Introducing Nova-3: Setting a New Standard for AI-Driven Speech-to-Text

Deepgram’s Nova-3 speech-to-text model is presented as an upgrade focused on improving transcription accuracy in difficult enterprise settings, including noisy audio, overlapping speech, specialized terminology, numeric sequences, real-time redaction, formatting, and word-level timestamps. Its notable additions include real-time transcription of code-switched conversations across 10 languages and Keyterm Prompting, which lets users supply up to 100 domain-specific terms at inference time without retraining the model. Deepgram reports that, in tests covering 2,703 files and nine domains, Nova-3 achieved median word error rates of 6.84% for streaming audio and 5.26% for batch audio, outperforming the next-best competitors by claimed margins of 54.2% and 47.4%, respectively; it also reports user preference over OpenAI’s Whisper across seven tested languages. The company says the model retains Nova-2’s low latency, supports cloud and self-hosted deployments through its API, and starts at $0.0077 per streaming minute, positioning it for uses such as customer support, emergency response, healthcare, finance, legal transcription, and retail.

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