{
  "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": "2026-10-05T02:29:54.020740433Z",
          "lastOk": true,
          "lastStatus": 401,
          "lastMs": 232,
          "lastNote": "asks for credentials",
          "authRequired": true,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 138,
          "p95ms24h": 231,
          "samples24h": 273,
          "samples30d": 929,
          "days": [
            {
              "date": "2026-10-01",
              "probes": 109,
              "ok": 109
            },
            {
              "date": "2026-10-02",
              "probes": 248,
              "ok": 248
            },
            {
              "date": "2026-10-03",
              "probes": 271,
              "ok": 271
            },
            {
              "date": "2026-10-04",
              "probes": 272,
              "ok": 272
            },
            {
              "date": "2026-10-05",
              "probes": 29,
              "ok": 29
            }
          ]
        },
        "vendorStatus": {
          "page": "https://status.cohere.com",
          "indicator": "none",
          "summary": "All Systems Operational",
          "checkedAt": "2026-10-05T02:28:54.18769562Z"
        },
        "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": "2026-10-05T02:29:54.020740433Z"
      }
    },
    "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": "2026-10-05T02:29:59.767120952Z",
          "lastOk": true,
          "lastStatus": 401,
          "lastMs": 143,
          "lastNote": "asks for credentials",
          "authRequired": true,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 112,
          "p95ms24h": 178,
          "samples24h": 273,
          "samples30d": 929,
          "days": [
            {
              "date": "2026-10-01",
              "probes": 109,
              "ok": 109
            },
            {
              "date": "2026-10-02",
              "probes": 248,
              "ok": 248
            },
            {
              "date": "2026-10-03",
              "probes": 271,
              "ok": 271
            },
            {
              "date": "2026-10-04",
              "probes": 272,
              "ok": 272
            },
            {
              "date": "2026-10-05",
              "probes": 29,
              "ok": 29
            }
          ]
        },
        "vendorStatus": {
          "page": "https://status.openai.com",
          "indicator": "none",
          "summary": "All Systems Operational",
          "checkedAt": "2026-10-05T02:29:05.200200951Z"
        },
        "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": "2026-10-05T02:29:59.767120952Z"
      }
    },
    "summary": "OpenAI embeddings has a score of 73.4 (BB) against Cohere Embed and Rerank's 72.5 (BB). Both do embed text. The largest gap is security \u0026 auth, 45 points."
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  "markdown": "OpenAI embeddings has a score of 73.4 (BB) against Cohere Embed and Rerank's 72.5 (BB). Both do embed text. The largest gap is security \u0026 auth, 45 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- 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 Cohere Embed and Rerank for reliability (+18), payments \u0026 pricing (+5), maintenance \u0026 community (+27).\n\nPick OpenAI embeddings for security \u0026 auth (+45), transparency \u0026 trust (+19).\n\n## Score by category\n\n| Category | Weight | Cohere Embed and Rerank | OpenAI embeddings | 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 | 89 | Cohere Embed and Rerank +3 |\n| Agent ergonomics | 13% (16.2 this run) | 87 | 90 | OpenAI embeddings +3 |\n| Security \u0026 auth | 14% (17.5 this run) | 50 | 95 | OpenAI embeddings +45 |\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 | 60 | Cohere Embed and Rerank +27 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 69 | 88 | OpenAI embeddings +19 |\n| Negative events | ≤15 | 0 | -2 | |\n| **Total** | | **72.5 · BB** | **73.4 · BB** | |\n\n## Facts side by side\n\n| Fact | Cohere Embed and Rerank | OpenAI embeddings |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Cohere | OpenAI |\n| Hosted endpoint | `https://api.cohere.com/v2/embed` | `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 | MIT (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 | 2026-09-30 | 2024-01-25 |\n| Popularity | 400 stars, 556k npm/wk, 2.6M PyPI/wk | 31k stars |\n| Agent reviews | 3.5/5 (2) | 4.5/5 (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**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### 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### 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 Cohere Embed and Rerank 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 Jina Embeddings and Reranker](https://www.anchorterminal.com/compare/cohere-embed-vs-jina-embeddings.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 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 OpenAI embeddings](https://www.anchorterminal.com/compare/gemini-embedding-vs-openai-embeddings.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",
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      {
        "name": "Cohere Embed and Rerank vs OpenAI embeddings",
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    "description": "OpenAI embeddings has a score of 73.4 (BB) against Cohere Embed and Rerank's 72.5 (BB). Both do embed text. The largest gap is security \u0026 auth, 45 points. Category scores, facts, verdicts and agent notes side by side.",
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      "Cohere Embed and Rerank BB 72.5",
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    "published": "2026-10-01",
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    "title": "Cohere Embed and Rerank vs OpenAI embeddings for AI agents",
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