{"slug":"voyage-ai","name":"Voyage AI","domain":"voyageai.com","verdict":"As of 2026-07-13, ChatGPT, Claude, Gemini, Grok collectively rank Voyage AI first for embeddings model api (one of 4 leaderboards it appears on). Source: https://modelsagree.com/product/voyage-ai (modelsagree.com, CC BY 4.0).","best_rank":1,"categories":4,"brief":{"category":"best-embeddings-model-api","title":"Best embeddings model API","rank":1,"of":7,"top":null,"day":"2026-07-16","why":[{"t":"elite retrieval quality","m":["ChatGPT","Claude","Gemini","Grok"],"q":"elite retrieval quality"},{"t":"domain-tuned variants","m":["Claude","Gemini","Grok"],"q":"offers domain-tuned variants (voyage-code-3, finance, law) that meaningfully beat general models"},{"t":"very long context","m":["Gemini","Grok"],"q":"very long context (32K)"},{"t":"strong multilingual performance","m":["ChatGPT"],"q":"strong multilingual performance"}],"gap":[],"fix":[{"t":"multilingual and multimodal capabilities","m":["ChatGPT","Grok"],"q":"Broaden multilingual and multimodal capabilities to match general-purpose leaders"},{"t":"Hosted, proprietary","m":["ChatGPT","Claude"],"q":"Hosted, proprietary, and text-only—not for self-hosted or multimodal retrieval"},{"t":"lacks global multi-region cloud provider availability","m":["Claude","Gemini"],"q":"lacks global multi-region cloud provider availability"}]},"entries":[{"slug":"best-embeddings-model-api","title":"Best embeddings model API","rank":1,"of":7,"score":18,"appearances":4,"modelRanks":{"ChatGPT":1,"Claude":1,"Gemini":2,"Grok":2},"reason":"Best overall for text-first RAG: elite retrieval quality, strong multilingual performance, flexible dimensions, $0.12/M tokens, and an unusually generous free allowance; near-tied with zembed-1, but has the safer production record","reasons":[{"model":"ChatGPT","reason":"Best overall for text-first RAG: elite retrieval quality, strong multilingual performance, flexible dimensions, $0.12/M tokens, and an unusually generous free allowance; near-tied with zembed-1, but has the safer production record"},{"model":"Claude","reason":"Consistently the best retrieval quality per dollar for RAG — tops real-world retrieval evals ahead of OpenAI and Cohere, offers domain-tuned variants (voyage-code-3, finance, law) that meaningfully beat general models, and supports Matryoshka dimensions plus int8/binary quantization to cut vector-DB cost; assumes the typical practitioner is building retrieval/RAG, which is what embeddings APIs are mostly for"},{"model":"Gemini","reason":"Best-in-class retrieval precision on technical documents, codebases, and custom enterprise domains, featuring a large 32k context window and dedicated models optimized for coding tasks."},{"model":"Grok","reason":"Leads domain-specific (code, technical, RAG) and specialized retrieval benchmarks with very long context (32K) and strong precision"}],"fixes":[{"model":"ChatGPT","fix":"Hosted, proprietary, and text-only—not for self-hosted or multimodal retrieval"},{"model":"Claude","fix":"Owned by MongoDB since 2025, so long-term neutrality and roadmap are a bet — and it lacks OpenAI's ubiquity, so some frameworks/tutorials need manual wiring; not the pick if you need one vendor for LLM + embeddings."},{"model":"Gemini","fix":"More expensive per token with lower rate limits than commodity API providers, and lacks global multi-region cloud provider availability."},{"model":"Grok","fix":"Broaden multilingual and multimodal capabilities to match general-purpose leaders"}],"updated":"2026-07-13","rank_history":{"days":["2026-06-29","2026-06-30","2026-07-08","2026-07-09","2026-07-10","2026-07-12","2026-07-13"],"ranks":[1,1,1,1,1,1,1]},"api":"https://modelsagree.com/api/v1/best/best-embeddings-model-api.json"},{"slug":"best-semantic-search-apis-for-multilingual-knowledge-bases","title":"Best semantic search APIs for multilingual knowledge bases","rank":2,"of":13,"score":7,"appearances":2,"modelRanks":{"Claude":2,"Gemini":3},"reason":"Consistently near the top of retrieval-focused leaderboards for embedding quality, with a dedicated multilingual model, long context, strong domain variants, and a competent reranker; Matryoshka/quantized outputs cut storage. Now backed by MongoDB, so tight Atlas Vector Search integration is a real path for practitioners already there.","reasons":[{"model":"Claude","reason":"Consistently near the top of retrieval-focused leaderboards for embedding quality, with a dedicated multilingual model, long context, strong domain variants, and a competent reranker; Matryoshka/quantized outputs cut storage. Now backed by MongoDB, so tight Atlas Vector Search integration is a real path for practitioners already there."},{"model":"Gemini","reason":"Provides top-tier dense retrieval accuracy for multilingual knowledge bases with voyage-multilingual-2, offering superior context window handling and benchmark performance for specialized multilingual enterprise RAG workflows."}],"fixes":[{"model":"Claude","fix":"Smaller ecosystem and heavier gravitational pull toward the MongoDB stack; less of a turnkey end-to-end search platform than the bigger clouds, so you assemble more of the pipeline yourself."},{"model":"Gemini","fix":"Lacks a broader platform ecosystem, offering no native vector database storage, hybrid BM25 text index, or integrated reranking pipeline out of the box."}],"updated":"2026-08-07","api":"https://modelsagree.com/api/v1/best/best-semantic-search-apis-for-multilingual-knowledge-bases.json"},{"slug":"best-semantic-search-apis-for-rag-applications","title":"Best semantic search APIs for RAG applications","rank":4,"of":10,"score":8,"appearances":2,"modelRanks":{"Claude":2,"Gemini":2},"reason":"Consistently at or near the top of retrieval benchmarks with domain-tuned embeddings (code, finance, law) and strong rerankers at aggressive pricing; MongoDB's acquisition gave it enterprise stability and tight Atlas Vector Search integration. Near-tie with Cohere — Cohere wins on reranker maturity and multicloud distribution, Voyage often wins on raw embedding quality per dollar.","reasons":[{"model":"Claude","reason":"Consistently at or near the top of retrieval benchmarks with domain-tuned embeddings (code, finance, law) and strong rerankers at aggressive pricing; MongoDB's acquisition gave it enterprise stability and tight Atlas Vector Search integration. Near-tie with Cohere — Cohere wins on reranker maturity and multicloud distribution, Voyage often wins on raw embedding quality per dollar."},{"model":"Gemini","reason":"It is the top-performing embedding and reranking API for technical, financial, and legal domains, representing a near-tie with Cohere; it is chosen for applications where complex domain-specific jargon or long-context documents require maximum semantic retrieval precision."}],"fixes":[{"model":"Claude","fix":"Narrower ecosystem and fewer deployment options than Cohere; if you're not on MongoDB/Atlas the integration advantage evaporates."},{"model":"Gemini","fix":"It is a proprietary, closed-source service with fewer broad ecosystem integrations than Cohere, and the quality gains over cheaper models are less pronounced on simple, general-domain English text."}],"updated":"2026-07-16","api":"https://modelsagree.com/api/v1/best/best-semantic-search-apis-for-rag-applications.json"},{"slug":"best-multilingual-embedding-api-for-semantic-search","title":"Best multilingual embedding API for semantic search","rank":13,"of":13,"score":1,"appearances":1,"modelRanks":{"Grok":5},"reason":"High retrieval quality on technical/multilingual benchmarks, competitive pricing, long context options, domain specialization options, strong practitioner value for quality-sensitive RAG where self-hosting isn't mandatory.","reasons":[{"model":"Grok","reason":"High retrieval quality on technical/multilingual benchmarks, competitive pricing, long context options, domain specialization options, strong practitioner value for quality-sensitive RAG where self-hosting isn't mandatory."}],"fixes":[{"model":"Grok","fix":"More English/technical focus than pure multilingual leaders; commercial API pricing adds up at massive scale vs open alternatives."}],"updated":"2026-07-17","api":"https://modelsagree.com/api/v1/best/best-multilingual-embedding-api-for-semantic-search.json"}],"page":"https://modelsagree.com/product/voyage-ai","check":"https://modelsagree.com/check?q=Voyage%20AI","updated":"2026-08-10T18:18:45.051Z","attribution":"modelsagree.com, CC BY 4.0"}