New guide ranks leading speech-to-text APIs for voice application developers
Comparison names Deepgram market leader on accuracy and latency as global market approaches $4 billion
A new guide comparing the 10 leading speech-to-text APIs has ranked providers on accuracy, speed, cost and customisation for engineering teams building voice applications.
The guide highlighted research from Grand View Research estimating the global speech-to-text (STT) API market reached $3.8 billion in 2024 and is projected to hit $8.6 billion by 2030, growing at a compound annual growth rate of 14.4%.
Leading STT solutions now use transformer-based architectures and foundation models trained on millions of hours of audio, supporting real-time multilingual transcription across dozens of languages, according to the guide.
The ranking names Deepgram as the market leader on accuracy and latency, citing its Nova-3 model with a 5.26% batch Word Error Rate (WER), a measure of transcription accuracy where lower numbers indicate fewer mistakes.
The guide highlighted Deepgram's Flux as a conversational model with model-integrated end-of-turn detection for identifying when speakers finish talking.
Deepgram supports pre-recorded and real-time audio streams and offers cloud, on-premises and private cloud deployment options, the guide said.
Pricing for Deepgram is listed at $0.0077 per minute for streaming ($0.462 per hour) and $0.0043 per minute for batch processing ($0.258 per hour) on a pay-as-you-go basis.
The guide also compared OpenAI's Whisper family of models, noting support for more than 50 languages and API pricing of $0.006 per minute ($0.36 per hour).
However, Whisper does not offer native real-time transcription or built-in speaker diarisation, which identifies and labels different speakers in audio, the guide said.
Microsoft Azure supports more than 140 languages and reports Word Error Rates around 13% to 23%, according to the comparison.
Azure pricing is listed at $1.00 per hour for real-time transcription and $0.36 per hour for batch processing, the guide said.
The guide advises development teams to prioritise accuracy, latency, cost, customisation and deployment requirements when selecting a provider.
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Teams should perform side-by-side tests using audio that resembles their production workloads before making a final selection, the company said.
The guide recommended developers "run custom evaluations with real audio files from your specific use case" rather than relying solely on published benchmarks.
The Recap
- Guide compares ten leading speech-to-text APIs in 2026.
- Deepgram Nova-3 delivers a 5.26% batch Word Error Rate.
- The company advised running custom evaluations with real audio.
