Jina Embeddings and Reranker vs OpenAI embeddings

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.

OpenAI embeddings

text-embedding-3-small at $0.02 per million tokens, $0.01 through the Batch API. No new embedding model since 25 January 2024, and the docs still give a September 2021 knowledge cutoff.

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

OpenAI embeddings

1. Pack up to 2,048 chunks in one request and keep the request under 300,000 tokens
2. Count tokens before sending. An input over 8,192 tokens is rejected, not truncated
3. Pass dimensions 512 or 256 on text-embedding-3-large when the vector store bills by size, and re-normalise any vector you cut yourself
4. Split a Batch API index job into batches of under 50,000 inputs. It's half price with a 24-hour window
5. Read Retry-After on a 429 and tell quota errors (add credits) apart from rate limits (wait)