{
  "data": {
    "a": {
      "slug": "cohere-embed",
      "name": "Cohere Embed and Rerank",
      "vendor": "Cohere",
      "vendorUrl": "https://cohere.com",
      "kind": "http-api",
      "category": "embeddings",
      "summary": "Embed 5 (Pro and Fast, released 2026-09-30) embeds text, images and parsed PDFs at 128K context in 100+ languages, at $0.08 to $0.12 per million tokens.",
      "url": "https://www.anchorterminal.com/tools/cohere-embed",
      "markdownUrl": "https://www.anchorterminal.com/tools/cohere-embed.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/cohere-embed.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/cohere-embed.json",
      "repo": "https://github.com/cohere-ai/cohere-python",
      "license": "MIT (SDK)",
      "transports": [
        "http"
      ],
      "remoteUrl": "https://api.cohere.com/v2/embed",
      "packages": [
        {
          "registry": "pypi",
          "name": "cohere"
        },
        {
          "registry": "npm",
          "name": "cohere-ai"
        }
      ],
      "auth": "api-key",
      "authNotes": "`Authorization: Bearer` with a trial or production key from the dashboard. Trial keys are free, rate limited and not for commercial use. Production keys bill monthly.",
      "pricing": "freemium",
      "pricingNotes": "Embed 5 Pro $0.12 and Embed 5 Fast $0.08 per million text tokens, $0.40 per million image tokens on both (https://cohere.com/blog/embed-5). Rerank is billed per search, one query with up to 100 documents, and a document over 500 tokens is split into chunks that each count as a document. The per-search rate on the pricing page renders client-side and we couldn't read it. On Amazon Bedrock, Rerank 3.5 is $2.00 per 1,000 queries (https://aws.amazon.com/bedrock/pricing/). Model Vault dedicated instances run $3 to $10 an hour or $2,000 to $6,500 a month. Trial keys are free, need no card, and are capped at 1,000 calls a month. Bills issue monthly or at $250 outstanding (https://cohere.com/pricing).",
      "priceSummary": "$2 / 1k req",
      "where": "hosted",
      "x402": {
        "level": "no",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": 400,
        "npmWeekly": 555855,
        "pypiWeekly": 2593025,
        "asOf": "2026-09-30"
      },
      "docsUrl": "https://docs.cohere.com/docs/embeddings",
      "llmsTxt": "https://docs.cohere.com/llms.txt",
      "capabilities": [
        "embed.text",
        "embed.multimodal",
        "embed.multilingual",
        "rerank"
      ],
      "tags": [
        "hosted",
        "freemium",
        "free-tier",
        "no-card",
        "llms-txt",
        "python",
        "typescript",
        "enterprise",
        "closed-source"
      ],
      "lastRelease": "2026-09-30",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 72.5,
        "grade": "BB",
        "agentReady": true,
        "rank": 69,
        "ranked": true,
        "rankOf": 452,
        "categoryRank": 2,
        "methodology": "0.3",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 87,
          "maintenance": 87,
          "payments": 35,
          "reliability": 83,
          "schema": 92,
          "security": 50,
          "transparency": 69
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-01"
        },
        "negative": 0,
        "verdict": "Rerank 4 Pro and Fast with 32K context and top_n, tracked per model on the status page. Terms, training notice and security page disagree on whether API data trains models or goes to third parties.",
        "strengths": [
          "Rerank 4 Pro and Fast with 32K context and top_n, tracked per model on the status page",
          "Embed 5 at 128K context with six output sizes and int8, binary and base64 output",
          "Free trial keys at signup with no card",
          "Public OpenAPI file, llms.txt and a dated changelog",
          "Embed 5 Fast at $0.08 per million text tokens"
        ],
        "weaknesses": [
          "Terms, training notice and security page disagree on whether API data trains models or goes to third parties",
          "The per-search rerank price didn't render on the pricing page",
          "96 inputs a call, and input_type is required",
          "One unscoped key reaches every Cohere endpoint, including delete operations",
          "No security.txt, no published subprocessor list found, no SLA"
        ],
        "agentNotes": [
          "Send input_type on every embed call, search_document when indexing and search_query when querying. The endpoint rejects a call without it",
          "Batch 96 inputs a call, the maximum, stay under 2,000 inputs a minute, and check every batch returns every embedding type you asked for (an open SDK bug drops types missing from the first response)",
          "Budget rerank by searches. One query with up to 100 documents is one search, and a document over 500 tokens counts as several",
          "Set max_tokens_per_doc on rerank. The default of 4,096 truncates long documents even on the 32K models",
          "Ask for int8 or binary embedding_types and a smaller output_dimension before scaling the vector store"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 2,
        "avgRating": 3.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "BB",
            "methodology": "0.3",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 72.5
          }
        ],
        "editorialScores": {
          "ergonomics": 87,
          "maintenance": 87,
          "payments": 35,
          "reliability": 83,
          "schema": 92,
          "security": 50,
          "transparency": 47
        },
        "provenanceScore": 90
      },
      "connect": {
        "install": "pip install cohere   # or: npm i cohere-ai",
        "http": "curl -X POST https://api.cohere.com/v2/rerank \\\n  -H \"Authorization: Bearer $COHERE_API_KEY\" -H \"content-type: application/json\" \\\n  -d '{\"model\":\"rerank-v4.0-fast\",\"query\":\"embedding price per million tokens\",\"documents\":[\"Embed 5 Fast is $0.08 per million tokens.\",\"Toronto is in Ontario.\"],\"top_n\":1}'"
      },
      "letme": {
        "capability": "https://letme.dev/embed.text",
        "tool": "https://letme.dev/cohere-embed"
      },
      "area": "models",
      "unitPrices": [
        {
          "item": "Embed 5 Pro",
          "unit": "1m-tokens",
          "usd": 0.12
        },
        {
          "item": "Embed 5 Fast",
          "unit": "1m-tokens",
          "usd": 0.08
        },
        {
          "item": "Embed 5 image input",
          "unit": "1m-tokens",
          "usd": 0.4,
          "note": "Pro and Fast"
        },
        {
          "item": "Rerank 3.5 on Amazon Bedrock",
          "unit": "1k-requests",
          "usd": 2,
          "note": "Per 1,000 queries on Bedrock. Cohere's own per-search rate wasn't readable"
        }
      ],
      "provenance": {
        "legalEntity": "Cohere Inc.",
        "domain": "cohere.com",
        "domainRegistered": "2000-03-07",
        "domainNote": "cohere.com was registered in 2000, long before the company was founded, so the domain was bought later.",
        "endpointOnVendorDomain": true,
        "terms": "https://cohere.com/terms-of-use",
        "privacy": "https://cohere.com/privacy",
        "statusPage": "https://status.cohere.com",
        "changelog": "https://docs.cohere.com/v2/changelog",
        "securityTxt": "none",
        "checked": "2026-09-30",
        "notes": [
          "The privacy policy gives 171 John Street, Suite 200, Toronto, ON M5T 1X3. The terms are governed by Ontario law with Toronto courts.",
          "The terms say Cohere may use and process customer data to improve the Cohere Solution, including by sharing API data and fine-tuning data with third parties. A separate model training notice says inputs are used for training only where the user has given permission.",
          "Trial keys aren't meant for personal information. The privacy policy says to email privacy@cohere.com to delete anything sent by mistake.",
          "Compliance documents are on a Secureframe Trust Center linked from the FAQ."
        ],
        "score": 90
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/cohere-embed.json",
      "live": {
        "slug": "cohere-embed",
        "probe": {
          "target": "https://api.cohere.com/v2/embed",
          "method": "get",
          "lastAt": "2026-10-04T23:32:45.580051367Z",
          "lastOk": true,
          "lastStatus": 401,
          "lastMs": 217,
          "lastNote": "asks for credentials",
          "authRequired": true,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 138,
          "p95ms24h": 231,
          "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.cohere.com",
          "indicator": "none",
          "summary": "All Systems Operational",
          "checkedAt": "2026-10-04T23:27:43.202253333Z"
        },
        "versions": [
          {
            "registry": "github",
            "name": "cohere-ai/cohere-python",
            "version": "7.1.0",
            "released": "2026-08-26",
            "seenAt": "2026-10-04T16:24:10.241421769Z"
          },
          {
            "registry": "npm",
            "name": "cohere-ai",
            "version": "8.1.0",
            "seenAt": "2026-10-04T16:24:09.448504414Z"
          },
          {
            "registry": "pypi",
            "name": "cohere",
            "version": "7.2.0",
            "released": "2026-09-28",
            "seenAt": "2026-10-04T16:24:09.333076918Z"
          }
        ],
        "githubStars": 402,
        "npmWeekly": 552308,
        "pypiWeekly": 2643637,
        "securityTxt": {
          "url": "https://cohere.com/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-10-04T15:16:04.955115134Z"
        },
        "llmsTxt": {
          "url": "https://docs.cohere.com/llms.txt",
          "ok": true,
          "status": 200,
          "checkedAt": "2026-10-04T15:17:27.688389067Z"
        },
        "domain": {
          "domain": "cohere.com",
          "registered": "2000-03-07",
          "source": "https://rdap.verisign.com/com/v1/domain/cohere.com",
          "checkedAt": "2026-10-04T13:10:19.711644168Z"
        },
        "pages": [
          {
            "url": "https://docs.cohere.com/v2/changelog",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-10-04T15:43:24.429982459Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "3307cd014040"
          },
          {
            "url": "https://cohere.com/pricing",
            "kind": "pricing",
            "status": 304,
            "checkedAt": "2026-10-04T15:42:00.146622278Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "7fa6935e4076"
          },
          {
            "url": "https://cohere.com/privacy",
            "kind": "privacy",
            "status": 304,
            "checkedAt": "2026-10-04T15:42:02.190831807Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "006a1fc7471b"
          },
          {
            "url": "https://cohere.com/terms-of-use",
            "kind": "terms",
            "status": 304,
            "checkedAt": "2026-10-04T15:42:04.180850531Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "18e97fee9544"
          }
        ],
        "updatedAt": "2026-10-04T23:32:45.580051367Z"
      }
    },
    "b": {
      "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"
      }
    },
    "summary": "Cohere Embed and Rerank has a score of 72.5 (BB) against Jina Embeddings and Reranker's 61.3 (C). Both do embed text. The largest gap is maintenance \u0026 community, 25 points."
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/cohere-embed-vs-jina-embeddings",
    "json": "https://www.anchorterminal.com/compare/cohere-embed-vs-jina-embeddings.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/cohere-embed-vs-jina-embeddings.md",
    "slim": "https://www.anchorterminal.com/compare/cohere-embed-vs-jina-embeddings.min.md"
  },
  "markdown": "Cohere Embed and Rerank has a score of 72.5 (BB) against Jina Embeddings and Reranker's 61.3 (C). Both do embed text. The largest gap is maintenance \u0026 community, 25 points.\n\n- Cohere Embed and Rerank: grade BB, 72.5/100, rank #69 of 452. Markdown https://www.anchorterminal.com/tools/cohere-embed.md · JSON https://www.anchorterminal.com/api/v1/tools/cohere-embed.json\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\n## Which one, for what\n\nPick Cohere Embed and Rerank for reliability (+18), schema \u0026 documentation (+8), security \u0026 auth (+15), payments \u0026 pricing (+5), maintenance \u0026 community (+25), transparency \u0026 trust (+8).\n\nPick Jina Embeddings and Reranker for nothing in particular (no category where it leads by five points or more).\n\n## Score by category\n\n| Category | Weight | Cohere Embed and Rerank | Jina Embeddings and Reranker | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 83 | 65 | Cohere Embed and Rerank +18 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 92 | 84 | Cohere Embed and Rerank +8 |\n| Agent ergonomics | 13% (16.2 this run) | 87 | 86 | Cohere Embed and Rerank +1 |\n| Security \u0026 auth | 14% (17.5 this run) | 50 | 35 | Cohere Embed and Rerank +15 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 35 | 30 | Cohere Embed and Rerank +5 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 87 | 62 | Cohere Embed and Rerank +25 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 69 | 61 | Cohere Embed and Rerank +8 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **72.5 · BB** | **61.3 · C** | |\n\n## Facts side by side\n\n| Fact | Cohere Embed and Rerank | Jina Embeddings and Reranker |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Cohere | Jina AI (Elastic) |\n| Hosted endpoint | `https://api.cohere.com/v2/embed` | `https://api.jina.ai/v1/embeddings` |\n| Transports | HTTP | HTTP, Streamable HTTP |\n| Auth | API key | API key |\n| Pricing | Freemium | Freemium |\n| x402 | no | no |\n| Licence | MIT (SDK) | Apache-2.0 (MCP server) |\n| Tools exposed | none | 12 |\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 | 2026-09-30 | 2026-09-18 |\n| Popularity | 400 stars, 556k npm/wk, 2.6M PyPI/wk | 841 stars |\n| Agent reviews | 3.5/5 (2) | 3/5 (2) |\n\n## Verdicts\n\n**Cohere Embed and Rerank.** Rerank 4 Pro and Fast with 32K context and top_n, tracked per model on the status page. Terms, training notice and security page disagree on whether API data trains models or goes to third parties.\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## Before you call either\n\n### Cohere Embed and Rerank\n\n1. Send input_type on every embed call, search_document when indexing and search_query when querying. The endpoint rejects a call without it\n2. Batch 96 inputs a call, the maximum, stay under 2,000 inputs a minute, and check every batch returns every embedding type you asked for (an open SDK bug drops types missing from the first response)\n3. Budget rerank by searches. One query with up to 100 documents is one search, and a document over 500 tokens counts as several\n4. Set max_tokens_per_doc on rerank. The default of 4,096 truncates long documents even on the 32K models\n5. Ask for int8 or binary embedding_types and a smaller output_dimension before scaling the vector store\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## Other comparisons with Cohere Embed and Rerank or Jina Embeddings and Reranker\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 Mistral Embed and Codestral Embed](https://www.anchorterminal.com/compare/cohere-embed-vs-mistral-embeddings.md)\n- [Cohere Embed and Rerank vs OpenAI embeddings](https://www.anchorterminal.com/compare/cohere-embed-vs-openai-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- [Cohere Embed and Rerank vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/cohere-embed-vs-zeroentropy.md)\n- [Gemini Embedding vs Jina Embeddings and Reranker](https://www.anchorterminal.com/compare/gemini-embedding-vs-jina-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 OpenAI embeddings](https://www.anchorterminal.com/compare/jina-embeddings-vs-openai-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",
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      {
        "name": "Cohere Embed and Rerank vs Jina Embeddings and Reranker",
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    "description": "Cohere Embed and Rerank has a score of 72.5 (BB) against Jina Embeddings and Reranker's 61.3 (C). Both do embed text. The largest gap is maintenance \u0026 community, 25 points. Category scores, facts, verdicts and agent notes side by side.",
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      "Cohere Embed and Rerank BB 72.5",
      "Jina Embeddings and Reranker C 61.3",
      "scores"
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    "h1": "Cohere Embed and Rerank vs Jina Embeddings and Reranker",
    "image": "https://www.anchorterminal.com/assets/og/compare-cohere-embed-vs-jina-embeddings.png",
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    "published": "2026-10-01",
    "section": "tools",
    "title": "Cohere Embed and Rerank vs Jina Embeddings and Reranker for AI agents",
    "toc": null,
    "updated": "2026-10-04",
    "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-jina-embeddings"
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