> ## Documentation Index
> Fetch the complete documentation index at: https://docs.modulate.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Transcription results

> Open ASR Leaderboard results for Modulate's English Fast Transcription model, per-dataset WER, and published price per hour across transcription vendors.

*Reviewed as of 19 August 2026.*

[English Fast Transcription](/get-started/stt) is ranked 1st of 88 systems on the [Open ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard), which scores English transcription across [eight corpora](/benchmarks/datasets#open-asr-leaderboard-8-transcription-datasets) under a single text normaliser.

## Word error rate

| Measurement                   | Datasets                  | WER   |
| ----------------------------- | ------------------------- | ----- |
| Contact-centre subset average | Earnings-22 and VoxPopuli | 7.80% |
| Meeting audio                 | AMI                       | 14.9% |

## Published price per hour

List prices as of 19 August 2026. The leaderboard does not measure them.

### Batch

| Provider and model         | Price per hour |
| -------------------------- | -------------- |
| **Modulate transcription** | **\$0.03**     |
| Grok STT                   | \$0.10         |
| AssemblyAI Universal-3 Pro | \$0.21         |
| ElevenLabs Scribe v2       | \$0.22         |
| Speechmatics Enhanced      | \$0.24         |
| Deepgram Nova-3            | \$0.31         |
| OpenAI GPT-4o-transcribe   | \$0.36         |

### Streaming

| Provider and model         | Price per hour |
| -------------------------- | -------------- |
| **Modulate transcription** | **\$0.06**     |
| Grok                       | \$0.20         |
| Speechmatics Enhanced      | \$0.24         |
| Deepgram Nova-3            | \$0.35         |
| OpenAI GPT-4o-transcribe   | \$0.36         |
| ElevenLabs Scribe v2       | \$0.39         |
| AssemblyAI Universal-3-Pro | \$0.45         |

<Note>
  **Worth knowing:** these are prices per audio hour and exclude enrichment flags. `speaker_diarization`, `time_stamps`, and PII/PHI tagging each change cost and latency, as [Transcription](/get-started/stt) documents per flag.
</Note>

## Reading WER against your own requirement

WER counts substitutions, insertions, and deletions equally, with no notion of which words carry the decision. A transcript that renders a sentence correctly except the account number scores better than one that drops two filler words. An application that reads names, amounts, or identifiers out of the transcript needs accuracy measured on those spans.

Normalisation determines comparability. The leaderboard applies one normaliser to every entrant. Figures taken from two vendors' own marketing were normalised differently, and this tab does not mix them.

## Sources

* [Open ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard), Hugging Face. Live table and evaluation code.
* [Earnings-21: A Practical Benchmark for ASR in the Wild](https://arxiv.org/abs/2104.11348), arXiv:2104.11348. Background on the earnings-call corpora.
* [ESB: A Benchmark For Multi-Domain End-to-End Speech Recognition](https://arxiv.org/abs/2210.13352), arXiv:2210.13352. Dataset selection and normalisation rationale.