Google launches Gemini 3.5 Transcribe

Gemini 3.5 Transcribe does not just convert audio into text - it edits it. Google calls this "smart transcription": the model strips filler words like "um" and "uh," resolves mid-sentence self-corrections, and reformats rambling speech into clean, readable text rather than a literal record of what was said. In Google's own example, "Um, so for the meeting, I think we should, uh, invite Alice and, wait no, Bob and Carol" becomes simply "For the meeting, I think we should invite Bob and Carol."^[2]That is a break from older ASR systems built to match the audio waveform word-for-word - this one is built to match what the speaker meant to say^[1].

Under the hood, "Gemini 3.5 Transcribe" is really two separate models. gemini-3.5-transcribe handles pre-recorded audio through the Interactions API, supporting speaker diarization, word-level timestamps, and custom vocabulary biasing of up to 1,000 terms, on files up to one hour long (30 minutes if diarization or timestamps are switched on). gemini-3.5-transcribe-live streams through the Live API instead, trading those features for sub-200ms interim latency - but it caps sessions at 10 minutes and skips diarization and word timestamps entirely^[2]. The split mirrors how the launch itself was framed: one endpoint built for accuracy on recordings, the other for responsiveness in real time^[3].

Google, citing independent measurement from Artificial Analysis, reports a 2.6% average word error rate on pre-recorded audio and 4.0% on live streaming audio^[2]. On the FLEURS multilingual benchmark the model scores 5.04% WER non-streaming and 5.50% streaming^[2]- one independent breakdown of that same benchmark put those numbers ahead of Google's own prior model Chirp 3, OpenAI's live transcription model, and Deepgram's Nova 3, though reviewers caution that different benchmarks use different audio conditions and recommend testing against your actual use case rather than relying on headline numbers alone. Google also claims a 70% improvement in time-to-final-transcription versus Chirp 3^[3][4], with estimated pricing of about $0.005 per batch minute and $0.009 per live minute^[2].

Chirp 3, Google's previous flagship transcription model, was a standalone engine. Gemini 3.5 Transcribe is pitched differently: as a substrate for agentic workflows, with function-calling support that lets a transcription session hand off follow-up tasks - like generating an image or running a web search - to other Gemini models^[4]. That reframing matters because it puts transcription inside the same interface teams use to build voice agents rather than as a separate pipeline stage bolted on afterward, a positioning reinforced by the platform's own list of early integration partners spanning voice-agent infrastructure (LiveKit, Pipecat, Agora, Vision Agents), developer tooling (LangChain, Vercel), and vertical apps (IntelliTek Health, Lingopal).

Within a day of launch, independent developers were already shipping demos - one builder open-sourced a Wispr Flow-style dictation app built directly on the new model, and Google's developer account followed up by showcasing community projects ranging from voice-first agents to multilingual audio tools. The most-repeated proof point across social discussion was the model's ability to handle mid-sentence language switching accurately. Reddit reaction was largely positive, with one thread reporting the model outperforming Whisper and OpenAI's transcription model on a difficult Finnish-language dataset, but the response was not uniform: one user said a rival ASR vendor beat it in their own side-by-side test, another hit a usage cap on a paid Gemini subscription tier, and others pointed out that the live/streaming endpoint still cannot distinguish between multiple speakers, since diarization is batch-only.