Gemini 3.5 Transcribe: Features, API & Business Use Cases

Announced on August 26, 2026, by Google, Gemini 3.5 Transcribe has officially entered public preview as the company's most precise speech-to-text model yet. Designed to convert raw audio directly into accurate, polished, and structured text, the model bridges the gap between raw acoustic signals and advanced natural language processing. For organizations relying on AI to process spoken data, this release represents a meaningful leap forward in accuracy, speed, and developer flexibility.

What Makes Gemini 3.5 Transcribe Different?

Unlike traditional transcription engines that output verbatim text with little context awareness, Gemini 3.5 Transcribe handles self-corrections, removes conversational filler words, and structures unstructured audio streams in real time. According to Artificial Analysis benchmarks, the model achieves an average Word Error Rate (WER) of 4.0% for streaming and 2.6% for non-streaming workloads. On the multilingual FLEURS benchmark, it records 5.50% WER for streaming and 5.04% for non-streaming. It also runs 70% faster in time-to-final-transcription than Google's previous model, Chirp 3. Early feedback from companies including vivo, Intellitek Health, and Lingopal highlighted improvements in latency, accuracy, and multilingual coverage.

Core Features and Technical Capabilities

Gemini 3.5 Transcribe introduces capabilities that go beyond legacy transcription software, useful for teams building AI chat and voice agents or automating document workflows.

Multilingual Support and Code-Switching

The model automatically detects and transcribes more than 85 languages, handling regional accents and dialects along the way. A standout capability is mid-sentence code-switching, letting speakers move between languages naturally without triggering errors. Custom vocabulary biasing supports up to 1,000 terms, with the best accuracy under 100, which helps with industry-specific jargon, product names, and technical terminology.

Disfluency Removal and Natural Editing

Conversational audio is rarely clean. Gemini 3.5 Transcribe handles self-corrections (turning "let's meet Tuesday—no, Wednesday" into the intended final choice) and filters out filler words like "ums" and "ahs". It also supports multi-speaker identification with timestamps for up to three speakers out of the box, with larger speaker counts available as an experimental feature.

Integrating Gemini 3.5 Transcribe into Business Workflows

Enterprises generate large volumes of audio through support calls, sales meetings, and internal briefings. Turning that audio into accurate, searchable text opens up real operational value.

Automating Customer Support Documentation

Call centers and support desks handle thousands of hours of audio a day. Automated transcription pipelines can convert support calls into searchable text, flag sentiment, and log resolution summaries straight into a CRM, cutting down on manual admin work and speeding up quality checks.

Powering RAG Knowledge Agents with Audio Data

Plenty of institutional knowledge lives in recorded meetings and training sessions rather than written documents. Feeding clean transcripts from a model like this into RAG knowledge agents makes those audio archives searchable and retrievable through a normal conversation. Paired with custom AI agent development, teams can query past meetings and technical debriefs in plain language instead of scrubbing through recordings.

Building Solutions with the Gemini API

Google offers two separate API endpoints depending on the use case:

• Live API (model id: gemini-3.5-transcribe-live): built for real-time streaming with sub-second latency. It processes 16-bit PCM, 16kHz mono audio in 100ms chunks, with a 10-minute session cap.
• Interactions API (model id: gemini-3.5-transcribe): built for pre-recorded or batch audio, with speaker attribution, function calling to other Gemini models, and word-level timestamps. It supports files up to 1 hour, or 30 minutes with diarization enabled.

One technical limit worth knowing: smart mode (filler-word removal) can't currently run at the same time as word-level timestamps or diarization.

The model is available now through Google AI Studio, Google Antigravity, the Gemini Enterprise Agent Platform, the Gemini app on macOS, and the Rambler dictation feature on Android Gboard in select regions, with Chrome integration and Gemini Enterprise for Customer Experience coming soon. Third-party platforms including Agora, Fishjam, LangChain, LiveKit, Pipecat, Vercel, and Vision Agents have already added support.

Pricing is roughly $0.005 per minute for batch processing through the Interactions API and $0.009 per minute for real-time streaming through the Live API, subject to change, with a free tier through Google AI Studio and enterprise volume pricing available. Check Google's official pricing page for current rates before building anything cost-sensitive around it.

Frequently Asked Questions

What is Gemini 3.5 Transcribe?

Gemini 3.5 Transcribe is Google's speech-to-text model, announced August 26, 2026, that converts audio files and live speech into accurate written text, automatically handling multi-language code-switching and filtering out disfluencies.

How can businesses integrate Gemini 3.5 Transcribe into their workflows?

Businesses can integrate the model using the Interactions API (batch) or Live API (streaming) to build automated transcription pipelines, customer support analysis tools, and searchable knowledge bases from audio.

What languages does Gemini 3.5 Transcribe support?

It automatically detects and transcribes more than 85 languages, including regional accents and dialects, with mid-sentence code-switching between languages.

Does Gemini 3.5 Transcribe support real-time transcription?

Yes. The Live API handles real-time streaming with sub-second latency, while the Interactions API handles pre-recorded batch audio with speaker attribution and timestamps.

How accurate is Gemini 3.5 Transcribe?

Per Artificial Analysis benchmarks, it reports a 4.0% average Word Error Rate for streaming and 2.6% for non-streaming use, and runs about 70% faster to final transcription than Google's previous Chirp 3 model.

What does Gemini 3.5 Transcribe cost?

Google's published rates are roughly $0.005 per minute for batch processing and $0.009 per minute for live streaming, with a free tier via Google AI Studio. Pricing is subject to change, so check Google's official page before committing to a build.

Streamline Operations with Custom AI Integration

As speech models like Gemini 3.5 Transcribe make real-time audio intelligence faster and more accurate, the harder part is usually wiring it into what your business already runs: a call center, a CRM, a knowledge base. Whether you're building a voice agent, automating call-center analytics, or feeding audio into an internal knowledge base, the integration work is where most of the value gets lost or won.

At AuraStag, we build custom AI chat and voice agents, workflow automations, and RAG knowledge agents around your exact process, not a generic template. If you're evaluating how to bring a model like this into your stack, contact our team to talk through what it would actually take.