Cohere Embed and Rerank vs Mistral Embed and Codestral Embed

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.

Mistral Embed and Codestral Embed

EU and US regional endpoints and a French legal entity. Embedding API uptime of 94.36 per cent over 90 days on Mistral's status page, with incidents on 12 and 27 August 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

Mistral Embed and Codestral Embed

1. Use codestral-embed whenever you want smaller or binary vectors. mistral-embed has no output options
2. Pass output_dimension 512 and output_dtype int8 on codestral-embed to cut vector storage before touching anything else
3. Keep chunks under 8k tokens. There's no long-context embedding model on this API
4. Check status.mistral.ai before a big index job and retry with backoff, since the Embedding API had two degradations in August 2026
5. Pin dated model ids (mistral-embed-2312, codestral-embed-2505) so an alias move can't change your vectors