# Cohere embed-v4 Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/cohere-embed-v4
> Markdown URL: https://aitinkerers.org/technologies/cohere-embed-v4.md
> Technology record last updated: 2026-02-25T07:24:23Z
> Generated: 2026-10-23T15:49:05Z

A high-performance embedding model optimized for multilingual retrieval and 96% storage cost reduction via advanced quantization.

Embed-v4 maps text into a 1024-dimensional vector space to power high-accuracy retrieval-augmented generation (RAG). It supports over 100 languages and processes 512-token sequences. This iteration focuses on compression-aware training: developers use int8 or binary quantization to slash storage costs by 96% while maintaining performance. It ranks at the top of MTEB leaderboards for complex datasets (legal documents and technical manuals). Native support for Pinecone and Weaviate ensures rapid deployment for production-ready AI applications.

- Official technology site: https://cohere.com/embeddings
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Google Maps for Vector Embeddings](https://boston.aitinkerers.org/talks/rsvp_qMsq4QJj4uI)

- Event context: AI Tinkerers Boston Meetup at Publicis Sapient — 2025-09-29 — Boston
- Public talk page: https://boston.aitinkerers.org/talks/rsvp_qMsq4QJj4uI

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