{ "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": "2026-10-04T23:48:10.087395061Z", "lastOk": true, "lastStatus": 401, "lastMs": 170, "lastNote": "asks for credentials", "authRequired": true, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 189, "p95ms24h": 254, "samples24h": 272, "samples30d": 898, "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": 270, "ok": 270 } ] }, "vendorStatus": { "page": "https://status.jina.ai", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-10-04T23:49:17.423904336Z" }, "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": "2026-10-04T23:49:17.423904336Z" } }, "b": { "slug": "voyage-ai", "name": "Voyage AI embeddings and rerankers", "vendor": "Voyage AI (MongoDB)", "vendorUrl": "https://www.voyageai.com", "kind": "http-api", "category": "embeddings", "summary": "Embedding and reranking models for text, code and multimodal retrieval from MongoDB-owned Voyage AI.", "url": "https://www.anchorterminal.com/tools/voyage-ai", "markdownUrl": "https://www.anchorterminal.com/tools/voyage-ai.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/voyage-ai.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/voyage-ai.json", "repo": "https://github.com/voyage-ai/voyageai-python", "license": "MIT (SDK)", "transports": [ "http" ], "remoteUrl": "https://api.voyageai.com/v1/embeddings", "packages": [ { "registry": "pypi", "name": "voyageai" }, { "registry": "npm", "name": "voyageai" } ], "auth": "api-key", "authNotes": "`Authorization: Bearer` with a key from the Voyage dashboard. The Python and TypeScript clients read `VOYAGE_API_KEY`.", "pricing": "freemium", "pricingNotes": "Per million tokens. voyage-4-large, voyage-context-4, voyage-code-4 and voyage-multimodal-3.5 $0.12, voyage-4 $0.06, voyage-4-lite $0.02, rerank-3 $0.05, rerank-3-lite $0.02. Multimodal adds $0.60 per billion pixels. Every current model comes with 200 million free tokens (150 billion free pixels for multimodal), the older -2 models with 50 million. The Batch API is 33 per cent cheaper and the free tokens don't apply to it. Files API storage $0.05 per GB a month (https://docs.voyageai.com/docs/pricing).", "priceSummary": "Freemium", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 105, "npmWeekly": 306748, "pypiWeekly": 936716, "asOf": "2026-09-30" }, "docsUrl": "https://docs.voyageai.com/docs/introduction", "llmsTxt": "https://docs.voyageai.com/llms.txt", "capabilities": [ "embed.text", "embed.multimodal", "embed.code", "embed.multilingual", "rerank" ], "tags": [ "hosted", "freemium", "free-tier", "no-card", "llms-txt", "python", "typescript", "batch", "closed-source" ], "lastRelease": "2026-09-30", "graded": true, "anchor": { "graded": true, "score": 59, "grade": "C", "agentReady": false, "rank": 273, "ranked": true, "rankOf": 452, "categoryRank": 5, "methodology": "0.3", "run": "2026-10-01", "scores": { "ergonomics": 98, "maintenance": 78, "payments": 40, "reliability": 45, "schema": 61, "security": 45, "transparency": 51 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "200 million free tokens per current model, then $0.02 to $0.12 per million. Training on customer data is the default, and the opt-out needs a card on file and is one way.", "strengths": [ "200 million free tokens per current model, then $0.02 to $0.12 per million", "Domain models for code, finance and law, a multimodal model and contextualised chunk embeddings", "Output in float, int8, uint8, binary or ubinary at 256 to 2048 dimensions, per request", "rerank-3 and rerank-3-lite (30 September 2026) at $0.05 and $0.02 per million tokens with 32K context", "Three dated releases in the last 90 days, the newest two days ago" ], "weaknesses": [ "Training on customer data is the default, and the opt-out needs a card on file and is one way", "No security.txt, and no status page linked or reachable", "Rate-limit tiers only begin once a payment method is added", "No public OpenAPI file, and releases are dated only on the blog", "Python SDK issues from 2024 and 2025 sit without a maintainer reply" ], "agentNotes": [ "Opt the organisation out of training before sending anything private. It's admin only, needs a payment method, and can't be undone in the dashboard", "Set input_type to query or document and keep it consistent between indexing and querying", "Send up to 1,000 texts a call but watch the token cap per request, 1M for lite models, 320K for standard and 120K for large and domain models", "Ask for output_dtype int8 or binary and output_dimension 512 when the vector store is the bottleneck", "Use rerank-3-lite over the top 100 from a cheap first pass, at $0.02 per million tokens" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 4, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.3", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 59 } ], "editorialScores": { "ergonomics": 98, "maintenance": 78, "payments": 40, "reliability": 45, "schema": 61, "security": 45, "transparency": 25 }, "provenanceScore": 76 }, "connect": { "install": "pip install voyageai # or: npm i voyageai", "http": "curl https://api.voyageai.com/v1/embeddings \\\n -H \"Authorization: Bearer $VOYAGE_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"model\":\"voyage-4\",\"input\":[\"What does the embeddings endpoint return?\"],\"input_type\":\"query\",\"output_dimension\":1024}'" }, "letme": { "capability": "https://letme.dev/embed.text", "tool": "https://letme.dev/voyage-ai" }, "sameCompany": [ "mongodb-mcp" ], "area": "models", "unitPrices": [ { "item": "voyage-4-large embeddings", "unit": "1m-tokens", "usd": 0.12, "note": "Also voyage-context-4, voyage-code-4 and voyage-multimodal-3.5" }, { "item": "voyage-4 embeddings", "unit": "1m-tokens", "usd": 0.06 }, { "item": "voyage-4-lite embeddings", "unit": "1m-tokens", "usd": 0.02 }, { "item": "rerank-3", "unit": "1m-tokens", "usd": 0.05 }, { "item": "rerank-3-lite", "unit": "1m-tokens", "usd": 0.02 }, { "item": "Files API storage", "unit": "gb-month", "usd": 0.05 } ], "provenance": { "legalEntity": "Voyage AI Innovations, Inc.", "domain": "voyageai.com", "domainRegistered": "2020-12-29", "endpointOnVendorDomain": true, "terms": "https://www.voyageai.com/tos", "privacy": "https://www.voyageai.com/privacy", "statusPage": "", "changelog": "https://docs.voyageai.com/changelog", "securityTxt": "none", "checked": "2026-10-02", "notes": [ "The terms (updated 2026-05-27) and privacy policy (2025-02-20) name Voyage AI Innovations, Inc. under California law, with no postal address. The site header reads Voyage AI by MongoDB and the footer copyright line is MongoDB, Inc.", "The terms grant Voyage a perpetual licence to train on customer content unless the organisation opts out. Content sent before the opt-out stays covered.", "status.voyageai.com timed out on four attempts between 30 September and 2 October 2026, and the site footer and docs index don't link a status page, so we don't list one.", "The docs changelog shows one undated entry. Model releases are dated on blog.voyageai.com, newest rerank-3 on 2026-09-30.", "SOC 2 and HIPAA reports are on a Vanta trust page linked from the footer." ], "score": 76 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/voyage-ai.json", "live": { "slug": "voyage-ai", "probe": { "target": "https://api.voyageai.com/v1/embeddings", "method": "get", "lastAt": "2026-10-04T23:48:18.326854436Z", "lastOk": true, "lastStatus": 405, "lastMs": 675, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 196, "p95ms24h": 685, "samples24h": 272, "samples30d": 898, "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": 270, "ok": 270 } ] }, "versions": [ { "registry": "github", "name": "voyage-ai/voyageai-python", "version": "v0.5.0", "released": "2026-07-10", "seenAt": "2026-10-04T16:43:54.223118342Z" }, { "registry": "npm", "name": "voyageai", "version": "0.4.0", "seenAt": "2026-10-04T16:43:53.823742775Z" }, { "registry": "pypi", "name": "voyageai", "version": "0.5.0", "released": "2026-07-10", "seenAt": "2026-10-04T16:43:53.714440219Z" } ], "githubStars": 113, "npmWeekly": 334044, "pypiWeekly": 1007721, "securityTxt": { "url": "https://voyageai.com/.well-known/security.txt", "state": "unknown", "checkedAt": "2026-10-04T15:15:47.28134032Z" }, "llmsTxt": { "url": "https://docs.voyageai.com/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-04T15:18:21.616441109Z" }, "domain": { "domain": "voyageai.com", "registered": "2020-12-29", "source": "https://rdap.verisign.com/com/v1/domain/voyageai.com", "checkedAt": "2026-10-04T13:07:42.741568711Z" }, "pages": [ { "url": "https://docs.voyageai.com/changelog", "kind": "changelog", "status": 404, "checkedAt": "2026-10-04T15:44:14.753232313Z", "changedAt": "0001-01-01T00:00:00Z" }, { "url": "https://docs.voyageai.com/docs/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-10-04T15:44:17.073382402Z", "changedAt": "2026-10-04T15:44:17.073382402Z", "fingerprint": "e94e011a3e9b" }, { "url": "https://www.voyageai.com/privacy", "kind": "privacy", "status": 304, "checkedAt": "2026-10-04T15:52:50.401389134Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "d71dc9022a14" }, { "url": "https://www.voyageai.com/tos", "kind": "terms", "status": 304, "checkedAt": "2026-10-04T15:52:52.573014262Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "885b45461466" } ], "updatedAt": "2026-10-04T23:48:18.326854436Z" } }, "summary": "Jina Embeddings and Reranker has a score of 61.3 (C) against Voyage AI embeddings and rerankers's 59 (C). Both do embed text. The largest gap is schema \u0026 documentation, 23 points." }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/jina-embeddings-vs-voyage-ai", "json": "https://www.anchorterminal.com/compare/jina-embeddings-vs-voyage-ai.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/jina-embeddings-vs-voyage-ai.md", "slim": "https://www.anchorterminal.com/compare/jina-embeddings-vs-voyage-ai.min.md" }, "markdown": "Jina Embeddings and Reranker has a score of 61.3 (C) against Voyage AI embeddings and rerankers's 59 (C). Both do embed text. The largest gap is schema \u0026 documentation, 23 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- Voyage AI embeddings and rerankers: grade C, 59/100, rank #273 of 452. Markdown https://www.anchorterminal.com/tools/voyage-ai.md · JSON https://www.anchorterminal.com/api/v1/tools/voyage-ai.json\n\n## Which one, for what\n\nPick Jina Embeddings and Reranker for reliability (+20), schema \u0026 documentation (+23), transparency \u0026 trust (+10).\n\nPick Voyage AI embeddings and rerankers for agent ergonomics (+12), security \u0026 auth (+10), payments \u0026 pricing (+10), maintenance \u0026 community (+16).\n\n## Score by category\n\n| Category | Weight | Jina Embeddings and Reranker | Voyage AI embeddings and rerankers | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 65 | 45 | Jina Embeddings and Reranker +20 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 84 | 61 | Jina Embeddings and Reranker +23 |\n| Agent ergonomics | 13% (16.2 this run) | 86 | 98 | Voyage AI embeddings and rerankers +12 |\n| Security \u0026 auth | 14% (17.5 this run) | 35 | 45 | Voyage AI embeddings and rerankers +10 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 30 | 40 | Voyage AI embeddings and rerankers +10 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 62 | 78 | Voyage AI embeddings and rerankers +16 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 61 | 51 | Jina Embeddings and Reranker +10 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61.3 · C** | **59 · C** | |\n\n## Facts side by side\n\n| Fact | Jina Embeddings and Reranker | Voyage AI embeddings and rerankers |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Jina AI (Elastic) | Voyage AI (MongoDB) |\n| Hosted endpoint | `https://api.jina.ai/v1/embeddings` | `https://api.voyageai.com/v1/embeddings` |\n| Transports | HTTP, Streamable HTTP | HTTP |\n| Auth | API key | API key |\n| Pricing | Freemium | Freemium |\n| x402 | no | no |\n| Licence | Apache-2.0 (MCP server) | MIT (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 | 2026-09-30 |\n| Popularity | 841 stars | 105 stars, 307k npm/wk, 937k PyPI/wk |\n| Agent reviews | 3/5 (2) | 4/5 (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**Voyage AI embeddings and rerankers.** 200 million free tokens per current model, then $0.02 to $0.12 per million. Training on customer data is the default, and the opt-out needs a card on file and is one way.\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### Voyage AI embeddings and rerankers\n\n1. Opt the organisation out of training before sending anything private. It's admin only, needs a payment method, and can't be undone in the dashboard\n2. Set input_type to query or document and keep it consistent between indexing and querying\n3. Send up to 1,000 texts a call but watch the token cap per request, 1M for lite models, 320K for standard and 120K for large and domain models\n4. Ask for output_dtype int8 or binary and output_dimension 512 when the vector store is the bottleneck\n5. Use rerank-3-lite over the top 100 from a cheap first pass, at $0.02 per million tokens\n\n## Other comparisons with Jina Embeddings and Reranker or Voyage AI embeddings and rerankers\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 Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/cohere-embed-vs-voyage-ai.md)\n- [Gemini Embedding vs Jina Embeddings and Reranker](https://www.anchorterminal.com/compare/gemini-embedding-vs-jina-embeddings.md)\n- [Gemini Embedding vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/gemini-embedding-vs-voyage-ai.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 OpenAI embeddings](https://www.anchorterminal.com/compare/jina-embeddings-vs-openai-embeddings.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 Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/mistral-embeddings-vs-voyage-ai.md)\n- [OpenAI embeddings vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/openai-embeddings-vs-voyage-ai.md)\n- [Voyage AI embeddings and rerankers vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/voyage-ai-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 Voyage AI embeddings and rerankers", "url": "" } ], "description": "Jina Embeddings and Reranker has a score of 61.3 (C) against Voyage AI embeddings and rerankers's 59 (C). Both do embed text. The largest gap is schema \u0026 documentation, 23 points. Category scores, facts, verdicts and agent notes side by side.", "facts": [ "Jina Embeddings and Reranker C 61.3", "Voyage AI embeddings and rerankers C 59", "scores" ], "h1": "Jina Embeddings and Reranker vs Voyage AI embeddings and rerankers", "image": "https://www.anchorterminal.com/assets/og/compare-jina-embeddings-vs-voyage-ai.png", "path": "/compare/jina-embeddings-vs-voyage-ai", "published": "2026-10-01", "section": "tools", "title": "Jina Embeddings and Reranker vs Voyage AI embeddings and rerankers", "toc": null, "updated": "2026-10-04", "url": "https://www.anchorterminal.com/compare/jina-embeddings-vs-voyage-ai" }, "tokens": { "markdown": 1650, "slim": 380 }, "version": 1 }