> This page is for version v2 API (default).
> For other versions, use one of these documentation indexes:
> - v2 API (default): https://docs.cohere.com/v2/llms.txt
> - v1 API: https://docs.cohere.com/v1/llms.txt

> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.cohere.com/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.cohere.com/_mcp/server.

# Announcing Embed Multimodal v4

> Release of Embed Multimodal v4, a performant search model, with Matryoshka embeddings and a 128k context length.

We’re thrilled to announce the release of [Embed 4](https://docs.cohere.com/docs/cohere-embed), the most recent entrant into the Embed family of enterprise-focused [large language models](https://docs.cohere.com/docs/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](https://docs.cohere.com/docs/the-cohere-platform), [AWS Sagemaker](https://docs.cohere.com/docs/amazon-sagemaker-setup-guide#embeddings), and [Azure AI Foundry](https://docs.cohere.com/docs/cohere-on-microsoft-azure#embeddings). For more information, check out our [dedicated blog post](https://cohere.com/blog/embed-4).