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A voice agent feels responsive when the gap between the end of the user’s turn and the first sound of the reply is short. Closing that gap is a pipeline and transport problem as much as a model-speed problem. The practical levers are to start synthesis as soon as the first words of the reply exist, stream audio back before the full answer is finished, choose a connection type that suits the client, and keep the network path short and steady. A fast TTS model helps, but a model’s inference time on its own is not the latency your user hears.
Measure the interval the user actually hears
The word “latency” covers three different intervals, and vendor figures often measure only one of them.
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- Model inference time: how long the TTS model takes to generate audio on the provider’s side. It excludes network transport and everything that happens before synthesis begins.
- Time to first audio chunk: how long it takes your server to receive the first playable audio after it sends text to the TTS service.
- End-of-turn to first audible response: the interval from the end of the user’s speech to the first sound the user hears. This is the figure that determines whether the agent feels responsive, and it includes every stage in the pipeline.
ElevenLabs describes its voice-agent chain as speech recognition, an LLM, text-to-speech and playback, with each stage contributing latency (ElevenLabs, “Voice agent latency optimization: Techniques and methods”, published June 23, 2026 and updated September 6, 2026). Use the third interval as your target and treat the first two as component measurements.
What the published figures measure
The figures below are the ones most often quoted. Each is qualified by who measured it and what it covers. Model names, regions, pricing and rate limits change, so confirm them on the linked pages before you build.
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| Figure | Source and date | What it measures | Qualification |
|---|---|---|---|
| About 75 ms | ElevenLabs, “Latency optimization,” official documentation, retrieved 2026 (reducing-latency page) | Model inference for the Flash model | Vendor-stated as inference time only. Actual end-to-end latency varies with location and endpoint. |
| About 75 ms (Flash v2.5) | ElevenLabs, Text to Speech (TTS) API product page, undated | Flash v2.5 figure as listed on the product page | Vendor claim. The page does not establish a wider measurement scope. |
| About 250–300 ms (Turbo v2.5) | ElevenLabs product page, undated | Not stated on the page | Vendor claim. Whether the figure includes network transport or playback is not stated. |
| 947 ms P50; 729 ms best case, time to first audio | Authors of “Building Enterprise Realtime Voice Agents from Scratch: A Technical Tutorial,” arXiv preprint, March 2026 (arXiv:2603.05413) | Time to first audio in one cascaded voice pipeline built by the authors | One implementation in one setup. It is not a cross-vendor benchmark and not a general expectation. |
None of these numbers describes what your users will hear. The sources do not establish a shared test method across providers, so the figures cannot be ranked against each other.
Where the time goes in a turn
A voice-agent turn passes through several stages, and any of them can add delay. The sources do not provide a fixed budget for each stage, so use this list as a map of what to measure:
- End-of-speech detection (endpointing or turn detection), which decides when the user has finished talking.
- Speech recognition, up to the final transcript.
- Model reasoning and any tool calls.
- Text-to-speech startup.
- Media transport from server to client.
- Buffering on the client.
- Playback start.
A fast TTS model shortens only one of these stages. An agent with a quick voice still waits if endpointing is slow or if the LLM takes a long time to produce its first phrase.
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Start speaking before the answer is finished
Streaming is the main lever inside the speech-synthesis stage. Instead of waiting for the full reply, the system sends text to the TTS service and plays audio as it returns. Streaming can shorten time to first audio because playback begins while synthesis continues. It does not shorten the LLM’s own generation time, so the benefit depends on how soon the first usable text reaches TTS. ElevenLabs lists progressive streaming among its latency factors (ElevenLabs, “Latency optimization,” API guide).
Complete-response synthesis
The simplest pattern sends the full reply, waits for synthesis to finish and then plays the result. Playback cannot begin until the whole reply exists, so time to first sound grows with reply length. This pattern suits pre-recorded or non-interactive audio better than live conversation.
HTTP streaming when the full text is already known
If the complete text is available before synthesis starts, an HTTP streaming endpoint can return audio chunks as they are generated, and the client can begin playback on the first chunk. This fits prompts whose text is already finished, such as a scripted greeting or a sentence generated in full before it is spoken. It does not help when text is still arriving from an LLM.
Rank #3
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Bidirectional WebSocket when the LLM emits text incrementally
An LLM that streams tokens produces text over time. A bidirectional path lets that text keep flowing into the TTS service while audio flows back out at the same time. ElevenLabs documents WebSocket generation for real-time text input (ElevenLabs, “Latency optimization”). Amazon Polly documents a bidirectional streaming lifecycle in which text is sent and audio received concurrently (Amazon Web Services, “Sending text and receiving audio – Amazon Polly”).
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Chunk size: earlier audio against natural speech
Smaller or earlier chunks reduce the initial wait, but synthesis that starts before a full phrase is available can sound less natural. Amazon’s guidance recommends buffering text to natural boundaries when possible and forcing synthesis when latency requires it. ElevenLabs likewise notes that smaller chunks may help response time but need evaluation for prosody and perceived quality. Tune chunk size against three things at once: time to first audio, speech naturalness, and errors at chunk boundaries.
Choosing the transport
Transport choice covers two links: the connection between the client and your system, and the path that text and audio take through the provider. Two questions settle most cases. Is the client a browser or phone app, or is the audio handled entirely on your server? Is the text complete before synthesis begins, or does it arrive over time?
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| Option | Where it fits | What the sources document | Main trade-off |
|---|---|---|---|
| WebRTC session | Browser or mobile clients connecting to a realtime voice service | OpenAI’s WebRTC guide recommends WebRTC for client-side Realtime connections for more consistent performance. Microphone audio is sent in and generated speech comes back on media tracks. | Depends on low, stable media round-trip time. The client must handle WebRTC session setup and media. |
| Bidirectional WebSocket TTS | Server-side pipelines that send text as the LLM produces it | ElevenLabs documents WebSocket generation for real-time text input. Amazon Polly documents concurrent text sending and audio receiving. | Your code owns chunking, buffering and error handling at chunk boundaries. |
| HTTP streaming TTS | Cases where the full text is known before synthesis | Audio chunks are returned as they are generated. | Playback cannot start until the text is complete, so it does not suit text still arriving from an LLM. |
WebRTC for browser and mobile clients
WebRTC is an open standard for low-latency media and data. In an engineering write-up on OpenAI’s low-latency voice AI, Yi Zhang and William McDonald, Members of Technical Staff at OpenAI, treat low and stable media round-trip time as a core requirement (OpenAI, “How OpenAI delivers low-latency voice AI at scale,” May 4, 2026). For client-side sessions, the OpenAI WebRTC guide is the reference to follow for session setup.
WebSocket for server-side text-to-audio
When your backend already holds the LLM output and needs audio returned, a WebSocket TTS connection keeps one channel open in both directions. The main engineering cost sits in your own code: you decide when to flush text, how to buffer it, and how to recover when a chunk fails.
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The OpenAI Realtime conversations guide documents speech-to-speech conversations that skip a separate intermediate TTS or STT step, and it presents that as a source of lower latency. A cascaded design, with speech recognition, an LLM and TTS as separate stages, lets you choose each component and log the text between stages. The documentation does not establish that the native path is faster for every workload, so test both designs against the same scripted conversations before committing.
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Network geography and connection setup
Network effects can rival model effects in a live call. Check each of these separately:
- Geography: ElevenLabs lists geographic proximity to its service as a latency factor. Measure from the regions where your users are, not only from your office or from one cloud region.
- Connection setup: how long it takes to establish the session before the first turn. Measure a cold start separately from steady-state turns.
- Round-trip stability: a low average can hide spikes. Spikes produce the awkward pauses users notice, so track the spread of round-trip times, not only the mean.
- Jitter and packet loss: these can make audio choppy or delayed even when the model responds quickly.
- Voice choice: ElevenLabs also lists voice selection among its latency factors.
Trade-offs to test before you ship
- Naturalness and prosody: faster models and smaller chunks can cut the wait but change pacing and emphasis. ElevenLabs frames its Flash model as faster with a quality trade-off. Evaluate on your own scripts and voices.
- Chunk boundaries: listen for pauses, clipped words or tonal shifts where one chunk meets the next.
- Barge-in: when the user interrupts, playback must stop and in-flight synthesis must be cancelled. Test this path separately, because a fast first sound does not show that interruption works.
- Voice consistency: confirm that the same voice sounds the same across streamed chunks and across sessions.
How to benchmark the whole turn
- Timestamp the end of user speech at the moment your endpointing logic decides the turn is over.
- Timestamp the final transcript from speech recognition.
- Timestamp the first text token from the LLM and the first text sent to TTS.
- Timestamp the first audio chunk received from the TTS service on your server.
- Timestamp the first audible sample on the client, using the client’s playback-start event rather than the time the chunk arrived.
Time to first audible response is the timestamp from step 5 minus the timestamp from step 1. Run the measurement from each deployment region and each client type you support, under concurrent load that resembles production. Report the median and the tail, such as the 95th or 99th percentile, because a few slow turns are what users remember. Keep the per-stage timestamps so that a slow result points to a specific stage rather than to the whole system.
When first audio is slow: where to look
- Long silence before anything starts: check endpointing and the wait for the final transcript (steps 1 and 2 above).
- Fast TTS but a slow start: check whether text goes to TTS as soon as the LLM produces its first phrase, or whether the code waits for the full reply.
- Good server timings but slow client audio: check connection setup, round-trip time, client buffering and playback start.
- Audio starts quickly but sounds uneven: check chunk size and how text is buffered at phrase boundaries.
- Good results in one region only: test from the other regions where users connect.
The Amazon Polly and OpenAI documentation cited above describes capabilities rather than a controlled comparison, and the sources do not rank these providers. Choose on the measurements from your own deployment.
The Bottom Line
Start by instrumenting the full turn, from end of user speech to first audible sound. If the timings show that the TTS call waits for the full reply, change that first. If the slow stage is the client connection, revisit the transport.
Quick Recap
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