same model)
GrowwStacks specializes in building optimized voice AI solutions that balance latency, cost and quality for business applications.
We'll analyze your specific requirements, select the ideal platform (VAPI, Retell or custom), implement performance optimizations, and handle ongoing maintenance. Our team has deep expertise in achieving sub-600ms latency while maintaining response quality.
- Platform selection guidance
- Latency optimization
- Ongoing maintenance
- Free initial consultation
Ready to Implement Low-Latency Voice AI?
Every second of delay costs you customer satisfaction and conversion rates. Let GrowwStacks build a voice AI solution with sub-600ms response times tailored to your business needs.