How to build with Gemini 3.5 Transcribe

A new speech-to-text model built for developers

Google introduced Gemini 3.5 Transcribe on August 26, 2026, describing it as its most precise speech-to-text model so far and positioning it for “intelligent voice interactions” rather than only literal dictation [1]. The core promise is straightforward: developers can send audio to a dedicated Gemini transcription model and receive text that is more accurate on real-world speech, mixed languages, jargon, alphanumeric strings and everyday speech disfluencies [1].

The practical shift is that Gemini 3.5 Transcribe is not a single endpoint. Google is making it available through two different implementation paths: gemini-3.5-transcribe for pre-recorded audio through the Interactions API, and gemini-3.5-transcribe-live for continuous streaming transcription through the Live API [1]. That split is the main architectural decision developers need to make before writing code.

If the product is a meeting recorder, interview tool, call-summary pipeline or media archive, the standard model is the better starting point because it is optimized for completed audio files and can return richer annotations such as speaker attribution and word-level timestamps [2]. If the product is a real-time captioning system, voice interface, agent front end or in-call assistant, the Live API path is designed for incremental text as speech happens [3].

The two API routes: final transcript versus live text

For file transcription, the Gemini API documentation shows a simple sequence: upload the audio file, create an interaction with the model gemini-3.5-transcribe, pass the uploaded file URI as an audio input, and read the transcript from interaction.output_text [2]. This makes the Interactions API path natural for workflows where latency is less important than a final, post-processed transcript [2].

The batch route also exposes the features that teams typically need for compliance review, editorial work or meeting intelligence. Google’s documentation says the model supports automatic language identification, speaker diarization, word-level timestamps, custom vocabulary hints and a smart transcription mode [2]. It also handles code-switching by detecting language automatically or by accepting BCP-47 language hints when the language is known in advance [2].

The Live API route is different. Google’s Live transcription guide says developers connect over WebSockets or the Google Gen AI SDK to stream continuous audio input and receive incremental text transcriptions as speech occurs [3]. The model ID here is gemini-3.5-transcribe-live, and the response modality is text rather than audio [3]. Google distinguishes this from a live conversational agent: Live Transcription is a dedicated speech-recognition pipeline, while a Live Agent listens, reasons and can speak back [3].

That distinction matters for product design. A live transcription app should not be treated as a chatbot with a microphone; it is a streaming text service that can feed captions, search indexes, moderation systems or agent orchestration. Conversely, a voice assistant that needs reasoning, tool calls and spoken output may need to combine transcription with other Gemini capabilities rather than relying on the transcription endpoint alone.

Smart transcription is useful, but not always neutral

One of the headline capabilities is smart transcription. In Google’s announcement, the model can remove filler words, handle self-corrections and format spoken text automatically [1]. In the developer documentation, smart mode is described as a reading-optimized output that removes disfluencies, resolves inline corrections, structures lists and applies punctuation, casing, dates, currencies and number formatting [2].

For everyday dictation, that is a major convenience. A user can say a messy sentence, correct themselves aloud, and receive a cleaner written version. For product builders, it reduces the amount of downstream cleanup code normally needed for paragraphs, bullets, dates and numbers.

But smart transcription is also a product-policy decision. In legal, journalistic, medical, research or HR settings, removing hesitation and restructuring sentences can change the evidentiary character of a transcript. The safer pattern is to offer both modes: use verbatim when fidelity matters, and use smart when the transcript is meant to become readable notes, a message draft or a summary input [2]. Google’s documentation also notes that smart transcription is incompatible with timestamp granularities and diarization mode, so teams that require word timings or speaker labels should configure verbatim mode instead [2].

Accuracy, languages and specialized terms

Google says Gemini 3.5 Transcribe improves over Chirp 3 and reports average word error rates of 4.0% for streaming and 2.6% for non-streaming use cases, as measured by Artificial Analysis [1]. It also reports FLEURS benchmark results of 5.50% WER in streaming mode and 5.04% WER in non-streaming use cases across selected top languages and locales [1]. 9to5Google separately highlighted Google’s claim that time to final transcription improves by 70% compared with Chirp 3 [5].

Those numbers explain why the launch is aimed at more than conventional speech recognition. Google emphasizes alphanumeric entities such as postal codes and order IDs, which are common failure points in customer support, logistics and voice commerce [1]. The model also supports custom vocabulary so developers can bias recognition toward brand names, acronyms, technical terms or uncommon proper nouns [2].

The multilingual angle is equally important. Google says the model automatically detects and transcribes more than 85 languages, including regional accents and diverse dialects [1]. The developer documentation lists supported languages and BCP-47 codes, and says an empty or omitted language_codes field allows automatic detection and code-switching [2]. For bilingual products, that means a support call can move between English and Spanish, or a meeting can include mixed-language phrases, without requiring the developer to preselect only one language.

Building patterns: what to choose first

A practical implementation plan starts with a question: is the transcript the final artifact, or is it needed while someone is still talking?

If the final artifact is the goal, developers should begin with gemini-3.5-transcribe through the Interactions API [2]. Upload the audio, pass the file URI, and request the right transcript mode. Add custom_vocabulary early for product names, medical terms, customer IDs or internal acronyms. Use verbatim plus diarization and word timestamps when the transcript must support review, search, subtitles or speaker-specific analytics [2]. Use smart when the output is intended as clean prose or a ready-to-edit note [2].

If low latency is the goal, developers should begin with gemini-3.5-transcribe-live through the Live API [3]. The guide shows a persistent session with response_modalities set to text and input_audio_transcription configured for language detection or language hints [3]. For browser or mobile clients that stream directly from the microphone, Google recommends ephemeral tokens so the application does not expose a long-lived API key in client code [3].

Enterprise developers have a parallel path. Google Cloud’s Gemini Enterprise Agent Platform documentation describes preview model IDs for live streaming and synchronous transcription, with feature support such as language auto-detection, custom vocabulary biasing, smart dictation and formatting, diarization and duration limits depending on endpoint and features [4]. This matters for companies that need Google Cloud project controls, enterprise authentication, governance and deployment patterns rather than only AI Studio experimentation [4].

Where the model is already showing up

Google is also bringing the technology into its own products. The announcement says users are already seeing related voice capabilities in Rambler on Android and the Gemini app on macOS, while developers can build with the model in the Gemini API in Google AI Studio and the Gemini Enterprise Agent Platform [1]. 9to5Google reported that the model powers Gboard Rambler, is present in the Gemini macOS app and Google Antigravity, and is coming next to Chrome’s “talk to type” experience for web fields [5].

That consumer rollout matters because it gives developers a reference point. Google is not pitching Gemini 3.5 Transcribe only as a back-office transcription API. It is positioning the model as the speech layer for editing, dictation, agent control, document interaction and real-time user interfaces.

The bottom line

The most important decision is not whether Gemini 3.5 Transcribe can turn speech into text; it is which kind of text your application needs. Use the Interactions API when the product needs a final transcript with richer metadata. Use the Live API when the product needs text while the user is still speaking. Use smart mode when readability is the goal, and verbatim mode when the exact record matters.

For developers, Gemini 3.5 Transcribe is best understood as a two-lane transcription stack: one lane for high-quality completed audio, another for low-latency live audio. The strongest applications will not simply replace an older speech-to-text engine; they will design around the difference between raw speech, readable dictation, structured transcripts and real-time intent capture.

Sources from the last 72 hours

1. [1]Intelligent transcription with Gemini 3.5 TranscribeAug 26, 2026, 12:00 AM UTC
2. [2]Live transcription with Gemini Live API | Gemini API | Google AI for DevelopersAug 26, 2026, 12:00 AM UTC
3. [3]Gemini 3.5 Audio (Live Translate, Transcribe, Transcribe Live)Aug 26, 2026, 12:00 AM UTC
4. [4]Google launches Gemini 3.5 Transcribe, which powers Gboard Rambler & is coming to ChromeAug 26, 2026, 5:00 PM UTC
5. [5]Audio transcription | Gemini API | Google AI for DevelopersAug 26, 2026, 12:00 AM UTC

AI-generated article based on recent web research, then preserved as a dated editorial snapshot.