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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Loquent’s account of using vox-bench offers a useful lesson in voice AI latency: an average can hide the slow responses callers notice. But the post does not substantiate the title’s “14,000 benchmark runs” figure. Autor Technologies says it tracked latency across more than 10,000 calls over six months, and describes the first four months of monitoring as flawed. Its numbers are company-reported observations from a particular setup, not a universal benchmark or vendor guarantee.
What Loquent’s production experience actually showed
In a June 3, 2026 post, Autor Technologies Inc. described a Loquent voice-agent pipeline that ingested calls through Twilio media streams, used Deepgram for speech recognition, sent text to Anthropic Claude with OpenAI as a fallback, generated speech with ElevenLabs, and streamed audio back through Twilio. The company said it tracked more than 10,000 calls over six months. Its post does not report 14,000 benchmark runs.
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The first monitoring approach logged only inbound and outbound timestamps. That produced an average round trip near 650 ms, but did not reveal which stage was slow or how bad the slowest responses could get. During an ElevenLabs latency incident, the company reported that TTS p50 rose from 120 ms to p95 340 ms. The overall average moved from 650 ms to 710 ms, while 12% of calls reached 1.4 seconds or more. Autor Technologies said callers began talking over the agent and repeating themselves.
Those figures describe the company’s account of one production configuration. They should not be read as typical performance for Twilio, Deepgram, Claude, ElevenLabs, or voice agents generally. Read Autor Technologies’ account and description of vox-bench.
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Why an average is not enough
Latency is a distribution, not one number. The median, or p50, describes the point at which half of measured responses were faster and half slower. A p95 value is the point at which 95% were faster; p99 exposes still rarer delays. These percentiles answer different questions: p50 describes a typical turn, while higher percentiles help reveal the tail that can disrupt a conversation.
In its reported configuration, Loquent’s total pipeline had p50 latency of 620 ms and p99 of 1,150 ms. The gap matters: a system can feel responsive in most turns while still producing a meaningful share of noticeably slower ones. An average can blur that distinction, especially when a few long delays are mixed with many quick responses.
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Autor Technologies described under 800 ms as its own “feels natural” threshold, and said callers began repeating themselves above 1.2 seconds and hung up above 1.8 seconds. These are the company’s operational thresholds, not an industry standard. The practical test is whether your own callers can comfortably take turns under the conditions you serve.
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The company published component timings as well as its total pipeline results. Each value below is Autor Technologies’ report for the configuration described in its June 3, 2026 post; component measurements and end-to-end timings are not interchangeable.
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| Stage or measure | Reported latency |
|---|---|
| Deepgram Nova-3 speech recognition | p50 180 ms; p95 245 ms; p99 310 ms |
| Claude Haiku, time to first byte (TTFB) | p50 210 ms; p95 340 ms; p99 480 ms |
| Claude Sonnet, TTFB | p50 380 ms; p95 620 ms |
| ElevenLabs text-to-speech | p50 130 ms; p95 220 ms; p99 350 ms; p99 reportedly reached 600 ms at times |
| Total pipeline | p50 620 ms; p95 890 ms; p99 1,150 ms |
The same company said its streaming approach reduced total p50 from more than 1,100 ms to 620 ms. It also reported that healthcare-domain utterances benchmarked 15–20% slower on STT than general conversation in its tests, using a corpus of 200 healthcare utterances. These are configuration- and test-specific results, not a guarantee that streaming or a particular provider will produce the same improvement elsewhere.
How to benchmark a voice agent usefully
Measure each stage to diagnose bottlenecks, but also measure the response a caller experiences. A component timer can tell you about STT, model, or TTS behavior; it cannot by itself establish when the caller hears a usable answer.
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- Define the end-to-end boundary. State exactly when the clock starts and stops. OpenBenchmarks defines time to first agent byte from the end of caller speech to the start of the agent reply. Voxworks advises distinguishing acoustic turn end from the system’s accepted turn end, and proposes measuring the first stable, playable phrase rather than counting any initial audio byte. These boundaries yield different results, so name the one you use.
- Instrument the pipeline by stage. Record separate timestamps for speech recognition, model response, speech generation, and audio delivery. Keep first-byte measures distinct from complete-response measures; a quick first byte does not mean a caller has received a complete or playable answer.
- Report percentiles and the sample size. Include at least p50 and p95, and consider p90, p99, and maximum when diagnosing tail behavior. Give the number of calls and turns so readers can understand the scale behind the distribution.
- Use representative conversation patterns. Include realistic utterances, longer turns, pauses, interruptions, and recovery after interruption. Loquent’s account says its tool could simulate interruptions, pauses, and silence; a clean, short prompt alone will not describe a live conversation.
- Make the test reproducible. Record model and configuration versions, region, connection state, codec, network route, and recording point. Separate conversational turns from tool or API turns, clean turns from interruption and recovery turns, and warm connections from cold ones.
- Disclose exclusions. State how many turns were excluded and why. Quality-gated benchmark sets can be useful, but their results cannot be compared fairly without knowing what was removed.
OpenBenchmarks publishes turn-level artifacts and exclusion reasons, and its repository leaderboard is a separate dataset: it lists 2,078 usable turns across 550 calls from 2,158 measured, with the leaderboard marked generated on August 1, 2026. Do not merge those figures with Loquent’s production account. See the OpenBenchmarks leaderboard and methodology.
What vox-bench was designed to do
Autor Technologies described vox-bench as a TypeScript command-line tool for measuring STT, LLM, and TTS stages independently and in chained pipeline simulations. The post says it reports p50, p95, p99, and maximum latency; compares providers; can detect scheduled p95 regressions; and simulates conversation profiles with interruptions, pauses, and silence.
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The post describes a Node.js 20+ implementation with a provider-adapter layer, pipeline orchestrator, t-digest statistics engine, and JSON, Markdown, or webhook reporting. It says the project was released under the MIT license. The post’s description establishes what the authors said they built; it does not by itself verify the repository’s present status or feature availability.
How to interpret other voice-latency figures
Different datasets can provide context, but only when their measurement boundaries and populations are clear. Voxworks reports analyzing 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 TTFT of 293 ms, and median TTS first-byte time of 237 ms. Voxworks cautions that the corpus combines changing models, architectures, scripts, and instrumentation, so it is not an immutable product benchmark. Those figures should not be ranked directly against Loquent’s production measurements.
Voxworks also proposes “Time to First Playable Phrase” (TTFPP), with a quality gate, as a caller-focused metric. It is a proposal from Voxworks, not an established standard. The underlying point is practical: a system should not get credit for a fast audio byte if that byte is not yet a stable, understandable phrase.
For vendor or architecture comparisons, hold the test conditions steady and publish the route, configuration, boundary, and exclusions. Latency is only one dimension; streaming behavior, speech quality, content-domain variation, and the caller’s ability to take turns also affect whether a setup works well.
Quick Recap
What the evidence supports—and what it does not
- It supports the value of measuring per-stage latency and tail percentiles alongside an end-to-end caller-turn measure.
- It supports treating the reported Loquent figures as a specific company’s experience with a named pipeline and test corpus.
- It does not support the “14,000 benchmark runs” figure in the supplied title; Autor Technologies’ post instead says more than 10,000 calls over six months.
- It does not establish universal latency thresholds, independent vendor rankings, or guaranteed results from streaming.
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