AI Models
Choose Your Transcription Engine — Compare accuracy, speed, and language support across leading speech recognition models.
How to Choose the Right Model
Different transcription models excel in different areas. Use this guide to pick the best model for your needs.
| Model | WER | Speed | Languages | Best For |
|---|---|---|---|---|
| STT.ai Enhanced | 3.2% | 160.0x | 100 | STT.ai's flagship speech-to-text model with best-in-class accuracy and speed. Optimized … |
| Whisper Large V3 | 4.2% | 8.0x | 99 | OpenAI's largest and most accurate Whisper model. Excellent multilingual support … |
| Whisper Turbo | 5.1% | 32.0x | 99 | OpenAI's speed-optimized Whisper variant. 4x faster than Large V3 with … |
| NVIDIA Canary | 3.5% | 45.0x | 4 | NVIDIA's multi-task ASR model with top-tier accuracy on English. Built … |
| Moonshine | 7.8% | 80.0x | 1 | Ultra-lightweight ASR model designed for edge devices. Runs on Raspberry … |
| NVIDIA Parakeet | 3.0% | 55.0x | 1 | NVIDIA's CTC-based English ASR model. One of the most accurate … |
| SenseVoice | 5.5% | 50.0x | 50 | Multilingual speech understanding model with emotion recognition and audio event … |
| Distil-Whisper | 5.8% | 48.0x | 99 | Distilled version of Whisper Large V3. 6x faster with 49% … |
| Vosk | 12.0% | 100.0x | 20 | Lightweight offline speech recognition. Works without internet, ideal for privacy-sensitive … |
What is WER (Word Error Rate)?
Word Error Rate (WER) is the standard metric for measuring speech recognition accuracy. It calculates the percentage of words in a transcript that differ from the reference. A WER of 5% means roughly 5 out of every 100 words contain an error. Lower is better.
Professional human transcriptionists typically achieve a WER of 4-5%. The best AI models now match or approach human-level accuracy on clean audio.
Not sure which model to use?
Try our default — Whisper Large V3 Turbo delivers the best balance of speed and accuracy. Free to start, no signup required.
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