Gemini-3.5-Transcribe

I’ve tested at least 20 STT models in a benchmark I’ve set up with German, Italian and English voices from meetings in my company.

The voices contained very industry specific words, the languages changed from one sentence to another, sometimes words in a language were mentioned while a discussion was in another.

The only local model that satisfies me is Voxtral Mini 3b, the only paid API that is slightly better is eleven labs. Yes, Voxtral might not reach the best score in the benchmarks, but to me, it just solves a problem. It might not be the best in terms of speed... but that’s not a problem for me.

Happy to test this new model from Google but I’m not sure I’d go with that instead of something that can run so easily in my machine.

I've been testing it on Pixel 11 Pro and I mostly dislike it. It is convenient when you have something long to say without thinking about it first. But the main issue is when you want to say something precise with specific wording it might "simplify" it and break the meaning.

Something like "I hesitated to check it, I should have verified" => "I should have verified" (The "I hesitated..." is removed but I said it because I wanted to let the person know that I thought about it earlier)

I tried the sentence few times and it always removed the first part.

That's really impressive! It says it's available in GBoard on Android but I don't see it yet. I assume like most Google features it will be a rolling release over the course of several months, can't wait to try it some day.

I've been using Wispr Flow on my pixel 9 and I like it. It does the things that Rambler says it's supposed to do but it's available on my Pixel 9 unlike rambler that's currently only available for pixel 11.

Curious if this suffers from the same hallucination problems as Chirp or not. For example, if you pass chirp some audio with noise or even no audio, it will barf text at you like "I don't know. I don't know. I don't know." until a request timeout fires after like 10 minutes. It's... really bad. For this reason, we've moved back to Whisper for timestamp accuracy and then Gemini Flash for transcription correction.

Still more expensive and worse performing than ElevenLabs Scribe, unfortunately. Not sure who's the target audience for this.

Very impressive, including the ability to hit fn in any text field and say "generate an image ...".

I am not sure if "Word Error Rate" captures what has always been wrong with transcription. My biggest complaint is that it inserts sentence breaks in random places, then fails to evaluate the result, even though it is obviously wrong. Then I have to go fix it which can be harder than having just typed it myself, due to the difficulty of positioning the Android cursor, the fact that it automatically capitalizes if you delete a capital letter, etc. And much of the time I fail to notice the errors until later.

Hopefully YouTube automatic captions improve with this

I'd love to know how this handles proper subtitle formatting. I'm in the process of learning many languages, and being able to cross check my own understanding with film and video would be fantastic.

Where does the compute happen?

I suppose it's a cloud thing?

Does somebody knows what top locales list is sampled from? their own usage data? Also when they will use their AI to give better directions in Waze?

I tried the sentence few times and it always removed the first part.

Still more expensive and worse performing than ElevenLabs Scribe, unfortunately. Not sure who's the target audience for this.

Very impressive, including the ability to hit fn in any text field and say "generate an image ...".

Hopefully YouTube automatic captions improve with this

I’ve tested at least 20 STT models in a benchmark I’ve set up with German, Italian and English voices from meetings in my company.

The voices contained very industry specific words, the languages changed from one sentence to another, sometimes words in a language were mentioned while a discussion was in another.

Happy to test this new model from Google but I’m not sure I’d go with that instead of something that can run so easily in my machine.

I'd love to know how this handles proper subtitle formatting. I'm in the process of learning many languages, and being able to cross check my own understanding with film and video would be fantastic.

Does somebody knows what top locales list is sampled from? their own usage data? Also when they will use their AI to give better directions in Waze?

Agree about Voxtral being the only model — local or cloud — that handles multilingual conversation really well. I‘m not sure what they do differently, but mixed-language sentences and industry terms don‘t seem to faze it where other model begin to struggle.

I use Parakeet 3. How does that fare in your testing?

Any possibility to share some of the eval audio?

Try the recent MOSS one? It’s very good

It is confusing. As far as I can tell "rambler for Android" is a named subfeature of gboard and only on Pixel 10+ and newer Samsung phones. From a user standpoint it "just" seems like the transcription has gotten better - not an app you use or a feature that you enable.

I eval'd this pretty heavily with no audio or just noise, as for us hallucinating a response is very bad. It works great in this scenario. There are some bugs, e.g. randomly exactly 20 seconds of silence will lead to a 403, but I'm sure these aspects will be improved over time. I didn't get any hallucinations though.

I've seen Whisper do this exact thing as well. And also repeating a few words over and over near the end, words that were said but not completely at the end. It seems to overwrite the last few sentences with that so actual content is lost. It's really annoying especially because Whisper is really quite good otherwise. Sometimes a friend sends me audio messages from a noisy car and I can't make it all out but whisper gets it mostly right.

I am using it all day as a main dictation model, and I can say it's the best model in terms of accuracy, latency, and formatting I have ever used.

have another model do a pass to clean it up, saw a demo of local STT where someone did this, can fix a lot of things, especially with gotchas for the STT model in a clean-transcript.md

Where does the compute happen?

I suppose it's a cloud thing?

They mentioned the pixel phone so probably using https://blog.google/products-and-platforms/platforms/android...

Try the recent MOSS one? It’s very good

I use Parakeet 3. How does that fare in your testing?

I am using it all day as a main dictation model, and I can say it's the best model in terms of accuracy, latency, and formatting I have ever used.

Any possibility to share some of the eval audio?

They mentioned the pixel phone so probably using https://blog.google/products-and-platforms/platforms/android...

I love it but it misses the business specific words when in different language. Sometimes it stretches them out to fit unrelated words in the language of the rest of the conversation. I miss its speed though.

mind if I ask what dictation tool you are using?

Of course not, but it’s easily replicable just by mixing different languages conversations together, adding a word here and there of some very specific German jargon.

have another model do a pass to clean it up, saw a demo of local STT where someone did this, can fix a lot of things, especially with gotchas for the STT model in a clean-transcript.md

I haven't tried it, but this looked promising for that exact task: https://huggingface.co/superwhisper/s1-mini

I think the model can even evaluate itself. If it looks afterward at an output like "do you. Want to get lunch?" in the absence of affirmative evidence that the user wanted it that way, it should be able to see that it goofed.

mind if I ask what dictation tool you are using?

Of course not, but it’s easily replicable just by mixing different languages conversations together, adding a word here and there of some very specific German jargon.

I haven't tried it, but this looked promising for that exact task: https://huggingface.co/superwhisper/s1-mini

Aug 26, 2026

Our latest speech-to-text model designed for precise and intelligent real-time transcription.

Diego Melendo Casado

Senior Director, Engineering, Gemini Audio

Luke Leonhard

Chief of Staff, Gemini Audio, on behalf of Gemini Audio Team

Text "Gemini 3.5 Transcribe" next to the Gemini spark, all on a blue background

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This content is generated by Google AI. Generative AI is experimental

Today, we’re introducing Gemini 3.5 Transcribe, our most precise speech-to-text model yet, designed for intelligent voice interactions. Unlike conventional speech recognition models that struggle with background noise, complex jargon, and disfluency cleanup, Gemini 3.5 Transcribe converts raw audio directly into accurate, polished, formatted text.

Across our products like the Gemini app and on Android, we’ve seen consumers already benefiting from this transcription model with new voice capabilities like Rambler on Android and in the Gemini app on macOS. Now, developers can build similar capabilities with Gemini 3.5 Transcribe in the Gemini API in Google AI Studio and Gemini Enterprise Agent Platform.

We've built 3.5 Transcribe to plug seamlessly into your developer workflows, whether you’re building voice agents, real-time captioning tools, or post-call analytics pipelines. The model is available across two separate APIs:

Real-time streaming: Delivers continuous, bidirectional streaming with sub-second latency for interactive voice apps via the Live API using gemini-3.5-transcribe-live****.
Pre-recorded audio processing: Transcribes recorded audio, meetings, call logs, and more with speaker attribution and word-level timestamps via the Interactions API using gemini-3.5-transcribe****.

Get more precise and intelligent transcription

Gemini 3.5 Transcribe is designed to capture your natural speaking style to better understand your intent and recognize custom vocabulary, so you can execute tasks with your voice.

Smart transcription: Seamlessly handles self-corrections (like "let’s meet Tuesday—no, Wednesday"), removes filler words (“ums” and ‘“ahs"), auto-formats your text.
Function calling: The model can delegate complex tasks (such as image generation and file analysis) to other Gemini models via function calls. Currently available in the Gemini macOS app.
More precise transcription: As measured by Artificial Analysis, achieves an average Word Error Rate (WER) of 4.0% for streaming and 2.6% for non-streaming use-cases. It shows strong performance across noisy, real-world environments, accurately capturing alphanumeric entities like postal codes and order IDs.
Custom vocabulary: Recognizes specialized jargon and unique spellings by seamlessly adapting transcriptions to your provided custom vocabulary.
Global language support: Automatically detects and transcribes over 85 languages, seamlessly handling regional accents and diverse dialects.
Multi-speaker identification: Accurately attributes speech in pre-recorded audio with timestamps for up to three speakers (support for 3+ speakers is experimental).

Gemini 3.5 Transcribe’s performance represents a major advancement from our previous transcription model, Chirp 3, offering new capabilities, improved word error rates, and significantly better latency. As measured by Artificial Analysis, time to final transcription, for example, improves by 70%. On the FLEURS benchmark across a set of top languages and locales, the model delivers precise multilingual performance, improving over Chirp 3, and achieving a 5.50% WER in streaming mode and 5.04% WER in non-streaming use-cases.

Graph of streaming speech recognition accuracy

Experience smart transcription and advanced dictation

In addition to the Gemini API in the Google AI Studio and Gemini Enterprise Agent Platform, 3.5 Transcribe goes further than standard speech-to-text to make working across Google feel more natural and intuitive. By bringing context-aware understanding directly into everyday surfaces like Gboard, Antigravity, the Gemini app, and Chrome, it captures nuances, intent, and inline edits with ease.

On Gboard on Android, through the new Rambler feature, 3.5 Transcribe transforms spoken thoughts into well-formatted text, filtering out filler words. You can also use your voice to make edits, correct misspellings, and change the writing style.
On Google Antigravity, 3.5 Transcribe pairs screen context and chat history, with your permission, to ensure pinpoint transcription accuracy across file names, agent thoughts, and active documents.
In Google AI Studio, you can access 3.5 Transcribe in Build mode to vibe code apps with your voice on the fly.
In the Gemini app on macOS, 3.5 Transcribe not only transcribes your free natural speech into clean formatted text, but also enables voice commands that can pair seamlessly with screen context to power complex workflows. By calling on other Gemini models in the background to handle the heavy lifting, the model makes it effortless to summarize local files, repurpose text across apps, or generate images right at your cursor—using just your voice.
Coming soon to Chrome, you’ll be able to talk to type in any web field — making it effortless to dictate replies, draft posts, or prompt Gemini in Chrome more naturally and easily with your voice.

Read the early reviews

By leveraging the Gemini Live API, developer platforms such as Agora, Fishjam, LangChain, LiveKit, Pipecat, Vercel, and Vision Agents enable developers to build and deploy high-performance voice-driven interfaces with ease. These platforms manage complex real-time media streaming infrastructure behind the scenes, allowing developers to focus entirely on crafting the user experience.

Companies like vivo, Intellitek Health, and Lingopal have also shared positive feedback on 3.5 Transcribe, highlighting its impressive latency, accuracy, and expansive language support.

vivo testimonial