Embed 5

Embed 5: Frontier Embedding Models for Enterprise | Cohere Skip to content Embed 5 is here: state-of-the-art retrieval - now available in Pro and Fast tiers. Learn more

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Sep 30, 2026

7 minute read

Introducing Embed 5—A new family of frontier embedding models Our most powerful embedding models yet, now available in Pro and Fast tiers.

Key takeaways State-of-the-art enterprise retrieval: Embed 5 Pro achieves the highest average score of any model we tested, particularly across financial datasets, parsed PDFs, and visually rich documents. A new Fast tier: Embed 5 Fast brings strong retrieval quality to latency, and cost-sensitive workloads, at $0.08 per million tokens. One index, two models: Pro and Fast share an embedding space, so teams can index with Pro and query with either model without re-indexing. Built for complex enterprise data: Embed 5 supports multimodal inputs and retrieval, 100+ languages, and a 128K-token context window for longer documents. More efficient at scale: Matryoshka representations and lower-precision outputs reduce vector storage and search costs, while quantized weights lower serving requirements for private deployments.

Today, we're releasing Embed 5, a new family of embeddings models at the frontier of high-quality enterprise retrieval.

Embed 5 delivers stronger retrieval across complex enterprise data while giving teams more control over latency, cost, and deployment. Embed 5 Pro is optimized for maximum quality across multimodal, multilingual, financial, code, and parsed-document retrieval. Embed 5 Fast brings highly competitive performance to latency- and cost-sensitive workloads. Both tiers share a single embedding space, so teams can index with Pro and query with either model without rebuilding the index.

Embed 5 establishes the retrieval foundation for search, RAG, and agentic workflows, surfacing more relevant context while filtering out noise before it reaches expensive generative models. Use Embed to improve answer quality and user experience while helping keep downstream inference costs under control.

Embed 5 is generally available today on the Cohere API and Model Vault , Microsoft Foundry , and Amazon SageMaker . Pricing is $0.12 per million tokens for Pro and $0.08 per million tokens for Fast. Snapshot

Capability Embed 5 Pro Embed 5 Fast

Best for Maximum retrieval quality; offline indexing; complex enterprise corpora Interactive search; high-volume RAG; agentic retrieval

Context length 128K tokens 128K tokens

Inputs Text, images, fused text + image Text, images, fused text + image

Languages 100+ 100+

Output dimensions 2048, 1536, 1024, 768, 512, 256 2048, 1536, 1024, 768, 512, 256

Embedding formats float, int8, binary float, int8, binary

Matryoshka embeddings Yes Yes

Shared embedding space Yes Yes

Supports self-hosting Yes Yes

Pricing $0.12 / 1M tokens (text) $0.40 / 1M tokens (image) $0.08 / 1M tokens (text) $0.40 / 1M tokens (image)

Performance Embed 5 Pro delivers our strongest retrieval performance to date. It achieves the highest average score of any model we tested across ViDoRe V3, financial documents, parsed PDFs, image retrieval, and across key business languages.

Embed 5 is also the first model family evaluated with RCP-nDCG@10 , our latest retrieval methodology. Instead of scoring only against a limited set of fixed labels, it evaluates retrieved documents against query-specific relevance criteria, capturing relevant results and giving a fuller view of performance on your own corpus. 1 Read more about RCP-nDCG@10 . Enterprise documents Embed 5 excels with visually rich documents where meaning lives in tables, charts, diagrams, and layout — not just text. On ViDoRe V3 , which features documents sampled across key enterprise domains, including financial filings, technical manuals, regulatory material, government reports, textbooks, and lectures, Embed 5 Pro averages 85.8 — an impressive 8.8 gain from Embed 4. 2

That puts it ahead of Voyage 4 Large (83.7), Gemini Embedding 2 (83.2), and OpenAI text-embedding-3-large (75.5). Pro leads five of the eight domains outright and ties Voyage 4 Large on energy, with its largest gains over Embed 4 on HR (+11.4) and industrial (+10.3). Embed 5 Fast averages 84.5, ahead of both Gemini Embedding 2 and Voyage 4 Large. See the full results here . Retrieval quality (RCP-nDCG@10) across eight visually rich document domains. Evaluations consisted of parsed text outputs, curated by the authors of ViDoRe. Finance Embed 5 Pro establishes itself as...