# Cohere embed-v3 vs OpenAI text-embedding-3-large: which embeddings to ship in 2026?

**Source:** https://promtable.com/compare/cohere-embed-vs-openai-embed

> OpenAI text-embedding-3-large is the strong general-purpose default. Cohere embed-v3 leads on multilingual + RAG-tuned recall + citation-friendly ergonomics. Pick OpenAI for breadth, Cohere for multilingual RAG.

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OpenAI text-embedding-3-large is the strong general-purpose default. Cohere embed-v3 leads on multilingual + RAG-tuned recall + citation-friendly ergonomics. Pick OpenAI for breadth, Cohere for multilingual RAG.

## At a glance

| Dimension | Cohere embed-v3 | OpenAI text-embedding-3-large |
|---|---|---|
| MTEB benchmark | Top tier | Top tier |
| Multilingual fidelity | **Best in class** ✓ | Strong |
| Dimensionality | 1024 (compact) | 3072 default + truncatable |
| RAG-tuned recall | **Strong with embed-v3 + Rerank** ✓ | Strong with hybrid + reranker add-on |
| Latency | Fast | Fast |
| Price per 1M tokens | **~$0.10 (embed-v3)** ✓ | ~$0.13 |
| API ergonomics | **Citations-by-design** ✓ | Standard |
| Ecosystem (frameworks, libs) | Strong | **Largest in the world** ✓ |

## Verdict

OpenAI text-embedding-3-large is the right pick when you want the broadest framework support and the highest-dimensional vectors. Cohere embed-v3 wins for serious multilingual RAG, EU-friendly deployment, and citation-first workflows. Many production teams in 2026 pair Cohere embed-v3 + Cohere Rerank as the cheapest credible multilingual RAG stack.

## When to pick which

- **Cohere embed-v3** — Multilingual RAG, citation-first answers, EU-friendly, Cohere Rerank stack.
- **OpenAI text-embedding-3-large** — Largest ecosystem, standard default, truncatable high-dimensional vectors.

## FAQ

### Cheapest embeddings model for RAG in 2026?

Open-weight BGE-M3 and nomic-embed-text-v1.5 (self-host). Among hosted, Cohere embed-v3 is materially cheaper than OpenAI.

### Best embeddings for multilingual RAG?

Cohere embed-v3 or BGE-M3 — multilingual recall leads in 2026 benchmarks.

### Can I switch embedding models later?

Yes but expensive — switching requires re-embedding the whole corpus. Pick carefully or abstract behind a routing layer.

## Related

- [/glossary/embeddings](https://promtable.com/glossary/embeddings)
- [/glossary/vector-database](https://promtable.com/glossary/vector-database)
- [/glossary/rag](https://promtable.com/glossary/rag)
- [/guides/rag-production-2026](https://promtable.com/guides/rag-production-2026)

*Last updated: 2026-06-01*
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Original page: https://promtable.com/compare/cohere-embed-vs-openai-embed
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