Gemini Embedding vs ZeroEntropy zerank and zembed

Gemini Embedding

Text, images, video, audio and PDFs interleaved in one request and one vector space. $0.20 per million text tokens, against $0.02 for OpenAI's small model.

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.

Gemini Embedding

1. Don't send task_type to gemini-embedding-2. Prefix the text instead, task: search result | query: ... for queries and title: ... | text: ... for documents
2. Ask for output_dimensionality 768 unless you need 3072. Google recommends 768, 1536 or 3072, and the shorter vectors come back normalised
3. Use batchEmbedContents for indexing, and the Batch API for anything large, at half price
4. Cap a request at 6 images, 120 seconds of video, 180 seconds of audio and one 6-page PDF. Split longer media first
5. Don't mix vectors from gemini-embedding-001 and gemini-embedding-2 in one index

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