Originally published at claudeguide.io/claude-voyage-embeddings-guide

Claude + Voyage AI Embeddings: The Anthropic-Recommended Stack (2026)

Voyage AI is the embedding provider Anthropic officially recommends for Claude RAG pipelines — voyage-3 scores 65.4 on MTEB (vs OpenAI text-embedding-3-large at 64.6) and costs $0.06/M tokens (vs OpenAI at $0.13/M), making it ~50% cheaper at higher quality. Voyage also ships rerank-2 ($0.05/M query+doc tokens) and voyage-code-2 (specialized for code search). This guide covers when to choose Voyage over alternatives, all 4 model variants, multilingual handling, the reranking workflow, and migration from OpenAI embeddings.

For Claude + Pinecone RAG end-to-end see Claude + Pinecone Vector DB. For embeddings-vs-search alternatives see Claude API Semantic Search.

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Voyage AI vs Alternatives (Benchmark)

Provider	Model	Cost/M tokens	MTEB	Dims
Voyage AI	voyage-3	$0.06	65.4	1024
Voyage AI	voyage-3-large	$0.18	67.2	2048
OpenAI	text-embedding-3-large	$0.13	64.6	3072
OpenAI	text-embedding-3-small	$0.02	62.3	1536
Cohere	embed-v3	$0.10	64.5	1024
Open source	BGE-large-en-v1.5	$0 (self-host)	64.2	1024

voyage-3 is the new default for Claude RAG: best quality-per-dollar, Anthropic-blessed.

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Setup (60 seconds)

pip install voyageai
# or
bun add voyageai

Get an API key at https://voyageai.com (Anthropic Console users get a Voyage credit).

import voyageai
vo = voyageai.Client()  # uses VOYAGE_API_KEY env var

# Embed documents
docs = ["Claude is a powerful LLM", "Voyage makes embeddings"]
result = vo.embed(docs, model="voyage-3", input_type="document")
embeddings = result.embeddings  # list of 1024-dim vectors

That's it. Now feed into Pinecone, pgvector, Qdrant, or any vector DB.

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Choose the Right Model

voyage-3 (recommended default)

• Cost: $0.06/M tokens
• Quality: 65.4 MTEB
• Dimensions: 1024
• Use for: most RAG, semantic search, dedup, recommendation
• Multilingual: yes (Korean, Japanese, Chinese, European languages)

voyage-3-large (when quality