AssemblyAI Universal-3 Pro vs Google Gemini: Speech-to-text API vs multimodal audio processing

Product overview

Google Gemini

Gemini is a multimodal LLM that processes text, images, audio, and video. It can transcribe audio through prompting as part of its broader AI functionality, leveraging Google's multimodal training to let developers guide transcription behavior through natural language instructions.

Gemini is not a dedicated speech-to-text API. Google's own documentation states: "As of now the Gemini API doesn't support real-time transcription use cases. For dedicated speech-to-text models with support for real-time transcription, use the Google Cloud Speech-to-Text API." For production STT workflows, Google offers its Cloud Speech-to-Text API as a dedicated solution.

AssemblyAI Universal-3 Pro

Universal-3 Pro is a speech language model built for audio transcription and understanding. The model works great out of the box— you don't need to prompt-engineer your way to a good transcript. Submit audio, get accurate results with speaker labels, word-level timestamps, and structured outputs included.

When you do need customization, it's there: natural language prompting for detailed instructions, keyterm prompting for domain-specific vocabulary, or both together. The API also includes speaker diarization without hallucinated labels, speaker identification, entity detection, subtitle generation, and audio redaction. These features that ship as structured API outputs, not prompt-dependent LLM responses.

Language support

Both platforms cover a wide range of languages. Gemini supports 100+ natively, and Universal-3 Pro supports 99 languages total through automatic fallback to Universal-2 when audio falls outside its 6 natively supported languages (English, Spanish, Portuguese, French, German, Italian). You don't need to configure this routing manually; submit any audio and the system handles it.

The real differences are in what you can do beyond raw transcription and how customization works across languages.

Universal-3 Pro supports speaker labels, timestamps, and structured output features like speaker diarization across all 99 languages. Gemini can transcribe across its language set, but doesn't provide the same structured outputs. Word-level timestamps, per-utterance speaker labels, and similar features aren't part of Gemini's transcription output.

On the customization side, natural language prompting on Universal-3 Pro is only available for the 6 natively supported languages. Gemini's prompting works across its full language set. If you need to guide transcription behavior for, say, Japanese or Hindi audio, Gemini gives you that flexibility.

For less common languages, neither platform is going to deliver exceptional accuracy — that's a reality of speech recognition across the board. But for production workflows that need structured outputs alongside the transcript, Universal-3 Pro's feature coverage across 99 languages is the distinction that matters.

Prompting and customization

Gemini's approach

Gemini uses natural language prompting to guide transcription. You write instructions like "Transcribe this medical consultation, paying special attention to drug names and dosages," and the model interprets them. The flexibility comes from Gemini being a language model at its core. It processes instructions the way it processes any other text. Transcription quality depends on prompt engineering, and you'll need to develop and iterate on prompts for your use case.

Universal-3 Pro's approach

The key difference: Universal-3 Pro doesn't require prompting to produce a good transcript. The model delivers accurate results out of the box, with structured outputs—speaker labels, timestamps, subtitles—included by default. Prompting is there when you want it, not because you need it.

When you do want customization, you get two methods. Natural language prompting supports instructions up to 1,500 words for detailed context about speakers, topics, or formatting. Keyterm prompting lets you provide up to 1,000 specific terms you expect in the audio, like drug names, product names, legal terminology, and the model prioritizes those during transcription. AssemblyAI reports up to 45% accuracy improvements on domain-specific vocabulary with keyterm lists.

You can combine both. A call center application might use keyterms for product names and natural language prompts for conversation structure. Legal transcription might pair case-specific terminology with instructions about speaker identification.

This matters at scale: when you're processing thousands of files, you want a model that's accurate by default, with optional customization layered on top, not one that requires a carefully crafted prompt for every request.

Audio tagging

Both platforms identify non-speech audio events through prompting. You instruct the model to note specific sounds like background music, laughter, doors, and it tags them in the transcript. Performance depends on prompt specificity in both cases.

Speaker diarization and multichannel audio

Speaker handling

Gemini handles diarization through prompting. You instruct the model to identify and label speakers in the transcript. The results are unstructured text; you don't get timestamped speaker labels, utterance-level segmentation, or a consistent speaker identification format. For a single file where you need a rough sense of who said what, this can work. For processing thousands of files where you need to programmatically extract speaker turns, it doesn't scale.

Universal-3 Pro provides diarized speaker labels with timestamps as structured API output, with no prompting required. Speaker labels are consistent and don't hallucinate (a known issue with LLM-based diarization where the model invents or misattributes speakers). The API handles unlimited speakers per file, supports speaker identification, and outputs utterance-level data you can parse and process programmatically across your entire pipeline.

Multichannel handling

Universal-3 Pro processes unlimited audio channels simultaneously, with speaker diarization applied to every channel while maintaining speaker tracking across the entire file. Each channel is billed separately.

Concretely: a call center recording with 50 agents on 50 separate channels can be processed concurrently, with diarization on each channel and consistent speaker identification throughout. Gemini typically handles mono or stereo configurations.

For contact centers processing thousands of agent-customer interactions, this difference directly affects processing pipeline complexity.

Entity detection and redaction

Regulated industries need to identify and remove sensitive information from transcripts. This is where the gap between a multimodal LLM and a dedicated speech API is widest.

Gemini

Gemini detects entities through language understanding. You instruct the model to identify PII, PHI, or PCI data like credit card numbers, SSNs, patient IDs, and it flags them based on its comprehension. Results come back as unstructured text. There's no audio redaction. Gemini can only redact at the text level after transcription.

Universal-3 Pro

Universal-3 Pro has built-in entity detection for PII, PHI, and PCI with 40+ specific entity types, like credit card numbers, SSNs, medical record numbers, and other categories, defined in compliance frameworks. Detected entities come back as structured data, not prompt-dependent text.

Audio redaction is the standout: Universal-3 Pro replaces sensitive portions of the original audio with silence or tone. A healthcare provider can distribute call recordings for quality assurance with patient identifiers removed from both transcript and audio, maintaining HIPAA compliance when sharing recordings. Gemini doesn't offer this.

Universal-3 Pro maintains SOC2 Type 2, ISO 27001:2022, PCI DSS v4.0 certifications, with HIPAA BAAs available. This is the kind of documentation compliance teams in healthcare and finance require before approving a vendor.