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Voice AI latency is best measured as a distribution across the full conversation pipeline—not as one average. In a June 3, 2026 account, Autor Technologies said its Loquent system tracked more than 10,000 calls over six months, then found that its first four months of monitoring missed important tail delays. The company’s experience shows why teams should measure each stage, report percentiles, and define exactly when the clock starts and stops. The account is self-reported; its figures describe that configuration, not universal service performance.
What Loquent’s production monitoring missed
Autor Technologies described a five-stage voice pipeline: Twilio media-stream ingestion, Deepgram speech recognition, Anthropic Claude inference with OpenAI as a fallback, ElevenLabs text-to-speech, and audio streamed back through Twilio. Its initial monitoring recorded only inbound and outbound timestamps. That produced an average round trip near 650 ms, but did not reveal where slow responses occurred or how often callers experienced them.
The company later reported an ElevenLabs latency incident in which p50 rose from 120 ms to p95 of 340 ms. Over the same incident, the total average moved from 650 ms to 710 ms, while 12% of calls reached 1.4 seconds or more. Autor said the delays contributed to callers talking over the agent and repeating themselves. These are the company’s observations and causal account, not an independently audited finding. Autor Technologies’ June 3, 2026 article describes the incident and its monitoring changes.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11The lesson is practical: an average can remain relatively stable while a meaningful share of conversations becomes noticeably slower. In the reported configuration, total pipeline latency was p50 620 ms and p99 1,150 ms. Those percentiles describe different parts of the distribution; neither one can stand in for the other.
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How to measure voice AI latency
Define the timer boundary
Before comparing runs, state what starts and stops the timer. A component test might measure a speech-recognition request or a model’s time to first token. A caller-facing turn metric might start when the caller finishes speaking and stop when the agent begins replying. Those measurements answer different questions.
OpenBenchmarks defines time to first agent byte (TTFAB) from the end of caller speech to the start of the agent reply, and publishes recordings, artifacts, and exclusion reasons. Voxworks recommends distinguishing acoustic turn end from the system’s accepted turn end, then measuring the first stable, playable phrase rather than any initial audio byte. Voxworks calls its proposed quality-gated measure Time to First Playable Phrase (TTFPP); it is a proposal, not an adopted industry standard. See the OpenBenchmarks methodology and leaderboard and Voxworks’ discussion of caller-heard latency.
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Measure stages and the complete chain
Track speech-to-text (STT), language-model (LLM), and text-to-speech (TTS) timing independently, then run chained tests that include the pipeline’s real handoffs and streaming behavior. Stage timings help locate a bottleneck; an end-to-end turn measures what the caller waits through. Do not add isolated component numbers and present the sum as a production result unless the test actually ran those components in that sequence under the same conditions.
Autor described vox-bench as a TypeScript command-line tool for independent STT, LLM, and TTS tests as well as chained pipeline simulations. The article says it reports p50, p95, p99, and maximum; compares providers; detects scheduled p95 regressions; and simulates conversation profiles with interruptions, pauses, and silence. It describes a Node.js 20+ implementation with provider adapters, a pipeline orchestrator, a t-digest statistics engine, and JSON, Markdown, or webhook reporting. The article says the project was MIT licensed and links to a vox-bench repository; current repository status and feature availability are not established by that account.
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Report percentiles, not only a mean
For a set of turns, p50 is the median: half the measured turns are at or below it. Higher percentiles expose slower portions of the distribution, while maximum shows the slowest observed case and is especially sensitive to sample size. Autor’s report presents p50, p95, p99, and maximum as useful outputs. Voxworks recommends also reporting p75 and p90. A mean can be included, but it should not replace these distribution measures.
Keep the population and measurement point attached to every reported number. Include the number of calls and turns, the share and reasons for excluded turns, and whether turns were conversational or involved tools or APIs. Distinguish clean turns from interruption and recovery turns, and warm from cold connections. Record the recording point, codec, network route, model, region, and configuration versions. Without that context, a percentile is difficult to reproduce or interpret.
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What the reported Loquent figures show
The values below are Autor Technologies/Loquent’s figures for the configuration described in its June 3, 2026 article. They are not cross-vendor rankings or guarantees, and component measures such as LLM TTFB should not be mistaken for full conversational turn latency.
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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →| Measure in the reported configuration | p50 | p95 | p99 | Other reported detail |
|---|---|---|---|---|
| Deepgram Nova-3 STT | 180 ms | 245 ms | 310 ms | |
| Claude Haiku LLM time to first byte | 210 ms | 340 ms | 480 ms | |
| Claude Sonnet LLM time to first byte | 380 ms | 620 ms | ||
| ElevenLabs TTS | 130 ms | 220 ms | 350 ms | p99 reportedly reached 600 ms at times |
| Total pipeline | 620 ms | 890 ms | 1,150 ms |
Autor also said its tests used a corpus of 200 healthcare-domain utterances and found those utterances benchmarked 15–20% slower on STT than general conversation. That is a result from the company’s own tests, not a general multiplier for healthcare speech. The content being recognized can affect results, so a benchmark corpus should reflect the language, terminology, and turn patterns a particular agent actually handles.
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The same article describes the company’s operational thresholds as under 800 ms for a response that “feels natural,” callers beginning to repeat themselves above 1.2 seconds, and hang-ups above 1.8 seconds. Treat these as Loquent’s working thresholds, not established industry standards. It also reports that streaming reduced its p50 total from 1,100 ms or more to 620 ms. That result is specific to the company’s pipeline and setup; streaming behavior and quality need to be assessed together.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to build a representative benchmark
- Choose a caller-facing metric and state its boundaries. Specify whether timing begins at acoustic speech end, accepted turn end, or another event, and whether it stops at first byte, first audio, or a stable playable phrase.
- Test each component and the real pipeline. Run STT, LLM, and TTS checks separately to find bottlenecks, then test the chained system with its actual streaming and fallback paths.
- Use representative conversation patterns. Include ordinary turns, longer utterances, pauses, silence, interruptions, and recovery after an interruption. A clean single-utterance test cannot characterize every caller interaction.
- Record configuration and context. Save model and provider versions, region, connection state, codec, route, recording point, and whether a turn used a tool or API. Preserve the audio or other artifacts where appropriate and document exclusions.
- Run enough turns to inspect the tail. Report sample counts alongside p50, p75, p90, p95, p99, and maximum. Percentiles from a small or selectively filtered set can give a misleading picture of slow cases.
- Repeat and compare like with like. Schedule recurring checks for regressions, but compare results only when the metric boundary, corpus, route, and configuration are materially consistent. A changed model or instrumentation may change the result even if the system feels no faster or slower.
- Evaluate quality alongside speed. Confirm that the first audio is intelligible and useful. Voxworks warns that starting text streaming very early can reduce the context available to TTS for prosody; a faster first byte alone does not establish a better caller experience.
Why results from other benchmarks are not directly comparable
Other published datasets illustrate how much measurement setup matters. OpenBenchmarks’ repository leaderboard, marked generated August 1, 2026, lists 2,078 usable turns across 550 calls from 2,158 measured and says exclusions were quality-gated. Its dataset is separate from Loquent’s, so those results should not be combined into a single performance figure.
Voxworks reports analysis of 102,201 selected latency rows in an evolving staging corpus, with an approximate median total response latency of 1.645 seconds, p90 of 3.115 seconds, median LLM time to first token of 293 ms, and median TTS first-byte time of 237 ms. Voxworks cautions that the corpus spans changing models, architectures, scripts, and instrumentation. These figures therefore are not an immutable product benchmark or a direct comparison with Loquent’s production results.
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When evaluating any two approaches, align the timer boundary, turn population, exclusions, network route, model and region, streaming behavior, and quality criteria. If those differ, describe the results as separate observations rather than declaring a winner.
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