Best embeddings model API
4 models · updated 2026-07-13
The verdict
Voyage AI leads — 2 of 4 models rank Voyage AI the top pick.
Not unanimous: Gemini picks Cohere Embed; Grok picks Gemini Embedding.
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).
Combined ranking
- 1GPT #1Claude #1Gemini #2Grok #2
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
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GPT 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
Claude 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
Gemini 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.
Grok Leads domain-specific (code, technical, RAG) and specialized retrieval benchmarks with very long context (32K) and strong precision
Where it falls shortper GPT Hosted, proprietary, and text-only—not for self-hosted or multimodal retrieval
per Claude 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.
per Gemini More expensive per token with lower rate limits than commodity API providers, and lacks global multi-region cloud provider availability.
per Grok Broaden multilingual and multimodal capabilities to match general-purpose leaders
- 2GPT #3Claude #4Gemini #4Grok #1
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)
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Grok 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)
GPT 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
Claude 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
Gemini 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.
Where it falls shortper GPT Its $0.20/M-token text price and 8K context make it poor value for high-volume, text-only embedding
per Claude 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.
per Gemini Higher retrieval latency and API call overhead compared to text-only models, and requires dependency on Google Cloud/Vertex AI infrastructure.
per Grok Lower pricing to compete with cheaper alternatives on cost-sensitive workloads
- 3GPT #5Claude #3Gemini #1Grok #4
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.
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Gemini 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.
Claude 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
Grok Strong multilingual support across 100+ languages, balanced enterprise performance, and competitive pricing with solid retrieval scores
GPT 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
Where it falls shortper GPT Pure text retrieval quality and price-performance trail the leaders—not for teams optimizing solely for best text recall per dollar
per Claude 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.
per Gemini Ranks lower in specialized code retrieval compared to technical domain-specific models, and has a complex pricing structure for multimodal queries.
per Grok Increase max sequence length and push further on specialized domain accuracy
- 4GPT —Claude #2Gemini #3Grok #3
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
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Claude 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
Gemini 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.
Grok Excellent general-purpose reliability, massive ecosystem/integration ease, consistent high performance, and enterprise trust
Where it falls shortper Claude 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.
per Gemini Ranks below specialized competitors on deep multilingual and long-context complex retrieval tasks, and lacks multimodal input support.
per Grok Improve context length and reduce cost for large-scale deployments
- 5GPT #2Claude —Gemini —Grok —
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
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GPT 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
Where it falls shortper GPT Newer provider whose strongest benchmark evidence remains substantially vendor-produced—not the lowest-risk enterprise default
- 6GPT #4Claude —Gemini —Grok #5
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
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GPT 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
Grok Outstanding value with long-document handling, competitive benchmarks at low cost, and strong open/API hybrid options
Where it falls shortper GPT CC BY-NC licensing restricts commercial self-hosting without separate terms
per Grok Enhance overall retrieval leadership and multimodal depth to overtake specialists
- 7GPT —Claude #5Gemini #5Grok —
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
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Claude 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
Gemini 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.
Where it falls shortper Claude 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.
per Gemini Requires substantial GPU memory (up to 8B parameters) and operational overhead to host locally at production scale.
Rank history
Just missed the top 5
GPT Qwen3-Embedding-8B — excellent Apache-licensed multilingual quality, but heavier to operate and hosted API availability is fragmented · OpenAI Embeddings — mature and easy to integrate, but now surpassed on retrieval quality, modalities, context, and value
Claude Jina Embeddings — solid open multimodal + late-interaction option, but general text retrieval trails Voyage/Cohere and the API business is smaller-scale
Gemini BAAI bge-m3 — excelled in hybrid search but lacks the instruction-following capabilities and raw accuracy of newer open-weights models like Qwen3-Embedding · Nomic Embed — highly cost-effective and fully auditable but lags behind the top proprietary models in multilingual performance and complex retrieval accuracy
Grok Qwen3 Embedding — top open-source MTEB but primarily self-hosted, less seamless API · NVIDIA NV-Embed — strong leaderboard but commercial restrictions and hosting complexity
By model
ChatGPT
- 1.Voyage AI
- 2.ZeroEntropy
- 3.Gemini Embedding
- 4.Jina Embeddings
- 5.Cohere Embed
Claude
- 1.Voyage AI
- 2.OpenAI Embeddings
- 3.Cohere Embed
- 4.Gemini Embedding
- 5.Qwen3-Embedding
Gemini
- 1.Cohere Embed
- 2.Voyage AI
- 3.OpenAI Embeddings
- 4.Gemini Embedding
- 5.Qwen3-Embedding
Grok
- 1.Gemini Embedding
- 2.Voyage AI
- 3.OpenAI Embeddings
- 4.Cohere Embed
- 5.Jina Embeddings
Common questions
What is the best embeddings model api according to AI models?
Voyage AI leads. 2 of 4 models rank Voyage AI the top pick. The current top 3: Voyage AI, Gemini Embedding, Cohere Embed. Ranked by asking ChatGPT, Claude, Gemini, Grok the same buying question and merging their top-5 picks, updated 2026-07-13. Source: modelsagree.com.
Which embeddings model api did each AI model pick first?
ChatGPT: Voyage AI. Claude: Voyage AI. Gemini: Cohere Embed. Grok: Gemini Embedding.
Do the AI models agree on the best embeddings model api?
Not unanimous. Gemini picks Cohere Embed; Grok picks Gemini Embedding.
What changed in the latest embeddings model api ranking?
In the latest poll (2026-07-13): Gemini Embedding climbed 3 spots, Jina Embeddings climbed 2 spots; Cohere Embed dropped 1 spot; ZeroEntropy and Qwen3-Embedding entered the ranking. The models are re-polled on demand, so this ranking moves.
How is this embeddings model api ranking made?
ChatGPT, Claude, Gemini, Grok are each asked the same buying question in a fresh session with no system steering. Their top-5 answers are merged (rank 1 = 5 pts … rank 5 = 1 pt) into the consensus ranking, re-polled on demand and tracked over time.
More on how polling works: full methodology →
Cite this ranking
ModelsAgree, “Best embeddings model API” — merged ranking from ChatGPT, Claude, Gemini & Grok, polled 2026-07-13. https://modelsagree.com/best/best-embeddings-model-api (CC BY 4.0)
Tracked by ModelsAgree · rank 1 = 5 pts … rank 5 = 1 pt · re-polled on demand