Voice AI Latency Calculator
Compare latency across different STT, LLM, and TTS provider combinations. Find the optimal stack for fast, natural conversations.
Skip separate STT/TTS for lowest latency
Process in parallel for faster response
Speech-to-Text (STT)
Converts spoken audio into text
Language Model (LLM)
Generates intelligent responses
Text-to-Speech (TTS)
Converts text into natural speech
50ms
Additional latency from your infrastructure, CDN, and geographic distance to providers
Estimated Latency
Natural, conversational feel
Latency Breakdown
Optimization Tips
This configuration provides excellent latency for natural conversations
Quick Comparison: Popular Configurations
| Configuration | Est. Latency | Quality | Cost |
|---|---|---|---|
| Gemini 2.0 Flash (Native Audio) | ~300ms | High | $$ |
| Deepgram + Groq + Deepgram Aura | ~350ms | Good | $ |
| Deepgram + GPT-4o-mini + ElevenLabs | ~550ms | High | $$ |
| Whisper + GPT-4o + ElevenLabs | ~1200ms | Excellent | $$$ |
Understanding Voice AI Latency
How different latency levels affect user experience
Feels like natural conversation
Acceptable, slight delay noticeable
Noticeable lag, still usable
Feels sluggish, affects UX
The Voice AI Pipeline
Three stages contribute to total response latency
1. Speech-to-Text (STT)
100-800msConverts spoken words into text that the LLM can process
Popular: Deepgram, Whisper, AssemblyAI, Google STT
2. Language Model (LLM)
80-800msProcesses the text and generates an intelligent response
Popular: GPT-4o, Claude, Gemini, Groq
3. Text-to-Speech (TTS)
80-400msConverts the text response back into natural speech
Popular: ElevenLabs, PlayHT, Deepgram Aura
Latency Optimization Tips
How to achieve the lowest possible latency
Use Streaming
Enable streaming to process STT, LLM, and TTS in parallel rather than sequentially
Consider Native Audio Models
Gemini 2.0 Flash and GPT-4o Realtime skip separate STT/TTS for lowest latency
Choose Regional Providers
Select providers with data centers close to your users to minimize network latency
Balance Quality vs Speed
Smaller, faster models (GPT-4o-mini, Claude Haiku) can be nearly as good for many tasks
Related Tools
More tools to help you evaluate voice AI
Frequently Asked Questions
Why does voice AI latency matter?
Latency directly impacts conversation quality. Delays over 800ms make conversations feel unnatural, leading to users talking over the AI or abandoning calls. For customer service and sales calls, low latency is critical for maintaining engagement and trust.
What's the difference between sequential and streaming processing?
Sequential processing waits for each stage (STT → LLM → TTS) to complete before starting the next. Streaming allows overlap - the LLM starts processing while STT is still transcribing, and TTS starts speaking while the LLM is still generating. This can reduce total latency by 30-50%.
Are native audio models always better?
Native audio models (Gemini 2.0 Flash, GPT-4o Realtime) offer the lowest latency but have trade-offs: fewer voice options, less control over individual components, and potentially higher costs. They're ideal when latency is the top priority.
How accurate are these latency estimates?
These are typical latencies based on published benchmarks and real-world testing. Actual latency varies based on: input length, network conditions, server load, geographic location, and specific model configurations. Use these as relative comparisons rather than absolute values.
What latency should I target for my use case?
For real-time conversations (customer support, sales): aim for under 500ms. For less interactive use cases (IVR, outbound notifications): 500-800ms is acceptable. For non-conversational voice (dictation, commands): up to 1000ms can work.
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