{"slug":"google-gemini-embedding-001","name":"Google gemini-embedding-001","domain":"store.google.com","verdict":"As of 2026-08-10, ChatGPT, Claude, Gemini, Grok collectively rank Google gemini-embedding-001 #12 of 15 for long-context embedding apis for document rag. Source: https://modelsagree.com/product/google-gemini-embedding-001 (modelsagree.com, CC BY 4.0).","best_rank":12,"categories":1,"entries":[{"slug":"best-long-context-embedding-apis-for-document-rag","title":"Best long-context embedding APIs for document RAG","rank":12,"of":15,"score":2,"appearances":1,"modelRanks":{"Claude":4},"reason":"State-of-the-art MTEB scores, solid ~2–8K context, Matryoshka dimensions, strong multilingual coverage, and frictionless if you're already on Vertex/Google Cloud with its security, quotas, and billing integration. A safe, high-quality default for GCP shops.","reasons":[{"model":"Claude","reason":"State-of-the-art MTEB scores, solid ~2–8K context, Matryoshka dimensions, strong multilingual coverage, and frictionless if you're already on Vertex/Google Cloud with its security, quotas, and billing integration. A safe, high-quality default for GCP shops."}],"fixes":[{"model":"Claude","fix":"Shorter effective context than Voyage/Cohere and closed/cloud-locked — awkward if your data or infra lives outside Google, and long documents need more chunking than the top two."}],"updated":"2026-08-10","api":"https://modelsagree.com/api/v1/best/best-long-context-embedding-apis-for-document-rag.json"}],"page":"https://modelsagree.com/product/google-gemini-embedding-001","check":"https://modelsagree.com/check?q=Google%20gemini-embedding-001","updated":"2026-08-10T18:18:45.051Z","attribution":"modelsagree.com, CC BY 4.0"}