Benchmarks
Industry-leading accuracy for streaming speech-to-text.
Benchmarks are an important first step before running your own evaluation. Below are the current benchmarks for our streaming models so you can assess performance across accuracy, latency, and error rates.
Public benchmarks can be misleading due to overfitting and benchmark gaming. We strongly recommend running your own evaluation on your audio data to identify the best model for your use case.
For the full interactive benchmark experience with competitive comparisons, visit assemblyai.com/benchmarks.
English benchmarks
Most recent update: March 2026.
Multilingual benchmarks
Most recent update: March 2026.
Latency gaming
In streaming, speed is critical. To achieve lower TTFT (time to first token) metrics, some providers emit tokens before any audio is actually spoken. These early tokens are hallucinations designed to game the benchmark, making TTFT a misleading measure of actual latency.
External benchmarks
For third-party streaming benchmarks, we recommend the Coval Speech-to-Text Playground.
Methodology
Our benchmarks are evaluated across 250+ hours of audio data, 80,000+ audio files, and 26 datasets. We apply standard text normalization before calculating metrics. For full details on our methodology, visit assemblyai.com/benchmarks.
Run your own benchmark
We’d be happy to help. AssemblyAI has a benchmarking tool to help you run a custom evaluation against your real audio files. Contact us for more information.
You can also run your own benchmarks following the Hugging Face framework which provides a GitHub repo with full instructions.
