# Moonshine: Open STT Models Aim to Beat Whisper

> Resemble AI releases MIT-licensed speech-to-text models that claim higher accuracy than OpenAI's Whisper Large v3.

Published by The Open Weights on Feb 24, 2026. Canonical: https://theopenweights.com/news/moonshine-stt-475j

## Key facts

- Company: Resemble AI
- Model: Moonshine STT
- Version: Moonshine
- Category: Speech → Text
- Modalities: Speech → Text
- License: MIT (Open weights, commercial use allowed)
- Significance: notable
- Published: 2026-02-24
- Last verified: 2026-06-23
- Canonical URL: https://theopenweights.com/news/moonshine-stt-475j

Resemble AI has released Moonshine, a family of open-weight speech-to-text models that the company claims achieve higher accuracy than OpenAI's Whisper Large v3. The models are published under the permissive MIT license, making them straightforward to adopt in commercial as well as research projects, and are available on [GitHub](https://github.com/moonshine-ai/moonshine).

Moonshine arrives in the under-1B parameter range, positioning it as a comparatively lightweight option in a space where Whisper has become the default reference point for open transcription. The compact size matters: smaller automatic speech recognition models are easier to run on edge devices and consumer hardware, and they reduce the cost and latency of real-time transcription.

## Why it matters

Whisper has dominated open speech recognition since its release, so any credible challenger draws attention. A permissively licensed alternative that claims to edge out Whisper Large v3 on accuracy could give developers a meaningful choice, particularly for applications where model footprint and licensing terms are deciding factors.

- MIT license, suitable for commercial use
- Open weights in the sub-1B parameter class
- Positioned as a direct accuracy competitor to Whisper Large v3

As always, vendor accuracy claims warrant independent verification across languages, accents, and noisy conditions before teams commit to a swap. But the combination of open weights, a permissive license, and a small footprint makes Moonshine worth a look for anyone building transcription into their stack.

## Get the model

- [Hacker News](https://github.com/moonshine-ai/moonshine)

## Sources

- [Show HN: Moonshine Open-Weights STT models – higher accuracy than WhisperLargev3](https://github.com/moonshine-ai/moonshine) — Hacker News, Feb 24, 2026

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Source: The Open Weights (https://theopenweights.com/). Aggregated and written by Claude, curated by humans. Cite as: "Moonshine: Open STT Models Aim to Beat Whisper", The Open Weights, Feb 24, 2026, https://theopenweights.com/news/moonshine-stt-475j