# Announcing Embed Multimodal v4

We’re thrilled to announce the release of [Embed 4](/guides/models-cohere-embed), the most recent entrant into the Embed family of enterprise-focused [large language models](/guides/cohere-platform-get-started-the-cohere-platform#large-language-models-llms) (LLMs).

Embed v4 is Cohere’s most performant search model to date, and supports the following new features:

1. Matryoshka Embeddings in the following dimensions: '\[256, 512, 1024, 1536]'
2. Unified Embeddings produced from mixed modality input (i.e. a single payload of image(s) and text(s))
3. Context length of 128k

Embed v4 achieves state of the art in the following areas:

1. Text-to-text retrieval
2. Text-to-image retrieval
3. Text-to-mixed modality retrieval (from e.g. PDFs)

Embed v4 is available today on the [Cohere Platform](/guides/cohere-platform-get-started-the-cohere-platform), [AWS Sagemaker](/guides/deployment-options-cohere-on-aws-amazon-sagemaker-setup-guide#embeddings), and [Azure AI Foundry](/guides/deployment-options-cohere-on-microsoft-azure#embeddings). For more information, check out our [dedicated blog post](https://cohere.com/blog/embed-4).

## Related pages

- [Changelog](../changelog.md)
- [Cohere](../index.md)
- [Cohere API](./cohere-api-index.md)
- [Cohere Labs](./cohere-labs-index.md)
- [Cohere Platform](./cohere-platform-index.md)
- [Cookbooks](./cookbooks-index.md)
- [Deployment Options](./deployment-options-index.md)
- [Embeddings (Vectors, Search, Retrieval)](./embeddings-vectors-search-retrieval-index.md)
- [Get Started](./get-started-index.md)
- [Going to Production](./going-to-production-index.md)

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