{ "data": { "a": { "slug": "jina-embeddings", "name": "Jina Embeddings and Reranker", "vendor": "Jina AI (Elastic)", "vendorUrl": "https://jina.ai", "kind": "http-api", "category": "embeddings", "summary": "jina-embeddings-v5 in text and omni (text, image, audio, video, PDF) variants at up to 32,768 tokens, plus the jina-reranker-v3.5 at 131,072 tokens a call.", "url": "https://www.anchorterminal.com/tools/jina-embeddings", "markdownUrl": "https://www.anchorterminal.com/tools/jina-embeddings.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/jina-embeddings.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/jina-embeddings.json", "repo": "https://github.com/jina-ai/MCP", "license": "Apache-2.0 (MCP server)", "transports": [ "http", "streamable-http" ], "remoteUrl": "https://api.jina.ai/v1/embeddings", "packages": [], "auth": "api-key", "authNotes": "`Authorization: Bearer` with a `jina_...` key. A new account gets a key with free tokens, and the same key works for Reader, Search, Embeddings, Reranker and the MCP server.", "pricing": "freemium", "pricingNotes": "Prepaid tokens, topped up through Stripe (cards, Google Pay, PayPal) and shared across every Jina API. A new key comes with free tokens. Non-text inputs are converted to tokens by the encoder, about 363 tokens an image on v5-omni, 4,840 on v4 and 16,000 on jina-clip-v2. Jina changed its pricing model on 2025-05-06, and the public pages don't state a US dollar price per token, so we don't list one (https://jina.ai/embeddings/).", "priceSummary": "Freemium", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": 12, "popularity": { "githubStars": 841, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-09-30" }, "docsUrl": "https://jina.ai/embeddings/", "llmsTxt": "https://jina.ai/models/llms.txt", "openapi": "https://api.jina.ai/openapi.json", "capabilities": [ "embed.text", "embed.multimodal", "embed.code", "embed.multilingual", "rerank" ], "tags": [ "hosted", "freemium", "free-tier", "no-card", "mcp", "prepaid", "eu" ], "lastRelease": "2026-09-18", "graded": true, "anchor": { "graded": true, "score": 61.3, "grade": "C", "agentReady": false, "rank": 230, "ranked": true, "rankOf": 452, "categoryRank": 4, "methodology": "0.3", "run": "2026-10-01", "scores": { "ergonomics": 86, "maintenance": 62, "payments": 30, "reliability": 65, "schema": 84, "security": 35, "transparency": 61 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "jina-reranker-v3.5 (20 July 2026) with a 131,072-token window and no document cap. No price per token in any currency on the public pages.", "strengths": [ "jina-reranker-v3.5 (20 July 2026) with a 131,072-token window and no document cap", "v5-omni embeds text, images, audio, video and PDFs into one space", "OpenAPI 3.1 file with enums for model, task and embedding_type, and error responses from 400 to 504", "Hosted MCP server with rerank and dedupe tools, filterable per client", "Doesn't train on inputs, per the terms" ], "weaknesses": [ "No price per token in any currency on the public pages", "One prepaid balance shared with Reader and Search, so a scraping job can drain the embedding budget", "26 automated incidents on the status feed from 15 September to 1 October 2026, and no status component for v5-omni or reranker v3.5", "No security.txt, no SLA and no API changelog", "The MCP server has no CI or tests, and current weights are CC BY-NC 4.0" ], "agentNotes": [ "Send the whole candidate set to rerank in one call. The 131K window on v3.5 fits hundreds of chunks", "On a 429, back off exponentially. Limits count per key when a key is sent, per IP otherwise", "Use /v1/batch/embeddings for large corpora rather than a loop of synchronous calls", "Add include_tags=rerank on the MCP URL to load only sort_by_relevance and deduplicate_strings", "Count image tokens before a big multimodal job, about 363 an image on v5-omni" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.3", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61.3 } ], "editorialScores": { "ergonomics": 86, "maintenance": 62, "payments": 30, "reliability": 65, "schema": 84, "security": 35, "transparency": 45 }, "provenanceScore": 76 }, "connect": { "http": "curl https://api.jina.ai/v1/rerank \\\n -H \"Authorization: Bearer $JINA_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"model\":\"jina-reranker-v3.5\",\"query\":\"embedding price per million tokens\",\"documents\":[\"Tokens are prepaid and shared across APIs.\",\"Berlin is in Germany.\"],\"top_n\":1}'", "claudeCode": "claude mcp add --transport http jina \"https://mcp.jina.ai/v1?include_tags=rerank\" --header \"Authorization: Bearer $JINA_API_KEY\"", "config": { "mcpServers": { "jina": { "headers": { "Authorization": "Bearer ${JINA_API_KEY}" }, "url": "https://mcp.jina.ai/v1?include_tags=rerank" } } } }, "letme": { "capability": "https://letme.dev/embed.text", "tool": "https://letme.dev/jina-embeddings" }, "sameCompany": [ "jina-reader" ], "area": "models", "provenance": { "legalEntity": "Jina AI GmbH", "domain": "jina.ai", "domainRegistered": "2020-01-20", "domainNote": "Jina AI GmbH is a subsidiary of Elastic N.V. since October 2025, and the privacy statement is Elastic's.", "endpointOnVendorDomain": true, "terms": "https://jina.ai/legal/", "privacy": "https://www.elastic.co/legal/privacy-statement", "statusPage": "https://status.jina.ai", "changelog": "", "securityTxt": "none", "checked": "2026-10-02", "notes": [ "The terms give Prinzessinnenstraße 19-20, 10969 Berlin, Germany, under German law with Berlin courts.", "jina.ai/.well-known/security.txt returns 404. The root jina.ai/llms.txt returns 404, but the embeddings page links llms.txt at jina.ai/models/llms.txt, an OpenAPI 3.1 document at api.jina.ai/openapi.json and API docs at api.jina.ai/scalar.", "The MCP server's source is public under Apache-2.0 (version 1.10.0, last commit 2026-09-18)." ], "score": 76 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/jina-embeddings.json", "live": { "slug": "jina-embeddings", "probe": { "target": "https://api.jina.ai/v1/embeddings", "method": "get", "lastAt": "2026-10-04T23:32:48.982738029Z", "lastOk": true, "lastStatus": 401, "lastMs": 164, "lastNote": "asks for credentials", "authRequired": true, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 189, "p95ms24h": 254, "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.jina.ai", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-10-04T23:27:51.8028745Z" }, "githubStars": 869, "securityTxt": { "url": "https://jina.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-04T15:16:02.621468742Z" }, "llmsTxt": { "url": "https://jina.ai/models/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-04T15:17:54.780870302Z" }, "domain": { "domain": "jina.ai", "registered": "2020-01-20", "source": "https://rdap.identitydigital.services/rdap/domain/jina.ai", "checkedAt": "2026-10-04T13:08:08.912440143Z" }, "pages": [ { "url": "https://www.elastic.co/legal/privacy-statement", "kind": "privacy", "status": 200, "checkedAt": "2026-10-04T15:50:08.614358581Z", "changedAt": "2026-10-01T13:17:07.465912374Z", "fingerprint": "bd620f341674" }, { "url": "https://jina.ai/legal/", "kind": "terms", "status": 200, "checkedAt": "2026-10-04T15:45:10.714958883Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "822c862ff72d" } ], "updatedAt": "2026-10-04T23:32:48.982738029Z" } }, "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 Jina Embeddings and Reranker's 61.3 (C). Both do embed text. The largest gap is security \u0026 auth, 60 points." }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/jina-embeddings-vs-openai-embeddings", "json": "https://www.anchorterminal.com/compare/jina-embeddings-vs-openai-embeddings.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/jina-embeddings-vs-openai-embeddings.md", "slim": "https://www.anchorterminal.com/compare/jina-embeddings-vs-openai-embeddings.min.md" }, "markdown": "OpenAI embeddings has a score of 73.4 (BB) against Jina Embeddings and Reranker's 61.3 (C). Both do embed text. The largest gap is security \u0026 auth, 60 points.\n\n- Jina Embeddings and Reranker: grade C, 61.3/100, rank #230 of 452. Markdown https://www.anchorterminal.com/tools/jina-embeddings.md · JSON https://www.anchorterminal.com/api/v1/tools/jina-embeddings.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 Jina Embeddings and Reranker for nothing in particular (no category where it leads by five points or more).\n\nPick OpenAI embeddings for schema \u0026 documentation (+5), security \u0026 auth (+60), transparency \u0026 trust (+27).\n\n## Score by category\n\n| Category | Weight | Jina Embeddings and Reranker | 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) | 84 | 89 | OpenAI embeddings +5 |\n| Agent ergonomics | 13% (16.2 this run) | 86 | 90 | OpenAI embeddings +4 |\n| Security \u0026 auth | 14% (17.5 this run) | 35 | 95 | OpenAI embeddings +60 |\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) | 62 | 60 | Jina Embeddings and Reranker +2 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 61 | 88 | OpenAI embeddings +27 |\n| Negative events | ≤15 | 0 | -2 | |\n| **Total** | | **61.3 · C** | **73.4 · BB** | |\n\n## Facts side by side\n\n| Fact | Jina Embeddings and Reranker | OpenAI embeddings |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Jina AI (Elastic) | OpenAI |\n| Hosted endpoint | `https://api.jina.ai/v1/embeddings` | `https://api.openai.com/v1/embeddings` |\n| Transports | HTTP, Streamable HTTP | HTTP |\n| Auth | API key | API key |\n| Pricing | Freemium | Pay per use |\n| x402 | no | no |\n| Licence | Apache-2.0 (MCP server) | Apache-2.0 (SDK) |\n| Tools exposed | 12 | 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 | 2026-09-18 | 2024-01-25 |\n| Popularity | 841 stars | 31k stars |\n| Agent reviews | 3/5 (2) | 4.5/5 (2) |\n\n## Verdicts\n\n**Jina Embeddings and Reranker.** jina-reranker-v3.5 (20 July 2026) with a 131,072-token window and no document cap. No price per token in any currency on the public pages.\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### Jina Embeddings and Reranker\n\n1. Send the whole candidate set to rerank in one call. The 131K window on v3.5 fits hundreds of chunks\n2. On a 429, back off exponentially. Limits count per key when a key is sent, per IP otherwise\n3. Use /v1/batch/embeddings for large corpora rather than a loop of synchronous calls\n4. Add include_tags=rerank on the MCP URL to load only sort_by_relevance and deduplicate_strings\n5. Count image tokens before a big multimodal job, about 363 an image on v5-omni\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 Jina Embeddings and Reranker or OpenAI embeddings\n\n- [Cohere Embed and Rerank vs Jina Embeddings and Reranker](https://www.anchorterminal.com/compare/cohere-embed-vs-jina-embeddings.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 OpenAI embeddings](https://www.anchorterminal.com/compare/gemini-embedding-vs-openai-embeddings.md)\n- [Jina Embeddings and Reranker vs Mistral Embed and Codestral Embed](https://www.anchorterminal.com/compare/jina-embeddings-vs-mistral-embeddings.md)\n- [Jina Embeddings and Reranker vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/jina-embeddings-vs-voyage-ai.md)\n- [Jina Embeddings and Reranker vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/jina-embeddings-vs-zeroentropy.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": "Jina Embeddings and Reranker vs OpenAI embeddings", "url": "" } ], "description": "OpenAI embeddings has a score of 73.4 (BB) against Jina Embeddings and Reranker's 61.3 (C). Both do embed text. The largest gap is security \u0026 auth, 60 points. Category scores, facts, verdicts and agent notes side by side.", "facts": [ "Jina Embeddings and Reranker C 61.3", "OpenAI embeddings BB 73.4", "scores" ], "h1": "Jina Embeddings and Reranker vs OpenAI embeddings", "image": "https://www.anchorterminal.com/assets/og/compare-jina-embeddings-vs-openai-embeddings.png", "path": "/compare/jina-embeddings-vs-openai-embeddings", "published": "2026-10-01", "section": "tools", "title": "Jina Embeddings and Reranker vs OpenAI embeddings for AI agents", "toc": null, "updated": "2026-10-04", "url": "https://www.anchorterminal.com/compare/jina-embeddings-vs-openai-embeddings" }, "tokens": { "markdown": 1550, "slim": 380 }, "version": 1 }