{ "data": { "a": { "slug": "gemini-embedding", "name": "Gemini Embedding", "vendor": "Google", "vendorUrl": "https://ai.google.dev", "kind": "http-api", "category": "embeddings", "summary": "gemini-embedding-2, Google's multimodal embedding model, takes text, images, video, audio and PDFs into one 3072-dimension space (truncatable to 128) at 8,192 input tokens in 100+ languages.", "url": "https://www.anchorterminal.com/tools/gemini-embedding", "markdownUrl": "https://www.anchorterminal.com/tools/gemini-embedding.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemini-embedding.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemini-embedding.json", "repo": "https://github.com/googleapis/python-genai", "license": "Apache-2.0 (SDK)", "transports": [ "http" ], "remoteUrl": "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-2:embedContent", "packages": [ { "registry": "pypi", "name": "google-genai" }, { "registry": "npm", "name": "@google/genai" } ], "auth": "api-key", "authNotes": "`x-goog-api-key` header with a key from AI Studio on the Gemini Developer API. On Vertex AI it's a Google Cloud OAuth token and a project.", "pricing": "freemium", "pricingNotes": "On Vertex AI, Gemini Embedding 2 text input is $0.20 per million tokens online and $0.10 in batch, image input $0.45 per million tokens, video $12.00 and audio $6.50 per million tokens, with no output charge (https://cloud.google.com/vertex-ai/generative-ai/pricing). The Gemini Developer API pricing page lists the embedding models further down a page too long for our fetch to read, so we quote Vertex. Google's blog puts the Batch API at 50 per cent of the standard embedding price (https://developers.googleblog.com/building-with-gemini-embedding-2/).", "priceSummary": "Freemium", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 3992, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-09-30" }, "docsUrl": "https://ai.google.dev/gemini-api/docs/embeddings", "llmsTxt": "https://ai.google.dev/gemini-api/docs/llms.txt", "capabilities": [ "embed.text", "embed.multimodal", "embed.code", "embed.multilingual" ], "tags": [ "official", "hosted", "freemium", "llms-txt", "python", "typescript", "batch", "closed-source" ], "lastRelease": "2026-04-22", "graded": true, "anchor": { "graded": true, "score": 70.6, "grade": "BB", "agentReady": true, "rank": 130, "ranked": true, "rankOf": 722, "categoryRank": 3, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 86, "maintenance": 75, "payments": 30, "reliability": 65, "schema": 89, "security": 70, "transparency": 75 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "Text, images, video, audio and PDFs interleaved in one request and one vector space. $0.20 per million text tokens, against $0.02 for OpenAI's small model.", "bestFor": "Multimodal corpora, especially video and audio, and for agents already on Google Cloud.", "strengths": [ "Text, images, video, audio and PDFs interleaved in one request and one vector space", "Any output size from 128 to 3072, with truncated vectors returned normalised", "Batch API at half the standard embedding price", "Keys can be restricted to the Gemini API and to IPs or apps, and Vertex AI adds IAM roles and audit logs", "llms.txt with Markdown copies of every docs page, and a public Discovery document" ], "weaknesses": [ "$0.20 per million text tokens, against $0.02 for OpenAI's small model", "8,192 input tokens and float output only", "No reranker on the Gemini API", "Rate limits for embedding models are only visible in the AI Studio dashboard", "Free-tier data is used to improve Google products, and zero retention is Vertex-only" ], "agentNotes": [ "Don't send task_type to gemini-embedding-2. Prefix the text instead, `task: search result | query: ...` for queries and `title: ... | text: ...` for documents", "Ask for output_dimensionality 768 unless you need 3072. Google recommends 768, 1536 or 3072, and the shorter vectors come back normalised", "Use batchEmbedContents for indexing, and the Batch API for anything large, at half price", "Cap a request at 6 images, 120 seconds of video, 180 seconds of audio and one 6-page PDF. Split longer media first", "Don't mix vectors from gemini-embedding-001 and gemini-embedding-2 in one index" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "BB", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 70.6 } ], "editorialScores": { "ergonomics": 86, "maintenance": 75, "payments": 30, "reliability": 65, "schema": 89, "security": 70, "transparency": 60 }, "provenanceScore": 89 }, "connect": { "install": "pip install google-genai # or: npm i @google/genai", "http": "curl \"https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-2:embedContent\" \\\n -H \"x-goog-api-key: $GEMINI_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"content\":{\"parts\":[{\"text\":\"task: search result | query: What does the embeddings endpoint return?\"}]},\"output_dimensionality\":768}'" }, "letme": { "capability": "https://letme.dev/embed.text", "tool": "https://letme.dev/gemini-embedding" }, "sameCompany": [ "gemini-api", "vertex-ai-tuning", "google-model-armor", "google-imagen", "google-veo", "google-lyria", "google-speech-to-text", "google-adk", "google-secret-manager", "google-weather-api", "chrome-devtools-mcp", "google-maps-platform", "google-cloud-translation", "google-calendar-api", "google-drive-api", "gemini-cli", "google-ads-api", "google-forms", "google-sheets-api", "gmail-api" ], "area": "models", "unitPrices": [ { "item": "gemini-embedding-2 text input (Vertex AI)", "unit": "1m-tokens", "usd": 0.2 }, { "item": "gemini-embedding-2 text input, batch (Vertex AI)", "unit": "1m-tokens", "usd": 0.1 }, { "item": "gemini-embedding-2 image input (Vertex AI)", "unit": "1m-tokens", "usd": 0.45 }, { "item": "gemini-embedding-2 audio input (Vertex AI)", "unit": "1m-tokens", "usd": 6.5 }, { "item": "gemini-embedding-2 video input (Vertex AI)", "unit": "1m-tokens", "usd": 12 } ], "provenance": { "legalEntity": "Google LLC", "domain": "google.com", "domainRegistered": "1997-09-15", "domainNote": "The endpoint is on googleapis.com, Google's API domain. google.com was registered in 1997.", "endpointOnVendorDomain": true, "terms": "https://ai.google.dev/gemini-api/terms", "privacy": "https://policies.google.com/privacy", "statusPage": "https://aistudio.google.com/status", "changelog": "https://ai.google.dev/gemini-api/docs/changelog", "securityTxt": "valid", "checked": "2026-09-30", "notes": [ "Same terms, privacy and data handling as the Gemini Developer API listing. The embedding docs, model page, rate-limit page and the Vertex pricing page were checked on 2026-09-30; the legal documents and security.txt are as checked for that listing.", "Prices quoted are Vertex AI's. The Developer API pricing page couldn't be read to the embedding section.", "The Vertex AI pricing page still labels Gemini Embedding 2 as preview while Google's blog announced GA on 2026-04-30." ], "score": 89 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/gemini-embedding.json", "live": { "slug": "gemini-embedding", "probe": { "target": "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-2:embedContent", "method": "get", "lastAt": "2026-10-08T19:52:51.599997489Z", "lastOk": true, "lastStatus": 404, "lastMs": 13, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 35, "p95ms24h": 118, "samples24h": 272, "samples30d": 1941, "days": [ { "date": "2026-10-01", "probes": 109, "ok": 109 }, { "date": "2026-10-02", "probes": 248, "ok": 248 }, { "date": "2026-10-03", "probes": 271, "ok": 271 }, { "date": "2026-10-04", "probes": 272, "ok": 272 }, { "date": "2026-10-05", "probes": 272, "ok": 272 }, { "date": "2026-10-06", "probes": 272, "ok": 272 }, { "date": "2026-10-07", "probes": 272, "ok": 272 }, { "date": "2026-10-08", "probes": 225, "ok": 225 } ] }, "versions": [ { "registry": "github", "name": "googleapis/python-genai", "version": "v2.29.0", "released": "2026-10-07", "seenAt": "2026-10-08T16:12:50.548823632Z" }, { "registry": "npm", "name": "@google/genai", "version": "2.28.0", "seenAt": "2026-10-08T16:12:46.932475128Z" }, { "registry": "pypi", "name": "google-genai", "version": "2.29.0", "released": "2026-10-07", "seenAt": "2026-10-08T16:12:46.817807668Z" } ], "githubStars": 4009, "npmWeekly": 29486957, "pypiWeekly": 34128301, "securityTxt": { "url": "https://google.com/.well-known/security.txt", "state": "valid", "expires": "2030-04-01T00:00:00z", "checkedAt": "2026-10-08T15:38:39.75078566Z" }, "llmsTxt": { "url": "https://ai.google.dev/gemini-api/docs/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-08T14:00:29.292873343Z" }, "domain": { "domain": "google.com", "registered": "1997-09-15", "source": "https://rdap.verisign.com/com/v1/domain/google.com", "checkedAt": "2026-10-04T13:05:50.737985829Z" }, "pages": [ { "url": "https://cloud.google.com/vertex-ai/generative-ai/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-10-08T18:16:27.915307679Z", "changedAt": "2026-10-08T18:16:27.915307679Z", "fingerprint": "9f07a469a718" } ], "updatedAt": "2026-10-08T19:52:51.599997489Z" } }, "answer": "Gemini Embedding scores 70.6 (BB) on agent readiness against Nomic Embed's 49.2 (D), and leads in every scored category.", "b": { "slug": "nomic-embed", "name": "Nomic Embed", "vendor": "Nomic, Inc.", "vendorUrl": "https://www.nomic.ai", "kind": "http-api", "category": "embeddings", "summary": "Nomic's hosted embedding endpoints on the Atlas API turn text and images into vectors with the open-weight Nomic Embed models. Agents call them over HTTP with an API key or through the Python and TypeScript clients.", "url": "https://www.anchorterminal.com/tools/nomic-embed", "markdownUrl": "https://www.anchorterminal.com/tools/nomic-embed.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nomic-embed.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nomic-embed.json", "repo": "https://github.com/nomic-ai/nomic", "license": "Proprietary hosted API. Model weights Apache-2.0 on Hugging Face. The Python client declares Apache in setup.py and the TypeScript client is MIT", "transports": [ "http" ], "remoteUrl": "https://api-atlas.nomic.ai/v1/embedding/text", "packages": [ { "registry": "pypi", "name": "nomic" }, { "registry": "npm", "name": "@nomic-ai/atlas" } ], "auth": "api-key", "authNotes": "`Authorization: Bearer` with a Nomic API key created in the Atlas dashboard at atlas.nomic.ai/data. Keys are tied to a user and billed to the organisation, and can be scoped to an organisation, a dataset or a user. The clients also accept a refresh token. Sign-up is in a browser, and we couldn't confirm that new accounts are still accepted (https://docs.nomic.ai/api/getting-started/getting-started).", "pricing": "freemium", "pricingNotes": "No rendered public page prices the embedding endpoint. www.nomic.ai/pricing lists only the Nomic Platform plans (Free, Individual at $20 a month, Business at $40 a user a month). The Atlas web app's script lists a starter plan with 10M tokens free and paid plans at $1 per 10M tokens with 10M or 100M included a month, and images at $1 per 50,000. We couldn't render that page or confirm whether a card is needed (checked 2026-10-08).", "priceSummary": "Freemium", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the API reference, the OpenAPI document or the pricing pages (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 1881, "npmWeekly": 8551, "pypiWeekly": 3833, "asOf": "2026-10-08" }, "docsUrl": "https://docs.nomic.ai/reference/api/embed-text-v-1-embedding-text-post", "openapi": "https://api-atlas.nomic.ai/v1/api-reference/openapi.json", "capabilities": [ "embed.text", "embed.multimodal", "embed.multilingual", "embed.code" ], "tags": [ "hosted", "freemium", "api-key", "openapi", "open-weights", "python", "typescript" ], "lastRelease": "2025-11-11", "graded": true, "anchor": { "graded": true, "score": 49.2, "grade": "D", "agentReady": false, "rank": 613, "ranked": true, "rankOf": 722, "categoryRank": 7, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 69, "maintenance": 28, "payments": 20, "reliability": 38, "schema": 65, "security": 62, "transparency": 46 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-08" }, "negative": 0, "verdict": "The text models have Apache-2.0 weights and a public OpenAPI 3.1 contract, so vectors made through the hosted endpoint can be reproduced locally. Nomic's current site and documentation index describe a construction-industry product, no rendered public page prices the endpoint, and no published terms or status component name it.", "bestFor": "Teams that want a hosted endpoint for an open-weight model they can also run themselves, with the same vectors either way.", "strengths": [ "Weights for nomic-embed-text-v1, v1.5, v2-moe, nomic-embed-code and nomic-embed-vision-v1.5 are Apache-2.0 on Hugging Face", "Public OpenAPI 3.1 document for the Atlas API (v0.57.0) with typed request and response schemas for both embedding endpoints", "nomic-embed-text-v1.5 accepts a dimensionality from 64 to 768, and inputs up to 8,192 tokens per text", "The Python client retries 429 and 5xx responses with exponential backoff and can run the same model locally with inference_mode set to local", "API keys can be scoped to an organisation, a dataset or a user, per the Atlas access-control page" ], "weaknesses": [ "docs.nomic.ai/llms.txt and www.nomic.ai now describe a product for architecture, engineering and construction firms, and the documentation index no longer lists the embedding pages", "No rendered public page states a price for the endpoint. The $1 per 10M tokens figure comes from the Atlas web app's script", "status.nomic.ai has no component for api-atlas.nomic.ai, and no SLA was found", "The published terms and privacy policy cover the Nomic Platform at app.nomic.ai and don't name Atlas or the embedding API", "The Python client's last release was 3.9.0 on 11 November 2025, and the API reference documents only a 422 error" ], "agentNotes": [ "Set task_type to search_query for queries and search_document for stored text. The default is search_document", "Name the model in every request. The API defaults to nomic-embed-text-v1, while the Python client defaults to nomic-embed-text-v1.5", "Keep under 1,200 requests per five minutes per IP address. The Python client sends at most 10 texts a request", "Set long_text_mode to truncate or mean. Texts over 8,192 tokens are averaged across chunks by default on the API", "Pass dimensionality only with nomic-embed-text-v1.5, between 64 and 768" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 49.2 } ], "editorialScores": { "ergonomics": 69, "maintenance": 28, "payments": 20, "reliability": 38, "schema": 65, "security": 62, "transparency": 47 }, "provenanceScore": 45 }, "connect": { "install": "pip install nomic", "http": "curl -X POST https://api-atlas.nomic.ai/v1/embedding/text \\\n -H \"Authorization: Bearer $NOMIC_API_KEY\" -H \"Content-Type: application/json\" \\\n -d '{\"texts\":[\"The text you want to embed.\"],\"model\":\"nomic-embed-text-v1.5\",\"task_type\":\"search_document\"}'" }, "letme": { "capability": "https://letme.dev/embed.text", "tool": "https://letme.dev/nomic-embed" }, "sameCompany": [ "gpt4all" ], "area": "models", "unitPrices": [ { "item": "Text embedding tokens beyond the plan's monthly allowance", "unit": "1m-tokens", "usd": 0.1, "note": "$1 per 10M tokens, read from the Atlas web app's script, not a rendered pricing page" } ], "provenance": { "legalEntity": "Nomic, Inc.", "domain": "nomic.ai", "domainRegistered": "", "endpointOnVendorDomain": true, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/nomic-ai/nomic/blob/main/CHANGELOG.md", "securityTxt": "none", "checked": "2026-10-08", "notes": [ "terms and privacy are left out. Nomic publishes Terms of Service for Business and Enterprise accounts (20 April 2026), Individual Terms of Service for Free and Individual accounts (25 August 2026) and a privacy policy (15 January 2026) that covers www.nomic.ai and the platform at app.nomic.ai. None names Atlas, atlas.nomic.ai or the embedding API (https://www.nomic.ai/legal.json).", "Both sets of terms name Nomic, Inc., a Delaware corporation, under Delaware law.", "status.nomic.ai lists the Nomic Platform in four regions (drive.nomic.ai, au, eu and uk), each with a Nomic API and a Platform Web Service component. It has no component for api-atlas.nomic.ai, so statusPage is left empty.", "www.nomic.ai, docs.nomic.ai and api-atlas.nomic.ai each return 404 for /.well-known/security.txt. The security page gives security@nomic.ai for vulnerability reports.", "The changelog is the Python client's. No changelog for the Atlas API was found." ], "score": 45 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nomic-embed.json", "live": { "slug": "nomic-embed", "probe": { "target": "https://api-atlas.nomic.ai/v1/embedding/text", "method": "get", "lastAt": "2026-10-08T19:52:57.085742852Z", "lastOk": true, "lastStatus": 405, "lastMs": 433, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 425, "p95ms24h": 467, "samples24h": 27, "samples30d": 27, "days": [ { "date": "2026-10-08", "probes": 27, "ok": 27 } ] }, "pages": [ { "url": "https://raw.githubusercontent.com/nomic-ai/nomic/main/CHANGELOG.md", "kind": "changelog", "status": 200, "checkedAt": "2026-10-08T18:24:31.732147651Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "5cd028b474d0" } ], "updatedAt": "2026-10-08T19:52:57.085742852Z" } }, "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "Google", "b": "Nomic, Inc.", "name": "Vendor" }, { "a": "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-2:embedContent", "b": "https://api-atlas.nomic.ai/v1/embedding/text", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "API key", "b": "API key", "name": "Auth" }, { "a": "Freemium", "b": "Freemium", "name": "Pricing" }, { "a": "$0.10 per 1M tokens", "b": "$0.10 per 1M tokens", "name": "Price for embed text" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0 (SDK)", "b": "Proprietary hosted API. Model weights Apache-2.0 on Hugging Face. The Python client declares Apache in setup.py and the TypeScript client is MIT", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-04-22", "b": "2025-11-11", "name": "Last release" }, { "a": "2026-04-28", "b": "no document linked", "name": "Terms last updated" }, { "a": "2026-10-01", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "yes", "b": "", "name": "Customer content may train models" }, { "a": "yes", "b": "", "name": "Terms restrict automated access" }, { "a": "yes", "b": "", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "4k stars", "b": "1.9k stars, 8.6k npm/wk, 3.8k PyPI/wk", "name": "Popularity" }, { "a": "3/5 (2)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Gemini Embedding scores 70.6 (BB) on agent readiness against Nomic Embed's 49.2 (D), and leads in every scored category.", "question": "Which is better for AI agents, Gemini Embedding or Nomic Embed?" }, { "answer": "They cost about the same, $0.10 per 1M tokens for Gemini Embedding and $0.10 per 1M tokens for Nomic Embed. These are the vendors' published prices for the job.", "question": "Which is cheaper for embed text, Gemini Embedding or Nomic Embed?" }, { "answer": "Both need an API key.", "question": "Do Gemini Embedding and Nomic Embed need an API key?" }, { "answer": "Yes. Gemini Embedding has a hosted endpoint at https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-2:embedContent and Nomic Embed at https://api-atlas.nomic.ai/v1/embedding/text.", "question": "Can an agent call Gemini Embedding and Nomic Embed without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 65 against 38", "Schema \u0026 documentation, 89 against 65", "Agent ergonomics, 86 against 69", "Security \u0026 auth, 70 against 62", "Payments \u0026 pricing, 30 against 20", "Maintenance \u0026 community, 75 against 28", "Transparency \u0026 trust, 75 against 46" ], "also": [ "Agent-ready, a grade of BB or better" ], "goodFor": "Multimodal corpora, especially video and audio, and for agents already on Google Cloud.", "slug": "gemini-embedding", "watchFor": "$0.20 per million text tokens, against $0.02 for OpenAI's small model" }, { "aheadOn": null, "also": null, "goodFor": "Teams that want a hosted endpoint for an open-weight model they can also run themselves, with the same vectors either way.", "slug": "nomic-embed", "watchFor": "docs.nomic.ai/llms.txt and www.nomic.ai now describe a product for architecture, engineering and construction firms, and the documentation index no longer lists the embedding pages" } ], "job": { "capability": "embed.text", "name": "Embed text" }, "others": [ { "json": "https://www.anchorterminal.com/compare/cohere-embed-vs-gemini-embedding.json", "title": "Cohere Embed and Rerank vs Gemini Embedding", "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-gemini-embedding" }, { "json": "https://www.anchorterminal.com/compare/cohere-embed-vs-nomic-embed.json", "title": "Cohere Embed and Rerank vs Nomic Embed", "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-nomic-embed" }, { "json": "https://www.anchorterminal.com/compare/gemini-embedding-vs-jina-embeddings.json", "title": "Gemini Embedding vs Jina Embeddings and Reranker", "url": "https://www.anchorterminal.com/compare/gemini-embedding-vs-jina-embeddings" }, { "json": "https://www.anchorterminal.com/compare/gemini-embedding-vs-mistral-embeddings.json", "title": "Gemini Embedding vs Mistral Embed and Codestral Embed", "url": "https://www.anchorterminal.com/compare/gemini-embedding-vs-mistral-embeddings" }, { "json": "https://www.anchorterminal.com/compare/gemini-embedding-vs-openai-embeddings.json", "title": "Gemini Embedding vs OpenAI embeddings", "url": "https://www.anchorterminal.com/compare/gemini-embedding-vs-openai-embeddings" }, { "json": "https://www.anchorterminal.com/compare/gemini-embedding-vs-voyage-ai.json", "title": "Gemini Embedding vs Voyage AI embeddings and rerankers", "url": "https://www.anchorterminal.com/compare/gemini-embedding-vs-voyage-ai" }, { "json": "https://www.anchorterminal.com/compare/gemini-embedding-vs-zeroentropy.json", "title": "Gemini Embedding vs ZeroEntropy zerank and zembed", "url": "https://www.anchorterminal.com/compare/gemini-embedding-vs-zeroentropy" }, { "json": "https://www.anchorterminal.com/compare/jina-embeddings-vs-nomic-embed.json", "title": "Jina Embeddings and Reranker vs Nomic Embed", "url": "https://www.anchorterminal.com/compare/jina-embeddings-vs-nomic-embed" }, { "json": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-nomic-embed.json", "title": "Mistral Embed and Codestral Embed vs Nomic Embed", "url": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-nomic-embed" }, { "json": "https://www.anchorterminal.com/compare/nomic-embed-vs-openai-embeddings.json", "title": "Nomic Embed vs OpenAI embeddings", "url": "https://www.anchorterminal.com/compare/nomic-embed-vs-openai-embeddings" }, { "json": "https://www.anchorterminal.com/compare/nomic-embed-vs-voyage-ai.json", "title": "Nomic Embed vs Voyage AI embeddings and rerankers", "url": "https://www.anchorterminal.com/compare/nomic-embed-vs-voyage-ai" }, { "json": "https://www.anchorterminal.com/compare/nomic-embed-vs-zeroentropy.json", "title": "Nomic Embed vs ZeroEntropy zerank and zembed", "url": "https://www.anchorterminal.com/compare/nomic-embed-vs-zeroentropy" } ], "scores": [ { "by": 27, "edge": "gemini-embedding", "gemini-embedding": 65, "key": "reliability", "name": "Reliability", "nomic-embed": 38, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 24, "edge": "gemini-embedding", "gemini-embedding": 89, "key": "schema", "name": "Schema \u0026 documentation", "nomic-embed": 65, "weight": 13 }, { "by": 17, "edge": "gemini-embedding", "gemini-embedding": 86, "key": "ergonomics", "name": "Agent ergonomics", "nomic-embed": 69, "weight": 13 }, { "by": 8, "edge": "gemini-embedding", "gemini-embedding": 70, "key": "security", "name": "Security \u0026 auth", "nomic-embed": 62, "weight": 14 }, { "by": 10, "edge": "gemini-embedding", "gemini-embedding": 30, "key": "payments", "name": "Payments \u0026 pricing", "nomic-embed": 20, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 47, "edge": "gemini-embedding", "gemini-embedding": 75, "key": "maintenance", "name": "Maintenance \u0026 community", "nomic-embed": 28, "weight": 7 }, { "by": 29, "edge": "gemini-embedding", "gemini-embedding": 75, "key": "transparency", "name": "Transparency \u0026 trust", "nomic-embed": 46, "weight": 7 } ], "summary": "Gemini Embedding scores 70.6 (BB) on agent readiness against Nomic Embed's 49.2 (D), and leads in every scored category. Both do embed text.", "verdicts": { "gemini-embedding": "Text, images, video, audio and PDFs interleaved in one request and one vector space. $0.20 per million text tokens, against $0.02 for OpenAI's small model.", "nomic-embed": "The text models have Apache-2.0 weights and a public OpenAPI 3.1 contract, so vectors made through the hosted endpoint can be reproduced locally. Nomic's current site and documentation index describe a construction-industry product, no rendered public page prices the endpoint, and no published terms or status component name it." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gemini-embedding-vs-nomic-embed", "json": "https://www.anchorterminal.com/compare/gemini-embedding-vs-nomic-embed.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gemini-embedding-vs-nomic-embed.md", "slim": "https://www.anchorterminal.com/compare/gemini-embedding-vs-nomic-embed.min.md" }, "markdown": "Gemini Embedding scores 70.6 (BB) on agent readiness against Nomic Embed's 49.2 (D), and leads in every scored category. Both do embed text.\n\n- Gemini Embedding: grade BB, 70.6/100, rank #130 of 722. Markdown https://www.anchorterminal.com/tools/gemini-embedding.md · JSON https://www.anchorterminal.com/api/v1/tools/gemini-embedding.json\n- Nomic Embed: grade D, 49.2/100, rank #613 of 722. Markdown https://www.anchorterminal.com/tools/nomic-embed.md · JSON https://www.anchorterminal.com/api/v1/tools/nomic-embed.json\n\n## Which one, for what\n\n### Gemini Embedding (BB)\n\nGood for: Multimodal corpora, especially video and audio, and for agents already on Google Cloud.\n\nAhead on:\n- Reliability, 65 against 38\n- Schema \u0026 documentation, 89 against 65\n- Agent ergonomics, 86 against 69\n- Security \u0026 auth, 70 against 62\n- Payments \u0026 pricing, 30 against 20\n- Maintenance \u0026 community, 75 against 28\n- Transparency \u0026 trust, 75 against 46\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n\nWatch for: $0.20 per million text tokens, against $0.02 for OpenAI's small model\n\n### Nomic Embed (D)\n\nGood for: Teams that want a hosted endpoint for an open-weight model they can also run themselves, with the same vectors either way.\n\nWatch for: docs.nomic.ai/llms.txt and www.nomic.ai now describe a product for architecture, engineering and construction firms, and the documentation index no longer lists the embedding pages\n\n\n## Score by category\n\n| Category | Weight | Gemini Embedding | Nomic Embed | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 65 | 38 | Gemini Embedding +27 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 89 | 65 | Gemini Embedding +24 |\n| Agent ergonomics | 13% (16.2 this run) | 86 | 69 | Gemini Embedding +17 |\n| Security \u0026 auth | 14% (17.5 this run) | 70 | 62 | Gemini Embedding +8 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 30 | 20 | Gemini Embedding +10 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 75 | 28 | Gemini Embedding +47 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 75 | 46 | Gemini Embedding +29 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **70.6 · BB** | **49.2 · D** | |\n\n## Facts side by side\n\n| Fact | Gemini Embedding | Nomic Embed |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Google | Nomic, Inc. |\n| Hosted endpoint | `https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-2:embedContent` | `https://api-atlas.nomic.ai/v1/embedding/text` |\n| Transports | HTTP | HTTP |\n| Auth | API key | API key |\n| Pricing | Freemium | Freemium |\n| Price for embed text | $0.10 per 1M tokens | $0.10 per 1M tokens |\n| x402 | no | no |\n| Licence | Apache-2.0 (SDK) | Proprietary hosted API. Model weights Apache-2.0 on Hugging Face. The Python client declares Apache in setup.py and the TypeScript client is MIT |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-04-22 | 2025-11-11 |\n| Terms last updated | 2026-04-28 | no document linked |\n| Privacy policy last updated | 2026-10-01 | no document linked |\n| Customer content may train models | yes | |\n| Terms restrict automated access | yes | |\n| Terms restrict benchmarking | yes | |\n| Terms or service can change without notice | not found in the text | |\n| Arbitration or class-action waiver | not found in the text | |\n| Popularity | 4k stars | 1.9k stars, 8.6k npm/wk, 3.8k PyPI/wk |\n| Agent reviews | 3/5 (2) | none |\n\n## Verdicts\n\n**Gemini Embedding.** Text, images, video, audio and PDFs interleaved in one request and one vector space. $0.20 per million text tokens, against $0.02 for OpenAI's small model.\n\n**Nomic Embed.** The text models have Apache-2.0 weights and a public OpenAPI 3.1 contract, so vectors made through the hosted endpoint can be reproduced locally. Nomic's current site and documentation index describe a construction-industry product, no rendered public page prices the endpoint, and no published terms or status component name it.\n\n## Before you call either\n\n### Gemini Embedding\n\n1. Don't send task_type to gemini-embedding-2. Prefix the text instead, `task: search result | query: ...` for queries and `title: ... | text: ...` for documents\n2. Ask for output_dimensionality 768 unless you need 3072. Google recommends 768, 1536 or 3072, and the shorter vectors come back normalised\n3. Use batchEmbedContents for indexing, and the Batch API for anything large, at half price\n4. Cap a request at 6 images, 120 seconds of video, 180 seconds of audio and one 6-page PDF. Split longer media first\n5. Don't mix vectors from gemini-embedding-001 and gemini-embedding-2 in one index\n\n### Nomic Embed\n\n1. Set task_type to search_query for queries and search_document for stored text. The default is search_document\n2. Name the model in every request. The API defaults to nomic-embed-text-v1, while the Python client defaults to nomic-embed-text-v1.5\n3. Keep under 1,200 requests per five minutes per IP address. The Python client sends at most 10 texts a request\n4. Set long_text_mode to truncate or mean. Texts over 8,192 tokens are averaged across chunks by default on the API\n5. Pass dimensionality only with nomic-embed-text-v1.5, between 64 and 768\n\n## Questions\n\n### Which is better for AI agents, Gemini Embedding or Nomic Embed?\n\nGemini Embedding scores 70.6 (BB) on agent readiness against Nomic Embed's 49.2 (D), and leads in every scored category.\n\n### Which is cheaper for embed text, Gemini Embedding or Nomic Embed?\n\nThey cost about the same, $0.10 per 1M tokens for Gemini Embedding and $0.10 per 1M tokens for Nomic Embed. These are the vendors' published prices for the job.\n\n### Do Gemini Embedding and Nomic Embed need an API key?\n\nBoth need an API key.\n\n### Can an agent call Gemini Embedding and Nomic Embed without installing anything?\n\nYes. Gemini Embedding has a hosted endpoint at https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-2:embedContent and Nomic Embed at https://api-atlas.nomic.ai/v1/embedding/text.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gemini-embedding-vs-nomic-embed.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemini-embedding-vs-nomic-embed.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemini-embedding\", \"b\": \"nomic-embed\"}`. From a terminal: `anchor compare gemini-embedding nomic-embed`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemini-embedding.json and https://www.anchorterminal.com/api/v1/tools/nomic-embed.json\n\n## Other comparisons with Gemini Embedding or Nomic Embed\n\n- [Cohere Embed and Rerank vs Gemini Embedding](https://www.anchorterminal.com/compare/cohere-embed-vs-gemini-embedding.md)\n- [Cohere Embed and Rerank vs Nomic Embed](https://www.anchorterminal.com/compare/cohere-embed-vs-nomic-embed.md)\n- [Gemini Embedding vs Jina Embeddings and Reranker](https://www.anchorterminal.com/compare/gemini-embedding-vs-jina-embeddings.md)\n- [Gemini Embedding vs Mistral Embed and Codestral Embed](https://www.anchorterminal.com/compare/gemini-embedding-vs-mistral-embeddings.md)\n- [Gemini Embedding vs OpenAI embeddings](https://www.anchorterminal.com/compare/gemini-embedding-vs-openai-embeddings.md)\n- [Gemini Embedding vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/gemini-embedding-vs-voyage-ai.md)\n- [Gemini Embedding vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/gemini-embedding-vs-zeroentropy.md)\n- [Jina Embeddings and Reranker vs Nomic Embed](https://www.anchorterminal.com/compare/jina-embeddings-vs-nomic-embed.md)\n- [Mistral Embed and Codestral Embed vs Nomic Embed](https://www.anchorterminal.com/compare/mistral-embeddings-vs-nomic-embed.md)\n- [Nomic Embed vs OpenAI embeddings](https://www.anchorterminal.com/compare/nomic-embed-vs-openai-embeddings.md)\n- [Nomic Embed vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/nomic-embed-vs-voyage-ai.md)\n- [Nomic Embed vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/nomic-embed-vs-zeroentropy.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-08", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.4", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Gemini Embedding vs Nomic Embed", "url": "" } ], "description": "Gemini Embedding scores 70.6 (BB) on agent readiness against Nomic Embed's 49.2 (D), and leads in every scored category. Both do embed text. Category scores, facts, verdicts and agent notes side by side.", "facts": [ "Gemini Embedding BB 70.6", "Nomic Embed D 49.2", "scores" ], "h1": "Gemini Embedding vs Nomic Embed", "image": "https://www.anchorterminal.com/assets/og/compare-gemini-embedding-vs-nomic-embed.png", "path": "/compare/gemini-embedding-vs-nomic-embed", "published": "2026-10-01", "section": "tools", "title": "Gemini Embedding vs Nomic Embed for AI agents, BB 70.6 vs D 49.2", "toc": null, "updated": "2026-10-08", "url": "https://www.anchorterminal.com/compare/gemini-embedding-vs-nomic-embed" }, "tokens": { "markdown": 2250, "slim": 680 }, "version": 1 }