{"slug":"openai-text-embedding-3-large","name":"OpenAI text-embedding-3-large","domain":"openai.com","verdict":"As of 2026-08-10, ChatGPT, Claude, Gemini, Grok collectively rank OpenAI text-embedding-3-large #4 of 11 for code embedding apis for repository search (one of 3 leaderboards it appears on). Source: https://modelsagree.com/product/openai-text-embedding-3-large (modelsagree.com, CC BY 4.0).","best_rank":4,"categories":3,"entries":[{"slug":"best-code-embedding-apis-for-repository-search","title":"Best code embedding APIs for repository search","rank":4,"of":11,"score":4,"appearances":2,"modelRanks":{"Claude":4,"Gemini":4},"reason":"Not code-specialized but a dependable, ubiquitous default — good enough retrieval on mixed code+doc corpora, dimension shortening, and the widest ecosystem/vendor-DB integration, making it the lowest-friction path to a working repo search.","reasons":[{"model":"Claude","reason":"Not code-specialized but a dependable, ubiquitous default — good enough retrieval on mixed code+doc corpora, dimension shortening, and the widest ecosystem/vendor-DB integration, making it the lowest-friction path to a working repo search."},{"model":"Gemini","reason":"Ubiquitous ecosystem integration, high semantic strength across mixed text/code docs, and Matryoshka dimension shortening to optimize vector storage costs."}],"fixes":[{"model":"Claude","fix":"A general-purpose model that measurably trails dedicated code embedders on pure code-to-code and NL-to-code retrieval; the wrong pick if search quality on code specifically is the priority."},{"model":"Gemini","fix":"General-purpose text focus causes lower precision on fine-grained syntax and exact symbol lookups compared to dedicated code models."}],"updated":"2026-08-10","rank_history":{"days":["2026-08-03","2026-08-10"],"ranks":[3,null]},"api":"https://modelsagree.com/api/v1/best/best-code-embedding-apis-for-repository-search.json"},{"slug":"best-multilingual-embedding-api-for-semantic-search","title":"Best multilingual embedding API for semantic search","rank":12,"of":13,"score":1,"appearances":1,"modelRanks":{"ChatGPT":5},"reason":"Dependable multilingual semantic search, adjustable dimensions, simple integration, and broad ecosystem support make it a safe operational choice when teams already use OpenAI.","reasons":[{"model":"ChatGPT","reason":"Dependable multilingual semantic search, adjustable dimensions, simple integration, and broad ecosystem support make it a safe operational choice when teams already use OpenAI."}],"fixes":[{"model":"ChatGPT","fix":"Its aging retrieval quality and price-performance no longer match the 2026 leaders, particularly for demanding cross-lingual search."}],"updated":"2026-07-17","api":"https://modelsagree.com/api/v1/best/best-multilingual-embedding-api-for-semantic-search.json"},{"slug":"best-long-context-embedding-apis-for-document-rag","title":"Best long-context embedding APIs for document RAG","rank":15,"of":15,"score":1,"appearances":1,"modelRanks":{"Claude":5},"reason":"The dependable baseline — good quality, dimension-shortening support, enormous ecosystem/tooling/vector-DB integration, and the lowest-friction path for teams already on OpenAI. Cheap, stable, well-documented.","reasons":[{"model":"Claude","reason":"The dependable baseline — good quality, dimension-shortening support, enormous ecosystem/tooling/vector-DB integration, and the lowest-friction path for teams already on OpenAI. Cheap, stable, well-documented."}],"fixes":[{"model":"Claude","fix":"Only ~8K context and now a generation behind on retrieval benchmarks — not a long-context specialist, so document-heavy RAG leans harder on your chunking/reranking than the leaders do."}],"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/openai-text-embedding-3-large","check":"https://modelsagree.com/check?q=OpenAI%20text-embedding-3-large","updated":"2026-08-10T18:18:45.051Z","attribution":"modelsagree.com, CC BY 4.0"}