{"slug":"best-embeddings-model-api","title":"Best embeddings model API","question":"What are the best embeddings model API?","verdict":"As of 2026-07-13, ChatGPT, Claude, Gemini and Grok collectively rank Voyage AI #1 for embeddings model api on ModelsAgree by aggregate score. The models' case: Best overall for text-first RAG: elite retrieval quality, strong multilingual performance, flexible dimensions, $0.12/M tokens, and an unusually generous free allowance. The models' main caveat: Hosted, proprietary, and text-only—not for self-hosted or multimodal retrieval. The strongest alternative is Gemini Embedding — Tops or near-tops cross-lingual, long-context, multimodal, and all-rounder benchmarks with superior key information retrieval and broad modality. Not unanimous: Gemini picks Cohere Embed; Grok picks Gemini Embedding. Source: https://modelsagree.com/best/best-embeddings-model-api (modelsagree.com, CC BY 4.0).","category":"RAG","url":"https://modelsagree.com/best/best-embeddings-model-api","updated":"2026-07-13","models":["ChatGPT","Claude","Gemini","Grok"],"consensus":"2 of 4 models rank Voyage AI the top pick","disagreement":"Gemini picks Cohere Embed; Grok picks Gemini Embedding","combined":[{"rank":1,"product":"Voyage AI","domain":"voyageai.com","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"},{"rank":2,"product":"Gemini Embedding","domain":"deepmind.google","score":12,"appearances":4,"modelRanks":{"ChatGPT":3,"Claude":4,"Gemini":4,"Grok":1},"reason":"Tops or near-tops cross-lingual, long-context, multimodal, and all-rounder benchmarks with superior key information retrieval and broad modality support (text/image/video/audio/PDF)"},{"rank":3,"product":"Cohere Embed","domain":"cohere.com","score":11,"appearances":4,"modelRanks":{"ChatGPT":5,"Claude":3,"Gemini":1,"Grok":4},"reason":"Exceptional multilingual retrieval and search accuracy in production, featuring built-in compression (binary and int8 quantization) to drastically reduce index storage costs, and Matryoshka dimension scaling."},{"rank":4,"product":"OpenAI Embeddings","domain":"openai.com","score":10,"appearances":3,"modelRanks":{"Claude":2,"Gemini":3,"Grok":3},"reason":"The safe default — dirt cheap ($0.13/M tokens, and -3-small at $0.02/M), extremely reliable infrastructure, shortening-friendly dimensions, and first-class support in virtually every framework, vector DB, and tutorial; if your team already uses OpenAI, the integration cost is near zero"},{"rank":5,"product":"ZeroEntropy","domain":"zeroentropy.dev","score":4,"appearances":1,"modelRanks":{"ChatGPT":2},"reason":"Exceptional retrieval value at $0.05/M tokens, leading graded-relevance results, multilingual support, flexible 40–2560 dimensions, and open weights; could rank first once its results have broader independent confirmation"},{"rank":6,"product":"Jina Embeddings","domain":"jina.ai","score":3,"appearances":2,"modelRanks":{"ChatGPT":4,"Grok":5},"reason":"Outstanding efficiency: near-frontier multilingual retrieval from a 677M-parameter model, 32K context, task-specific adapters, robust dimension truncation, and both hosted-API and self-hosting paths"},{"rank":7,"product":"Qwen3-Embedding","domain":"qwen.ai","score":2,"appearances":2,"modelRanks":{"Claude":5,"Gemini":5},"reason":"The open-weights champion — Apache-2.0 licensed, at or near the top of MTEB multilingual, instruction-tunable, and you can start on a cheap hosted API then move in-house for data privacy or cost at scale, an exit path no proprietary option offers"}],"perModel":{"ChatGPT":[{"rank":1,"product":"Voyage AI","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","fix":"Hosted, proprietary, and text-only—not for self-hosted or multimodal retrieval"},{"rank":2,"product":"ZeroEntropy","reason":"Exceptional retrieval value at $0.05/M tokens, leading graded-relevance results, multilingual support, flexible 40–2560 dimensions, and open weights; could rank first once its results have broader independent confirmation","fix":"Newer provider whose strongest benchmark evidence remains substantially vendor-produced—not the lowest-risk enterprise default"},{"rank":3,"product":"Gemini Embedding","reason":"Strongest general multimodal API, unifying text, images, PDFs, audio, and video across 100+ languages; excellent multilingual and code retrieval make it a near-tie for first when the corpus is not text-only","fix":"Its $0.20/M-token text price and 8K context make it poor value for high-volume, text-only embedding"},{"rank":4,"product":"Jina Embeddings","reason":"Outstanding efficiency: near-frontier multilingual retrieval from a 677M-parameter model, 32K context, task-specific adapters, robust dimension truncation, and both hosted-API and self-hosting paths","fix":"CC BY-NC licensing restricts commercial self-hosting without separate terms"},{"rank":5,"product":"Cohere Embed","reason":"A production-friendly multilingual, multimodal model with an exceptional 128K context window, mixed text-image/PDF input, flexible dimensions, compression options, and strong enterprise deployment choices","fix":"Pure text retrieval quality and price-performance trail the leaders—not for teams optimizing solely for best text recall per dollar"}],"Claude":[{"rank":1,"product":"Voyage AI","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","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."},{"rank":2,"product":"OpenAI Embeddings","reason":"The safe default — dirt cheap ($0.13/M tokens, and -3-small at $0.02/M), extremely reliable infrastructure, shortening-friendly dimensions, and first-class support in virtually every framework, vector DB, and tutorial; if your team already uses OpenAI, the integration cost is near zero","fix":"Quality has stagnated — unchanged since early 2024 and now measurably behind Voyage, Cohere, and Gemini on retrieval and multilingual benchmarks, so you're trading accuracy for convenience."},{"rank":3,"product":"Cohere Embed","reason":"The strongest enterprise package — natively multimodal (text + images/PDF pages), ~128k-token context for long documents, excellent multilingual coverage, built-in int8/binary compression, and deployable via AWS Bedrock, Azure, or private VPC where data residency matters; near-tie with Gemini Embedding on pure text quality","fix":"Pricier than rivals and its raw text-retrieval edge over Voyage is unclear, so solo devs and cost-sensitive startups get less for their money than enterprises do."},{"rank":4,"product":"Gemini Embedding","reason":"Top-tier MTEB multilingual scores since launch, a genuinely generous free tier that makes it the best zero-budget starting point, Matryoshka dimensions, and clean integration for teams already on Gemini or Vertex AI","fix":"Text-only, and production use pushes you into Google Cloud/Vertex quotas, batch quirks, and ecosystem lock-in that's heavier than a simple standalone API."},{"rank":5,"product":"Qwen3-Embedding","reason":"The open-weights champion — Apache-2.0 licensed, at or near the top of MTEB multilingual, instruction-tunable, and you can start on a cheap hosted API then move in-house for data privacy or cost at scale, an exit path no proprietary option offers","fix":"The API experience is fragmented across third-party hosts with varying reliability and no single accountable vendor — teams wanting a polished managed service with SLAs should look higher up this list."}],"Gemini":[{"rank":1,"product":"Cohere Embed","reason":"Exceptional multilingual retrieval and search accuracy in production, featuring built-in compression (binary and int8 quantization) to drastically reduce index storage costs, and Matryoshka dimension scaling.","fix":"Ranks lower in specialized code retrieval compared to technical domain-specific models, and has a complex pricing structure for multimodal queries."},{"rank":2,"product":"Voyage AI","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.","fix":"More expensive per token with lower rate limits than commodity API providers, and lacks global multi-region cloud provider availability."},{"rank":3,"product":"OpenAI Embeddings","reason":"Industry-standard integration with near-universal developer ecosystem support, offering a strong price-to-performance ratio and native Matryoshka dimension truncation with minimal accuracy loss.","fix":"Ranks below specialized competitors on deep multilingual and long-context complex retrieval tasks, and lacks multimodal input support."},{"rank":4,"product":"Gemini Embedding","reason":"Native multimodal support mapping text, images, video, audio, and documents (PDFs) into a unified vector space, paired with strong long-context performance up to 32k tokens.","fix":"Higher retrieval latency and API call overhead compared to text-only models, and requires dependency on Google Cloud/Vertex AI infrastructure."},{"rank":5,"product":"Qwen3-Embedding","reason":"The premier open-weights text embedding model series with support for 100+ languages, instruction-aware embeddings, and the ability to self-host to keep sensitive data within a secure network boundary.","fix":"Requires substantial GPU memory (up to 8B parameters) and operational overhead to host locally at production scale."}],"Grok":[{"rank":1,"product":"Gemini Embedding","reason":"Tops or near-tops cross-lingual, long-context, multimodal, and all-rounder benchmarks with superior key information retrieval and broad modality support (text/image/video/audio/PDF)","fix":"Lower pricing to compete with cheaper alternatives on cost-sensitive workloads"},{"rank":2,"product":"Voyage AI","reason":"Leads domain-specific (code, technical, RAG) and specialized retrieval benchmarks with very long context (32K) and strong precision","fix":"Broaden multilingual and multimodal capabilities to match general-purpose leaders"},{"rank":3,"product":"OpenAI Embeddings","reason":"Excellent general-purpose reliability, massive ecosystem/integration ease, consistent high performance, and enterprise trust","fix":"Improve context length and reduce cost for large-scale deployments"},{"rank":4,"product":"Cohere Embed","reason":"Strong multilingual support across 100+ languages, balanced enterprise performance, and competitive pricing with solid retrieval scores","fix":"Increase max sequence length and push further on specialized domain accuracy"},{"rank":5,"product":"Jina Embeddings","reason":"Outstanding value with long-document handling, competitive benchmarks at low cost, and strong open/API hybrid options","fix":"Enhance overall retrieval leadership and multimodal depth to overtake specialists"}]},"missedByModel":{"ChatGPT":[{"product":"Qwen3-Embedding-8B","reason":"excellent Apache-licensed multilingual quality, but heavier to operate and hosted API availability is fragmented"},{"product":"OpenAI Embeddings","reason":"mature and easy to integrate, but now surpassed on retrieval quality, modalities, context, and value"}],"Claude":[{"product":"Jina Embeddings","reason":"solid open multimodal + late-interaction option, but general text retrieval trails Voyage/Cohere and the API business is smaller-scale"}],"Gemini":[{"product":"BAAI bge-m3","reason":"excelled in hybrid search but lacks the instruction-following capabilities and raw accuracy of newer open-weights models like Qwen3-Embedding"},{"product":"Nomic Embed","reason":"highly cost-effective and fully auditable but lags behind the top proprietary models in multilingual performance and complex retrieval accuracy"}],"Grok":[{"product":"Qwen3 Embedding","reason":"top open-source MTEB but primarily self-hosted, less seamless API"},{"product":"NVIDIA NV-Embed","reason":"strong leaderboard but commercial restrictions and hosting complexity"}]}}