Jina Embeddings and Reranker vs Voyage AI embeddings and rerankers

Jina Embeddings and Reranker

jina-reranker-v3.5 (20 July 2026) with a 131,072-token window and no document cap. No price per token in any currency on the public pages.

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.

Jina Embeddings and Reranker

1. Send the whole candidate set to rerank in one call. The 131K window on v3.5 fits hundreds of chunks
2. On a 429, back off exponentially. Limits count per key when a key is sent, per IP otherwise
3. Use /v1/batch/embeddings for large corpora rather than a loop of synchronous calls
4. Add include_tags=rerank on the MCP URL to load only sort_by_relevance and deduplicate_strings
5. Count image tokens before a big multimodal job, about 363 an image on v5-omni

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