Which transcription API is the most accurate?

For clean English audio, Deepgram Nova-2, AssemblyAI Universal, and OpenAI Whisper large-v3 are all near the top with word error rates in the 5–8% range. Accuracy varies heavily with audio quality, accents, and domain vocabulary, so test on your own samples.

Which APIs support real-time streaming?

Deepgram, AssemblyAI, Google Speech-to-Text, and Azure all offer low-latency streaming transcription. OpenAI Whisper (hosted API) and Rev.ai async are batch-oriented, so they are better for recorded files than live captions.

How is price per minute calculated?

Speech-to-text is billed per minute of audio processed, usually rounded up. Prices are list-price estimates and clearly labelled; real-time, diarization, and add-on models can carry surcharges, so confirm in each provider's pricing page.

What is diarization and do I need it?

Diarization labels who spoke when, separating speakers into channels like Speaker A and Speaker B. You need it for meetings, interviews, and call analytics, but not for single-speaker dictation or captions.

Transcription Model Comparison

Compare speech-to-text models at a glance

Choosing a transcription API means balancing accuracy, language coverage, real-time vs batch support, speaker diarization, and price per minute. This table puts the major speech-to-text models side by side — OpenAI Whisper, Deepgram, AssemblyAI, Rev.ai, Google, and Azure — so you can pick the right model for captions, meeting notes, call analytics, or media transcription.

How to read the table

WER is approximate word error rate on clean English audio; lower is better.
Languages is the number of supported languages.
Real-time flags whether the API supports low-latency streaming for live captioning, versus batch-only async transcription.
Diarization flags built-in speaker labelling.
$/min is a list-price estimate per audio minute; add-on models can cost more.

Filter by real-time requirement and budget, search by model name, and click a column header to sort.

Tips for picking a model

For live captions and voice agents, prioritise the real-time streaming providers — Deepgram and AssemblyAI lead on latency and accuracy.
For bulk media or podcast transcription, batch APIs like Whisper and Rev.ai are cost-effective and accuracy is excellent on clean audio.
For meetings and call analytics, make sure diarization and word-level timestamps are supported.
If privacy or cost at scale dominates, self-hosting open Whisper removes per-minute fees but you pay for GPU compute and operate the pipeline yourself.