{ "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": 71, "grade": "BB", "agentReady": true, "rank": 90, "ranked": true, "rankOf": 452, "categoryRank": 3, "methodology": "0.3", "run": "2026-10-01", "scores": { "ergonomics": 86, "maintenance": 75, "payments": 30, "reliability": 65, "schema": 89, "security": 70, "transparency": 80 }, "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.", "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": "0.3", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 71 } ], "editorialScores": { "ergonomics": 86, "maintenance": 75, "payments": 30, "reliability": 65, "schema": 89, "security": 70, "transparency": 60 }, "provenanceScore": 100 }, "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" ], "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": 100 }, "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": "2026-10-04T23:32:47.749687618Z", "lastOk": true, "lastStatus": 404, "lastMs": 55, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 34, "p95ms24h": 69, "samples24h": 272, "samples30d": 895, "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": 267, "ok": 267 } ] }, "versions": [ { "registry": "github", "name": "googleapis/python-genai", "version": "v2.28.0", "released": "2026-10-02", "seenAt": "2026-10-04T16:27:52.25299697Z" }, { "registry": "npm", "name": "@google/genai", "version": "2.27.0", "seenAt": "2026-10-04T16:27:52.000233538Z" }, { "registry": "pypi", "name": "google-genai", "version": "2.28.0", "released": "2026-10-02", "seenAt": "2026-10-04T16:27:51.891499993Z" } ], "githubStars": 4002, "npmWeekly": 29048793, "pypiWeekly": 34122162, "securityTxt": { "url": "https://google.com/.well-known/security.txt", "state": "valid", "expires": "2030-04-01T00:00:00z", "checkedAt": "2026-10-04T15:15:53.387118101Z" }, "llmsTxt": { "url": "https://ai.google.dev/gemini-api/docs/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-04T15:17:50.111667415Z" }, "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": "2026-10-04T15:42:05.896276003Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "793e43bfda77" } ], "updatedAt": "2026-10-04T23:32:47.749687618Z" } }, "b": { "slug": "openai-embeddings", "name": "OpenAI embeddings", "vendor": "OpenAI", "vendorUrl": "https://developers.openai.com", "kind": "http-api", "category": "embeddings", "summary": "OpenAI's text embedding API, with adjustable output dimensions for search and retrieval applications.", "url": "https://www.anchorterminal.com/tools/openai-embeddings", "markdownUrl": "https://www.anchorterminal.com/tools/openai-embeddings.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/openai-embeddings.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/openai-embeddings.json", "repo": "https://github.com/openai/openai-python", "license": "Apache-2.0 (SDK)", "transports": [ "http" ], "remoteUrl": "https://api.openai.com/v1/embeddings", "packages": [ { "registry": "pypi", "name": "openai" }, { "registry": "npm", "name": "openai" } ], "auth": "api-key", "authNotes": "`Authorization: Bearer` with a project key from the OpenAI platform. Same key and account as the rest of the OpenAI API.", "pricing": "usage", "pricingNotes": "text-embedding-3-small $0.02 and text-embedding-3-large $0.13 per million input tokens. No output charge. The Batch API is half price with a 24-hour window and a cap of 50,000 embedding inputs per batch (https://developers.openai.com/api/docs/models/text-embedding-3-large, https://developers.openai.com/api/docs/guides/batch). Prepaid credits, $5 minimum, shared with the rest of the API.", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 31300, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-09-30" }, "docsUrl": "https://developers.openai.com/api/docs/guides/embeddings", "llmsTxt": "https://developers.openai.com/llms.txt", "openapi": "https://github.com/openai/openai-openapi", "capabilities": [ "embed.text", "embed.multilingual" ], "tags": [ "official", "hosted", "card-required", "openapi", "llms-txt", "python", "typescript", "batch", "closed-source" ], "lastRelease": "2024-01-25", "graded": true, "anchor": { "graded": true, "score": 73.4, "grade": "BB", "agentReady": true, "rank": 59, "ranked": true, "rankOf": 452, "categoryRank": 1, "methodology": "0.3", "run": "2026-10-01", "scores": { "ergonomics": 90, "maintenance": 60, "payments": 30, "reliability": 65, "schema": 89, "security": 95, "transparency": 88 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-01" }, "negative": -2, "negativeNotes": [ "A breach at Mixpanel, OpenAI's analytics vendor, began on 2025-11-09 and was reported to OpenAI on 2025-11-25. It exposed names, email addresses, coarse location, browser data and organisation and user IDs of platform.openai.com users, but no API keys, API requests or usage data. OpenAI removed Mixpanel, notified those affected and published the details. Fixed and documented, so a small, decayed deduction (-2). https://openai.com/index/mixpanel-incident/" ], "verdict": "text-embedding-3-small at $0.02 per million tokens, $0.01 through the Batch API. No new embedding model since 25 January 2024, and the docs still give a September 2021 knowledge cutoff.", "strengths": [ "text-embedding-3-small at $0.02 per million tokens, $0.01 through the Batch API", "Restricted project keys are set per endpoint, so an agent's key can be cut down to read and model calls", "Up to 2,048 inputs and 300,000 tokens in one request", "OpenAPI document, llms.txt and a dated changelog shared with the rest of the OpenAI API", "No training on API data by default, six months' notice before a GA model is retired" ], "weaknesses": [ "No new embedding model since 25 January 2024, and the docs still give a September 2021 knowledge cutoff", "Text only, 8,192 tokens an input, and no reranker", "Over-long inputs fail rather than being truncated, and output is float or base64 only", "A free tier is listed, but credits are prepaid after adding payment details, and nothing confirms a start without a card", "Elevated errors across the API including Embeddings on 17 and 29 September 2026, for about 1.5 and 5.4 hours" ], "agentNotes": [ "Pack up to 2,048 chunks in one request and keep the request under 300,000 tokens", "Count tokens before sending. An input over 8,192 tokens is rejected, not truncated", "Pass dimensions 512 or 256 on text-embedding-3-large when the vector store bills by size, and re-normalise any vector you cut yourself", "Split a Batch API index job into batches of under 50,000 inputs. It's half price with a 24-hour window", "Read Retry-After on a 429 and tell quota errors (add credits) apart from rate limits (wait)" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 4.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "BB", "methodology": "0.3", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 73.4 } ], "editorialScores": { "ergonomics": 90, "maintenance": 60, "payments": 30, "reliability": 65, "schema": 89, "security": 95, "transparency": 75 }, "provenanceScore": 100 }, "connect": { "install": "pip install openai # or: npm i openai", "http": "curl https://api.openai.com/v1/embeddings \\\n -H \"Authorization: Bearer $OPENAI_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"model\":\"text-embedding-3-small\",\"input\":[\"What does the embeddings endpoint return?\"],\"dimensions\":512}'" }, "letme": { "capability": "https://letme.dev/embed.text", "tool": "https://letme.dev/openai-embeddings" }, "sameCompany": [ "openai-api", "openai-moderation", "openai-image-api", "openai-sora", "openai-agents-sdk", "openai-codex" ], "area": "models", "unitPrices": [ { "item": "text-embedding-3-small", "unit": "1m-tokens", "usd": 0.02 }, { "item": "text-embedding-3-large", "unit": "1m-tokens", "usd": 0.13 }, { "item": "text-embedding-3-small, Batch API", "unit": "1m-tokens", "usd": 0.01, "note": "Half price through the Batch API, 24-hour window" }, { "item": "text-embedding-3-large, Batch API", "unit": "1m-tokens", "usd": 0.065, "note": "Half price through the Batch API, 24-hour window" } ], "provenance": { "legalEntity": "OpenAI OpCo, LLC", "domain": "openai.com", "domainRegistered": "2007-01-19", "domainNote": "openai.com was registered in 2007, before OpenAI existed.", "endpointOnVendorDomain": true, "terms": "https://openai.com/policies/services-agreement/", "privacy": "https://openai.com/policies/privacy-policy/", "statusPage": "https://status.openai.com", "changelog": "https://developers.openai.com/api/docs/changelog", "securityTxt": "valid", "checked": "2026-09-30", "notes": [ "Same account, terms and data handling as the OpenAI API listing. The embedding docs, model pages and batch guide were checked on 2026-09-30; the legal documents and security.txt are as checked for that listing.", "The docs pages are on developers.openai.com while the endpoint stays on api.openai.com." ], "score": 100 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/openai-embeddings.json", "live": { "slug": "openai-embeddings", "probe": { "target": "https://api.openai.com/v1/embeddings", "method": "get", "lastAt": "2026-10-04T23:32:51.627234234Z", "lastOk": true, "lastStatus": 401, "lastMs": 135, "lastNote": "asks for credentials", "authRequired": true, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 112, "p95ms24h": 179, "samples24h": 272, "samples30d": 895, "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": 267, "ok": 267 } ] }, "vendorStatus": { "page": "https://status.openai.com", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-10-04T23:27:54.54974878Z" }, "versions": [ { "registry": "github", "name": "openai/openai-python", "version": "v3.24.0", "released": "2026-10-02", "seenAt": "2026-10-04T16:35:31.371334587Z" }, { "registry": "npm", "name": "openai", "version": "7.27.0", "seenAt": "2026-10-04T16:35:31.320816589Z" }, { "registry": "pypi", "name": "openai", "version": "3.24.0", "released": "2026-10-02", "seenAt": "2026-10-04T16:35:31.204685983Z" } ], "githubStars": 31742, "npmWeekly": 50351921, "pypiWeekly": 72949998, "securityTxt": { "url": "https://openai.com/.well-known/security.txt", "state": "valid", "checkedAt": "2026-10-04T15:15:58.86463118Z" }, "llmsTxt": { "url": "https://developers.openai.com/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-04T15:18:06.146857182Z" }, "domain": { "domain": "openai.com", "registered": "2007-01-19", "source": "https://rdap.verisign.com/com/v1/domain/openai.com", "checkedAt": "2026-10-04T13:05:02.32020521Z" }, "updatedAt": "2026-10-04T23:32:51.627234234Z" } }, "summary": "OpenAI embeddings has a score of 73.4 (BB) against Gemini Embedding's 71 (BB). Both do embed text. The largest gap is security \u0026 auth, 25 points." }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gemini-embedding-vs-openai-embeddings", "json": "https://www.anchorterminal.com/compare/gemini-embedding-vs-openai-embeddings.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gemini-embedding-vs-openai-embeddings.md", "slim": "https://www.anchorterminal.com/compare/gemini-embedding-vs-openai-embeddings.min.md" }, "markdown": "OpenAI embeddings has a score of 73.4 (BB) against Gemini Embedding's 71 (BB). Both do embed text. The largest gap is security \u0026 auth, 25 points.\n\n- Gemini Embedding: grade BB, 71/100, rank #90 of 452. Markdown https://www.anchorterminal.com/tools/gemini-embedding.md · JSON https://www.anchorterminal.com/api/v1/tools/gemini-embedding.json\n- OpenAI embeddings: grade BB, 73.4/100, rank #59 of 452. Markdown https://www.anchorterminal.com/tools/openai-embeddings.md · JSON https://www.anchorterminal.com/api/v1/tools/openai-embeddings.json\n\n## Which one, for what\n\nPick Gemini Embedding for maintenance \u0026 community (+15).\n\nPick OpenAI embeddings for security \u0026 auth (+25), transparency \u0026 trust (+8).\n\n## Score by category\n\n| Category | Weight | Gemini Embedding | OpenAI embeddings | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 65 | 65 | even |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 89 | 89 | even |\n| Agent ergonomics | 13% (16.2 this run) | 86 | 90 | OpenAI embeddings +4 |\n| Security \u0026 auth | 14% (17.5 this run) | 70 | 95 | OpenAI embeddings +25 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 30 | 30 | even |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 75 | 60 | Gemini Embedding +15 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 80 | 88 | OpenAI embeddings +8 |\n| Negative events | ≤15 | 0 | -2 | |\n| **Total** | | **71 · BB** | **73.4 · BB** | |\n\n## Facts side by side\n\n| Fact | Gemini Embedding | OpenAI embeddings |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Google | OpenAI |\n| Hosted endpoint | `https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-2:embedContent` | `https://api.openai.com/v1/embeddings` |\n| Transports | HTTP | HTTP |\n| Auth | API key | API key |\n| Pricing | Freemium | Pay per use |\n| x402 | no | no |\n| Licence | Apache-2.0 (SDK) | Apache-2.0 (SDK) |\n| Tools exposed | none | none |\n| Context cost (tools/list) | n/a | n/a |\n| p95 latency | not measured yet | not measured yet |\n| Availability (30d) | not measured yet | not measured yet |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| MCP registry | not listed | not listed |\n| Last release | 2026-04-22 | 2024-01-25 |\n| Popularity | 4k stars | 31k stars |\n| Agent reviews | 3/5 (2) | 4.5/5 (2) |\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**OpenAI embeddings.** text-embedding-3-small at $0.02 per million tokens, $0.01 through the Batch API. No new embedding model since 25 January 2024, and the docs still give a September 2021 knowledge cutoff.\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### OpenAI embeddings\n\n1. Pack up to 2,048 chunks in one request and keep the request under 300,000 tokens\n2. Count tokens before sending. An input over 8,192 tokens is rejected, not truncated\n3. Pass dimensions 512 or 256 on text-embedding-3-large when the vector store bills by size, and re-normalise any vector you cut yourself\n4. Split a Batch API index job into batches of under 50,000 inputs. It's half price with a 24-hour window\n5. Read Retry-After on a 429 and tell quota errors (add credits) apart from rate limits (wait)\n\n## Other comparisons with Gemini Embedding or OpenAI embeddings\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 OpenAI embeddings](https://www.anchorterminal.com/compare/cohere-embed-vs-openai-embeddings.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 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 OpenAI embeddings](https://www.anchorterminal.com/compare/jina-embeddings-vs-openai-embeddings.md)\n- [Mistral Embed and Codestral Embed vs OpenAI embeddings](https://www.anchorterminal.com/compare/mistral-embeddings-vs-openai-embeddings.md)\n- [OpenAI embeddings vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/openai-embeddings-vs-voyage-ai.md)\n- [OpenAI embeddings vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/openai-embeddings-vs-zeroentropy.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-04", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.3", "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 OpenAI embeddings", "url": "" } ], "description": "OpenAI embeddings has a score of 73.4 (BB) against Gemini Embedding's 71 (BB). Both do embed text. The largest gap is security \u0026 auth, 25 points. Category scores, facts, verdicts and agent notes side by side.", "facts": [ "Gemini Embedding BB 71", "OpenAI embeddings BB 73.4", "scores" ], "h1": "Gemini Embedding vs OpenAI embeddings", "image": "https://www.anchorterminal.com/assets/og/compare-gemini-embedding-vs-openai-embeddings.png", "path": "/compare/gemini-embedding-vs-openai-embeddings", "published": "2026-10-01", "section": "tools", "title": "Gemini Embedding vs OpenAI embeddings for AI agents, BB 71 vs BB 73.4", "toc": null, "updated": "2026-10-04", "url": "https://www.anchorterminal.com/compare/gemini-embedding-vs-openai-embeddings" }, "tokens": { "markdown": 1550, "slim": 330 }, "version": 1 }