Which STT Model Works Best for Your Vogent Voice Agent?

Speech-to-text (STT) is at the heart of any voice AI agent. It's the first step in turning a spoken phone call into actionable data, and it needs to be fast, accurate, and context-aware.

At Vogent, we offer built-in support for Deepgram, OpenAI’s Whisper, and Assembly AI. All three are industry leaders, but they shine in different ways. Here’s how they compare and how to choose the right one for your use case.

Accuracy

Deepgram is known for its high accuracy in noisy environments and diverse accents. It uses end-to-end deep learning models trained on real-world phone calls, making it well-suited for enterprise use cases.

OpenAI Whisper performs very well across a wide range of languages and has a solid grasp of informal, conversational speech. It's particularly strong in handling long-form or nuanced inputs.

Assembly AI offers strong accuracy across various environments with a focus on developer-friendly APIs. It is especially effective for structured environments and media transcription, but may require additional filtering for real-time phone call clarity.

In real-world conditions such as phone calls with background noise or variable mic quality, Deepgram often outperforms on clarity and consistency. Whisper is impressive for general-purpose and multilingual support. Assembly lands in the middle, accurate and accessible, but sometimes less optimized for real-time call audio.

Speed and Latency

Deepgram delivers sub-300ms latency in most scenarios. It is built for real-time streaming and optimized for interactive agents.

OpenAI Whisper has higher latency. It is not yet optimized for real-time and typically introduces a delay from several hundred milliseconds to over a second depending on configuration.

Assembly AI offers near-real-time capabilities, though latency is typically slightly higher than Deepgram. It is fast enough for many use cases, but might lag in high-speed, real-time interactions.

For time-sensitive voice calls, Deepgram still offers the best real-time performance.

Customization

Deepgram supports keyword boosting, custom vocabulary, and model tuning, making it ideal for specialized verticals like healthcare or finance.

Whisper does not support fine-tuning or custom vocabulary and is designed more as a general-purpose model.

Assembly AI offers topic detection, sentiment analysis, and auto chaptering, but has limited real-time customization or domain-specific vocabulary.

If you need precision with internal lingo or brand-specific terms, Deepgram is still the top pick.

Cost and Licensing

Deepgram uses usage-based pricing with enterprise SLAs and volume discounts.

Whisper is typically the most cost-effective for batch jobs through OpenAI's API, but real-time use comes with latency tradeoffs.

Assembly AI offers competitive pricing for developers and startups. It is great for projects that need transcription plus metadata like topics or sentiment, though real-time capabilities are still evolving.

The Verdict: Why Vogent Supports All Three

Some teams need blazing speed and robust phone-call transcription, and Deepgram excels here.

Others want a flexible, multilingual, or offline-friendly model, and Whisper fits well.
If you are looking for a versatile API with structured output and analytics, Assembly AI is a strong contender.

Build Smarter with STT Choice

When you build on Vogent, you are not locked into a single model. You can experiment, swap engines, or even combine them depending on the agent or use case.

Ready to see the difference in action? Try Vogent for free or reach out to our team to explore which STT setup is right for your voice AI agents.