Cohere Embed and Rerank vs ZeroEntropy zerank and zembed

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.

ZeroEntropy zerank and zembed

All four models now open weights under Apache 2.0 on Hugging Face. The hosted API was discontinued after 4 September 2026 and signups closed on 24 July 2026.

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

ZeroEntropy zerank and zembed

1. Don't call api.zeroentropy.dev. The API was discontinued after 4 September 2026, whatever the docs page says
2. Self-host zerank-2 or zembed-1 from Hugging Face under Apache 2.0 if you want the same model
3. Pick a hosted reranker elsewhere if you can't self-host. ZeroEntropy's own guide names Cohere and Voyage
4. Re-embed the corpus if you move off zembed-1. Vectors from another model aren't compatible