Cohere Embed and Rerank vs Voyage AI embeddings and rerankers

Cohere Embed and Rerank

Rerank 4 Pro and Fast with 32K context and top_n, tracked per model on the status page. Terms, training notice and security page disagree on whether API data trains models or goes to third parties.

Voyage AI embeddings and rerankers

200 million free tokens per current model, then $0.02 to $0.12 per million. Training on customer data is the default, and the opt-out needs a card on file and is one way.

Cohere Embed and Rerank

1. Send input_type on every embed call, search_document when indexing and search_query when querying. The endpoint rejects a call without it
2. Batch 96 inputs a call, the maximum, stay under 2,000 inputs a minute, and check every batch returns every embedding type you asked for (an open SDK bug drops types missing from the first response)
3. Budget rerank by searches. One query with up to 100 documents is one search, and a document over 500 tokens counts as several
4. Set max_tokens_per_doc on rerank. The default of 4,096 truncates long documents even on the 32K models
5. Ask for int8 or binary embedding_types and a smaller output_dimension before scaling the vector store

Voyage AI embeddings and rerankers

1. Opt the organisation out of training before sending anything private. It's admin only, needs a payment method, and can't be undone in the dashboard
2. Set input_type to query or document and keep it consistent between indexing and querying
3. Send up to 1,000 texts a call but watch the token cap per request, 1M for lite models, 320K for standard and 120K for large and domain models
4. Ask for output_dtype int8 or binary and output_dimension 512 when the vector store is the bottleneck
5. Use rerank-3-lite over the top 100 from a cheap first pass, at $0.02 per million tokens