Latencykey-up latency
In dictation, latency is the time between the end of your speech and finished text appearing at the cursor. It is the number that decides whether a tool feels instant or feels like waiting.
Measure it from the moment you release the key, because that is the moment you are waiting from. Everything that happens after key-up counts: finishing recognition of the last few hundred milliseconds of audio, the formatting pass, and the insertion into the app. A tool that streams recognition has only the tail to finish; a tool that batches has the whole utterance still to process, so its latency grows with every second you spoke.
Under about a second, most people stop noticing. Around two or three seconds, they start glancing at the screen and then at the tool. Beyond that, dictation stops being a way to write and becomes a way to make a recording you then wait for.
Where the time goes
Recognition tail: how much audio remains to be processed at key-up, which streaming reduces to almost nothing. Formatting: a language-model pass over the transcript, typically the largest single cost in a local tool, and the reason a fast tool keeps that model small and gives it a strict time budget. Insertion: typing the result into the focused app through the accessibility layer, usually tens of milliseconds. Network, for cloud tools: a round trip plus queueing on a server you do not control, which is why cloud latency varies from fast to unusable depending on the hour.
Latency versus quality is a real trade
A formatting pass costs time, and skipping it is the fastest possible tool. It is also a tool that hands you a transcript to fix by hand, which costs more time than it saved. The right design offers the choice: a verbatim or lightly cleaned mode that lands almost immediately, and a fully formatted mode that takes a little longer and needs no fixing. What a tool should never do is let a slow model hold the text hostage; if the pass runs late, the plain transcript should land anyway.
In Flit
Flit lands finished, formatted text about 0.7 seconds after key-up, or about 0.15 seconds with formatting set to Clean, and a late model never delays the transcript.
How the 4× is measured →Questions
Fair questions.
What is a good latency for dictation?
Under a second from key-up to finished text feels instant. One to two seconds is acceptable. Beyond that, the wait becomes the experience. Streaming recognition and a small, time-boxed formatting model are how local tools get under a second.
Why does my dictation tool get slower the longer I talk?
Because it recognizes in batch: nothing is processed until you stop, so a longer recording means a longer wait. Streaming tools process as you speak and keep the delay constant.
Is cloud dictation slower than local?
Often, and less predictably. A round trip to a server adds time on every dictation and varies with your connection and their load. Local tools have a fixed cost that depends only on your Mac.
Where this shows up on flit.fyi
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