On August 26, Google released Gemini 3.5 Transcribe. This is not another version bump of the general-purpose Gemini line — it is a model built for exactly one job, speech-to-text, and it formally replaces Google's previous transcription model, Chirp 3.
The Numbers First
According to Google's official blog, on a set of top languages and locales on the FLEURS benchmark, Gemini 3.5 Transcribe achieves a 5.50% word error rate (WER) in streaming mode and 5.04% in non-streaming mode, improving over Chirp 3. Another set of average figures from Artificial Analysis reads 4.0% streaming and 2.6% non-streaming. On latency, also measured by Artificial Analysis, time to final transcription improves by 70% over Chirp 3 — for real-time use cases, that number cuts deeper than WER.
One Model, Two Endpoints
Architecturally it ships as two APIs: gemini-3.5-transcribe handles pre-recorded files through the Interactions API, while gemini-3.5-transcribe-live does bidirectional streaming over the Live API. The capability list includes multi-speaker attribution, word-level timestamps, automatic language detection and code-switching across more than 85 locales, custom vocabulary speech biasing, and a Smart transcription mode that cleans up spoken disfluencies. The limits are equally explicit: Smart mode cannot be combined with word timestamps or speaker diarization; live sessions cap at 10 minutes, single files at 1 hour, and 30 minutes with diarization on. Pricing runs about $0.005 per minute for batch and $0.009 per minute for live, with no open weights.
Why a Dedicated ASR in the Era of General Models
My read: transcription is shifting from "hearing words" to "understanding speech." The old ASR race was about slicing audio into text; a voice agent needs to know who spoke, down to which word, and when the turn ended. Google packed these capabilities — speaker attribution, timestamps, custom vocabularies, live language switching — into a dedicated branch instead of asking developers to bolt them onto general Gemini. That is foundation work for the voice-agent ecosystem: the Live API is already wired into LiveKit, Pipecat, Agora, and Vercel, while on the consumer side the model is slated to power Search Live, Docs dictation, Gmail voice-to-text, and eventually voice typing in any Chrome text field.
The contrast is Whisper's open-source route: Google chose a closed API at aggressive prices, tying the ecosystem to its own pipeline. The real signal for developers is that transcription competition has moved from raw accuracy to the product of latency, contextual understanding, and toolchain integration. If your voice application still runs a two-stage "ASR then LLM" architecture, this launch is a reminder: the input side is being reinvented (see the official Google announcement).