Voice AI latency by stack configuration — 2026 benchmark
Production-grade 2026 voice AI stacks land between 600 ms and 1800 ms end-to-end turn-taking latency. Integration calls on the critical path are usually the largest contributor — not the model.
End-to-end turn-taking latency measured from end-of-turn detection to first audio frame returned. Sampled across approximately 25 production stacks in 2025–2026. Component figures show typical contributions, not vendor-specific claims.
| Stack component | Typical contribution | Notes |
|---|---|---|
| End-of-turn detection | 200–800 ms | Largest single component; semantic detection usually faster than pure silence |
| Speech-to-text (streaming) | 100–250 ms | Final commit latency; pre-streamed partials are faster |
| LLM first-token | 150–500 ms | Model and prompt-caching dependent; reasoning models add 500–1500 ms |
| Tool / integration calls | 100–1500 ms | Highly variable; often the actual bottleneck |
| Text-to-speech first-frame | 100–300 ms | Streaming TTS is essentially negligible after the first frame |
| SIP / carrier path | 20–150 ms | Codec and region dependent |
| Total typical end-to-end | 600–1800 ms | Above 2000 ms callers notice and disengage |
Caveats
- Numbers assume streaming throughout; non-streaming stacks routinely double these figures
- Integration latency varies most — a CRM call into an on-premise system can dominate the total
- Reasoning models (o-series, thinking models) trade 500–1500 ms of latency for higher accuracy on complex intents
- Measure on your own stack; vendor demos rarely reflect production tool-call latency
Frequently asked
What is a good voice AI latency target?
Under 1.5 seconds end-to-end is the practical production target for 2026. Under 1 second is achievable with disciplined integration design and streaming throughout the stack.
What is the largest contributor to voice AI latency?
Integration calls on the critical path, followed by end-of-turn detection. The LLM is rarely the bottleneck in production.
Should reasoning models be used in voice AI?
For complex intents where accuracy matters more than latency, yes — but route only the calls that need them, not the whole queue. Most production stacks use a fast default with reasoning escalation.