{"slug":"gemini-embedding","name":"Gemini Embedding","domain":"deepmind.google","verdict":"As of 2026-07-13, ChatGPT, Claude, Gemini, Grok collectively rank Gemini Embedding #2 of 7 for embeddings model api (one of 2 leaderboards it appears on). Source: https://modelsagree.com/product/gemini-embedding (modelsagree.com, CC BY 4.0).","best_rank":2,"categories":2,"brief":{"category":"best-embeddings-model-api","title":"Best embeddings model API","rank":2,"of":7,"top":"Voyage AI","day":"2026-07-17","why":[{"t":"native multimodal support","m":["Grok","ChatGPT","Gemini"],"q":"Native multimodal support mapping text, images, video, audio, and documents (PDFs) into a unified vector space"},{"t":"strong multilingual retrieval","m":["Grok","ChatGPT","Claude"],"q":"excellent multilingual and code retrieval"},{"t":"long-context performance","m":["Grok","Gemini"],"q":"strong long-context performance up to 32k tokens"},{"t":"generous free tier","m":["Claude"],"q":"a genuinely generous free tier that makes it the best zero-budget starting point"}],"gap":[{"t":"domain-tuned variants","m":["Claude","Gemini","Grok"],"q":"offers domain-tuned variants (voyage-code-3, finance, law) that meaningfully beat general models"},{"t":"best retrieval quality per dollar","m":["ChatGPT","Claude"],"q":"Consistently the best retrieval quality per dollar for RAG"},{"t":"int8 and binary quantization","m":["Claude"],"q":"supports Matryoshka dimensions plus int8/binary quantization to cut vector-DB cost"}],"fix":[{"t":"poor value for high-volume workloads","m":["ChatGPT","Grok"],"q":"poor value for high-volume, text-only embedding"},{"t":"Google Cloud and Vertex dependency","m":["Claude","Gemini"],"q":"requires dependency on Google Cloud/Vertex AI infrastructure"},{"t":"higher retrieval latency and API overhead","m":["Gemini"],"q":"Higher retrieval latency and API call overhead compared to text-only models"}]},"entries":[{"slug":"best-embeddings-model-api","title":"Best embeddings model API","rank":2,"of":7,"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)","reasons":[{"model":"Grok","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)"},{"model":"ChatGPT","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"},{"model":"Claude","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"},{"model":"Gemini","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."}],"fixes":[{"model":"ChatGPT","fix":"Its $0.20/M-token text price and 8K context make it poor value for high-volume, text-only embedding"},{"model":"Claude","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."},{"model":"Gemini","fix":"Higher retrieval latency and API call overhead compared to text-only models, and requires dependency on Google Cloud/Vertex AI infrastructure."},{"model":"Grok","fix":"Lower pricing to compete with cheaper alternatives on cost-sensitive workloads"}],"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":[4,4,3,5,2,5,3]},"api":"https://modelsagree.com/api/v1/best/best-embeddings-model-api.json"},{"slug":"best-multilingual-embedding-api-for-semantic-search","title":"Best multilingual embedding API for semantic search","rank":5,"of":13,"score":4,"appearances":1,"modelRanks":{"Claude":2},"reason":"Sits at or near the top of MTEB/MMTEB multilingual leaderboards among commercial APIs, covers 100+ languages, supports Matryoshka truncation, and is aggressively cheap with generous free-tier access; near-tie with Cohere for the top spot, edged out only on retrieval-specific tooling and deployment breadth.","reasons":[{"model":"Claude","reason":"Sits at or near the top of MTEB/MMTEB multilingual leaderboards among commercial APIs, covers 100+ languages, supports Matryoshka truncation, and is aggressively cheap with generous free-tier access; near-tie with Cohere for the top spot, edged out only on retrieval-specific tooling and deployment breadth."}],"fixes":[{"model":"Claude","fix":"Rate limits and Google Cloud's quota/billing friction make it clumsier for high-throughput ingestion pipelines, and there's no self-host option."}],"updated":"2026-07-17","api":"https://modelsagree.com/api/v1/best/best-multilingual-embedding-api-for-semantic-search.json"}],"page":"https://modelsagree.com/product/gemini-embedding","check":"https://modelsagree.com/check?q=Gemini%20Embedding","updated":"2026-08-10T18:18:45.051Z","attribution":"modelsagree.com, CC BY 4.0"}