Cohere's open-weight ASR model hits 5.4% word error rate — low enough to replace speech APIs in production pipelines

Enterprises building voice-enabled workflows have had limited options for production-grade transcription: closed APIs with data residency risks, or open models that trade accuracy for deployability. Cohere's new open-weight ASR model, Transcribe, is built to compete on all four key differentiators — contextual accuracy, latency, control and cost.

Cohere says that Transcribe outperforms current leaders on accuracy — and unlike closed APIs, it can run on an organization's own infrastructure.

Cohere, which can be accessed via an API or in Cohere’s Model Vault as cohere-transcribe-03-2026, has 2 billion parameters and is licensed under Apache-2.0. The company said Transcribe has an average word error rate (WER) of just 5.42%, so it makes fewer mistakes than similar models.

It’s trained on 14 languages: English, French, German, Italian, Spanish, Greek, Dutch, Polish, Portuguese, Chinese, Japanese, Korean, Vietnamese and Arabic. The company did not specify which Chinese dialect the model was trained on.

Cohere said it trained the model “with a deliberate focus on minimizing WER, while keeping production readiness top-of-mind.” According to Cohere, the result is a model that enterprises can plug directly into voice-powered automations, transcription pipelines, and audio search workflows.

Self-hosted transcription for production pipelines

Until recently, enterprise transcription has been a trade-off — closed APIs offered accuracy but locked in data; open models offered control but lagged on performance. Unlike Whisper, which launched as a research model under MIT license, Transcribe is available for commercial use from release and can run on an organization's own local GPU infrastructure. Early users flagged the commercial-ready open-weight approach as meaningful for enterprise deployments.

Organizations can bring Transcribe to their own local instances, since Cohere said the model has a more manageable inference footprint for local GPUs. The company said they were able to do this because the model “extends the Pareto frontier, delivering state-of-the-art accuracy (low WER) while sustaining best-in-class throughput (high RTFx) within the 1B+ parameter model cohort.”

How Transcribe stacks up

Transcribe outperformed speech-model stalwarts, including Whisper from OpenAI, which powers the voice feature of ChatGPT, and ElevenLabs, which many big retail brands deploy. It currently tops the Hugging Face ASR leaderboard, leading with an average word error rate of 5.42%, outperforming Whisper Large v3 at 7.44%, ElevenLabs Scribe v2 at 5.83%, and Qwen3-ASR-1.7B at 5.76%.

Based on other datasets tested by Hugging Face, Transcribe also performed well. The AMI dataset, which measures meeting understanding and dialogue analysis, Transcribe logged a score of 8.15%. For the Voxpopuli dataset that tests understanding of different accents, the model scored 5.87%, beaten only by Zoom Scribe.

Early users have flagged accuracy and local deployment as the standout factors — particularly for teams that have been routing audio data through external APIs and want to bring that workload in-house.

For engineering teams building RAG pipelines or agent workflows with audio inputs, Transcribe offers a path to production-grade transcription without the data residency and latency penalties of closed APIs.