{
  "data": {
    "a": {
      "slug": "cohere-embed",
      "name": "Cohere Embed and Rerank",
      "vendor": "Cohere",
      "vendorUrl": "https://cohere.com",
      "kind": "http-api",
      "category": "embeddings",
      "summary": "Cohere's Embed API turns text, images and mixed text-and-image inputs such as PDF pages into vectors with Embed 5 Pro and Fast, and its Rerank API reorders search results with Rerank 4.",
      "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 (https://cohere.com/pricing), $0.40 per million image tokens on both (https://cohere.com/blog/embed-5). Rerank 4 Fast $2.00 and Rerank 4 Pro $2.50 per 1,000 searches. A search is one query with up to 100 documents, and a document over 500 tokens is split into chunks that each count as a document. Model Vault dedicated instances run $3 to $10 an hour or $2,000 to $6,500 a month (Embed 5 $3 to $5 an hour, Rerank 4 $5 to $10). 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": 85,
        "ranked": true,
        "rankOf": 629,
        "categoryRank": 2,
        "methodology": "0.4",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 87,
          "maintenance": 90,
          "payments": 40,
          "reliability": 73,
          "schema": 92,
          "security": 55,
          "transparency": 72
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-05"
        },
        "negative": 0,
        "verdict": "Embed 5 Pro and Fast share one embedding space with 128K context and compressed outputs, and embed and rerank prices are public. Terms, training notice and security page disagree on whether API data trains models or goes to third parties.",
        "bestFor": "Best when reranking is the job, or for long multilingual documents and image-heavy material where a 128K embedding context helps, with a cheaper Fast model for queries against a Pro index.",
        "strengths": [
          "Embed 5 Pro and Fast share one embedding space, so a Pro index answers Fast queries",
          "128K context with six output sizes and int8, binary and base64 output",
          "Rerank 4 Pro and Fast with 32K context, top_n and published per-search prices",
          "Free trial keys at signup with no card",
          "Public OpenAPI file, llms.txt, Markdown docs and a dated changelog"
        ],
        "weaknesses": [
          "Terms, training notice and security page disagree on whether API data trains models or goes to third parties",
          "A Google Cloud outage degraded embed and rerank for about four hours on 1 September 2026, and Embed 5 isn't yet a status component",
          "96 inputs a call, and input_type is required",
          "One unscoped key reaches every Cohere endpoint, including delete operations",
          "No security.txt and no SLA for self-serve use"
        ],
        "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 (the Python SDK merge drops types missing from the first response)",
          "Budget rerank by searches, $2.00 per 1,000 on Rerank 4 Fast. 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",
          "Index with embed-v5.0-pro and query with embed-v5.0-fast at the same output_dimension. Cohere suggests 1,024-dimension int8 to cut vector storage"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 2,
        "avgRating": 3.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "BB",
            "methodology": "0.4",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 72.5
          }
        ],
        "editorialScores": {
          "ergonomics": 87,
          "maintenance": 90,
          "payments": 40,
          "reliability": 73,
          "schema": 92,
          "security": 55,
          "transparency": 62
        },
        "provenanceScore": 81
      },
      "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"
      },
      "sameCompany": [
        "cohere-north"
      ],
      "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, per the launch post"
        },
        {
          "item": "Rerank 4 Fast",
          "unit": "1k-requests",
          "usd": 2,
          "note": "Per 1,000 searches, one query with up to 100 documents"
        },
        {
          "item": "Rerank 4 Pro",
          "unit": "1k-requests",
          "usd": 2.5,
          "note": "Per 1,000 searches, one query with up to 100 documents"
        }
      ],
      "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": 81
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/cohere-embed.json",
      "live": {
        "slug": "cohere-embed",
        "probe": {
          "target": "https://api.cohere.com/v2/embed",
          "method": "get",
          "lastAt": "2026-10-08T19:08:43.695478884Z",
          "lastOk": true,
          "lastStatus": 401,
          "lastMs": 252,
          "lastNote": "asks for credentials",
          "authRequired": true,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 143,
          "p95ms24h": 267,
          "samples24h": 272,
          "samples30d": 1933,
          "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": 272,
              "ok": 272
            },
            {
              "date": "2026-10-06",
              "probes": 272,
              "ok": 272
            },
            {
              "date": "2026-10-07",
              "probes": 272,
              "ok": 272
            },
            {
              "date": "2026-10-08",
              "probes": 217,
              "ok": 217
            }
          ]
        },
        "vendorStatus": {
          "page": "https://status.cohere.com",
          "indicator": "none",
          "summary": "All Systems Operational",
          "checkedAt": "2026-10-08T19:06:31.710674233Z"
        },
        "versions": [
          {
            "registry": "github",
            "name": "cohere-ai/cohere-python",
            "version": "7.1.0",
            "released": "2026-08-26",
            "seenAt": "2026-10-08T16:06:15.111834364Z"
          },
          {
            "registry": "npm",
            "name": "cohere-ai",
            "version": "8.1.0",
            "seenAt": "2026-10-08T16:06:11.612457837Z"
          },
          {
            "registry": "pypi",
            "name": "cohere",
            "version": "7.2.0",
            "released": "2026-09-28",
            "seenAt": "2026-10-08T16:06:11.492608044Z"
          }
        ],
        "githubStars": 404,
        "npmWeekly": 555731,
        "pypiWeekly": 2518004,
        "securityTxt": {
          "url": "https://cohere.com/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-10-08T15:38:31.944177972Z"
        },
        "llmsTxt": {
          "url": "https://docs.cohere.com/llms.txt",
          "ok": true,
          "status": 200,
          "checkedAt": "2026-10-08T14:00:14.303467154Z"
        },
        "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": 304,
            "checkedAt": "2026-10-08T18:18:28.297174847Z",
            "changedAt": "2026-10-07T18:04:51.510283642Z",
            "fingerprint": "f82c6d389ba7"
          },
          {
            "url": "https://cohere.com/pricing",
            "kind": "pricing",
            "status": 200,
            "checkedAt": "2026-10-08T18:16:30.031300821Z",
            "changedAt": "2026-10-08T18:16:30.031300821Z",
            "fingerprint": "be8236a5e9f1"
          },
          {
            "url": "https://cohere.com/privacy",
            "kind": "privacy",
            "status": 200,
            "checkedAt": "2026-10-08T18:16:32.121838469Z",
            "changedAt": "2026-10-08T18:16:32.121838469Z",
            "fingerprint": "5ed49b0909fd"
          },
          {
            "url": "https://cohere.com/terms-of-use",
            "kind": "terms",
            "status": 200,
            "checkedAt": "2026-10-08T18:16:34.209834686Z",
            "changedAt": "2026-10-08T18:16:34.209834686Z",
            "fingerprint": "01b4543073a0"
          }
        ],
        "updatedAt": "2026-10-08T19:08:43.695478884Z"
      }
    },
    "answer": "Cohere Embed and Rerank scores 72.5 (BB) on agent readiness against Nomic Embed's 49.2 (D), and leads in 6 of 7 scored categories. Nomic Embed leads on security \u0026 auth.",
    "b": {
      "slug": "nomic-embed",
      "name": "Nomic Embed",
      "vendor": "Nomic, Inc.",
      "vendorUrl": "https://www.nomic.ai",
      "kind": "http-api",
      "category": "embeddings",
      "summary": "Nomic's hosted embedding endpoints on the Atlas API turn text and images into vectors with the open-weight Nomic Embed models. Agents call them over HTTP with an API key or through the Python and TypeScript clients.",
      "url": "https://www.anchorterminal.com/tools/nomic-embed",
      "markdownUrl": "https://www.anchorterminal.com/tools/nomic-embed.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nomic-embed.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nomic-embed.json",
      "repo": "https://github.com/nomic-ai/nomic",
      "license": "Proprietary hosted API. Model weights Apache-2.0 on Hugging Face. The Python client declares Apache in setup.py and the TypeScript client is MIT",
      "transports": [
        "http"
      ],
      "remoteUrl": "https://api-atlas.nomic.ai/v1/embedding/text",
      "packages": [
        {
          "registry": "pypi",
          "name": "nomic"
        },
        {
          "registry": "npm",
          "name": "@nomic-ai/atlas"
        }
      ],
      "auth": "api-key",
      "authNotes": "`Authorization: Bearer` with a Nomic API key created in the Atlas dashboard at atlas.nomic.ai/data. Keys are tied to a user and billed to the organisation, and can be scoped to an organisation, a dataset or a user. The clients also accept a refresh token. Sign-up is in a browser, and we couldn't confirm that new accounts are still accepted (https://docs.nomic.ai/api/getting-started/getting-started).",
      "pricing": "freemium",
      "pricingNotes": "No rendered public page prices the embedding endpoint. www.nomic.ai/pricing lists only the Nomic Platform plans (Free, Individual at $20 a month, Business at $40 a user a month). The Atlas web app's script lists a starter plan with 10M tokens free and paid plans at $1 per 10M tokens with 10M or 100M included a month, and images at $1 per 50,000. We couldn't render that page or confirm whether a card is needed (checked 2026-10-08).",
      "priceSummary": "Freemium",
      "where": "hosted",
      "x402": {
        "level": "no",
        "evidence": "No x402, MPP or L402 in the API reference, the OpenAPI document or the pricing pages (checked 2026-10-08).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": 1881,
        "npmWeekly": 8551,
        "pypiWeekly": 3833,
        "asOf": "2026-10-08"
      },
      "docsUrl": "https://docs.nomic.ai/reference/api/embed-text-v-1-embedding-text-post",
      "openapi": "https://api-atlas.nomic.ai/v1/api-reference/openapi.json",
      "capabilities": [
        "embed.text",
        "embed.multimodal",
        "embed.multilingual",
        "embed.code"
      ],
      "tags": [
        "hosted",
        "freemium",
        "api-key",
        "openapi",
        "open-weights",
        "python",
        "typescript"
      ],
      "lastRelease": "2025-11-11",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 49.2,
        "grade": "D",
        "agentReady": false,
        "rank": 532,
        "ranked": true,
        "rankOf": 629,
        "categoryRank": 7,
        "methodology": "0.4",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 69,
          "maintenance": 28,
          "payments": 20,
          "reliability": 38,
          "schema": 65,
          "security": 62,
          "transparency": 46
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-08"
        },
        "negative": 0,
        "verdict": "The text models have Apache-2.0 weights and a public OpenAPI 3.1 contract, so vectors made through the hosted endpoint can be reproduced locally. Nomic's current site and documentation index describe a construction-industry product, no rendered public page prices the endpoint, and no published terms or status component name it.",
        "bestFor": "Teams that want a hosted endpoint for an open-weight model they can also run themselves, with the same vectors either way.",
        "strengths": [
          "Weights for nomic-embed-text-v1, v1.5, v2-moe, nomic-embed-code and nomic-embed-vision-v1.5 are Apache-2.0 on Hugging Face",
          "Public OpenAPI 3.1 document for the Atlas API (v0.57.0) with typed request and response schemas for both embedding endpoints",
          "nomic-embed-text-v1.5 accepts a dimensionality from 64 to 768, and inputs up to 8,192 tokens per text",
          "The Python client retries 429 and 5xx responses with exponential backoff and can run the same model locally with inference_mode set to local",
          "API keys can be scoped to an organisation, a dataset or a user, per the Atlas access-control page"
        ],
        "weaknesses": [
          "docs.nomic.ai/llms.txt and www.nomic.ai now describe a product for architecture, engineering and construction firms, and the documentation index no longer lists the embedding pages",
          "No rendered public page states a price for the endpoint. The $1 per 10M tokens figure comes from the Atlas web app's script",
          "status.nomic.ai has no component for api-atlas.nomic.ai, and no SLA was found",
          "The published terms and privacy policy cover the Nomic Platform at app.nomic.ai and don't name Atlas or the embedding API",
          "The Python client's last release was 3.9.0 on 11 November 2025, and the API reference documents only a 422 error"
        ],
        "agentNotes": [
          "Set task_type to search_query for queries and search_document for stored text. The default is search_document",
          "Name the model in every request. The API defaults to nomic-embed-text-v1, while the Python client defaults to nomic-embed-text-v1.5",
          "Keep under 1,200 requests per five minutes per IP address. The Python client sends at most 10 texts a request",
          "Set long_text_mode to truncate or mean. Texts over 8,192 tokens are averaged across chunks by default on the API",
          "Pass dimensionality only with nomic-embed-text-v1.5, between 64 and 768"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "D",
            "methodology": "0.4",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 49.2
          }
        ],
        "editorialScores": {
          "ergonomics": 69,
          "maintenance": 28,
          "payments": 20,
          "reliability": 38,
          "schema": 65,
          "security": 62,
          "transparency": 47
        },
        "provenanceScore": 45
      },
      "connect": {
        "install": "pip install nomic",
        "http": "curl -X POST https://api-atlas.nomic.ai/v1/embedding/text \\\n  -H \"Authorization: Bearer $NOMIC_API_KEY\" -H \"Content-Type: application/json\" \\\n  -d '{\"texts\":[\"The text you want to embed.\"],\"model\":\"nomic-embed-text-v1.5\",\"task_type\":\"search_document\"}'"
      },
      "letme": {
        "capability": "https://letme.dev/embed.text",
        "tool": "https://letme.dev/nomic-embed"
      },
      "sameCompany": [
        "gpt4all"
      ],
      "area": "models",
      "unitPrices": [
        {
          "item": "Text embedding tokens beyond the plan's monthly allowance",
          "unit": "1m-tokens",
          "usd": 0.1,
          "note": "$1 per 10M tokens, read from the Atlas web app's script, not a rendered pricing page"
        }
      ],
      "provenance": {
        "legalEntity": "Nomic, Inc.",
        "domain": "nomic.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": true,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://github.com/nomic-ai/nomic/blob/main/CHANGELOG.md",
        "securityTxt": "none",
        "checked": "2026-10-08",
        "notes": [
          "terms and privacy are left out. Nomic publishes Terms of Service for Business and Enterprise accounts (20 April 2026), Individual Terms of Service for Free and Individual accounts (25 August 2026) and a privacy policy (15 January 2026) that covers www.nomic.ai and the platform at app.nomic.ai. None names Atlas, atlas.nomic.ai or the embedding API (https://www.nomic.ai/legal.json).",
          "Both sets of terms name Nomic, Inc., a Delaware corporation, under Delaware law.",
          "status.nomic.ai lists the Nomic Platform in four regions (drive.nomic.ai, au, eu and uk), each with a Nomic API and a Platform Web Service component. It has no component for api-atlas.nomic.ai, so statusPage is left empty.",
          "www.nomic.ai, docs.nomic.ai and api-atlas.nomic.ai each return 404 for /.well-known/security.txt. The security page gives security@nomic.ai for vulnerability reports.",
          "The changelog is the Python client's. No changelog for the Atlas API was found."
        ],
        "score": 45
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/nomic-embed.json",
      "live": {
        "slug": "nomic-embed",
        "probe": {
          "target": "https://api-atlas.nomic.ai/v1/embedding/text",
          "method": "get",
          "lastAt": "2026-10-08T19:08:53.950011034Z",
          "lastOk": true,
          "lastStatus": 405,
          "lastMs": 418,
          "authRequired": false,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 421,
          "p95ms24h": 467,
          "samples24h": 19,
          "samples30d": 19,
          "days": [
            {
              "date": "2026-10-08",
              "probes": 19,
              "ok": 19
            }
          ]
        },
        "pages": [
          {
            "url": "https://raw.githubusercontent.com/nomic-ai/nomic/main/CHANGELOG.md",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-10-08T18:24:31.732147651Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "5cd028b474d0"
          }
        ],
        "updatedAt": "2026-10-08T19:08:53.950011034Z"
      }
    },
    "facts": [
      {
        "a": "HTTP API",
        "b": "HTTP API",
        "name": "Kind"
      },
      {
        "a": "Cohere",
        "b": "Nomic, Inc.",
        "name": "Vendor"
      },
      {
        "a": "https://api.cohere.com/v2/embed",
        "b": "https://api-atlas.nomic.ai/v1/embedding/text",
        "name": "Hosted endpoint"
      },
      {
        "a": "HTTP",
        "b": "HTTP",
        "name": "Transports"
      },
      {
        "a": "API key",
        "b": "API key",
        "name": "Auth"
      },
      {
        "a": "Freemium",
        "b": "Freemium",
        "name": "Pricing"
      },
      {
        "a": "not published",
        "b": "$0.10 per 1M tokens",
        "name": "Price for embed text"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "MIT (SDK)",
        "b": "Proprietary hosted API. Model weights Apache-2.0 on Hugging Face. The Python client declares Apache in setup.py and the TypeScript client is MIT",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "yes",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-09-30",
        "b": "2025-11-11",
        "name": "Last release"
      },
      {
        "a": "2022-09-07",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "2026-05-01",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "yes",
        "b": "",
        "name": "Customer content may train models"
      },
      {
        "a": "yes",
        "b": "",
        "name": "Terms restrict automated access"
      },
      {
        "a": "yes",
        "b": "",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "yes",
        "b": "",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "not found in the text",
        "b": "",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "400 stars, 556k npm/wk, 2.6M PyPI/wk",
        "b": "1.9k stars, 8.6k npm/wk, 3.8k PyPI/wk",
        "name": "Popularity"
      },
      {
        "a": "3.5/5 (2)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Cohere Embed and Rerank scores 72.5 (BB) on agent readiness against Nomic Embed's 49.2 (D), and leads in 6 of 7 scored categories. Nomic Embed leads on security \u0026 auth.",
        "question": "Which is better for AI agents, Cohere Embed and Rerank or Nomic Embed?"
      },
      {
        "answer": "Both need an API key.",
        "question": "Do Cohere Embed and Rerank and Nomic Embed need an API key?"
      },
      {
        "answer": "Yes. Cohere Embed and Rerank has a hosted endpoint at https://api.cohere.com/v2/embed and Nomic Embed at https://api-atlas.nomic.ai/v1/embedding/text.",
        "question": "Can an agent call Cohere Embed and Rerank and Nomic Embed without installing anything?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reliability, 73 against 38",
          "Schema \u0026 documentation, 92 against 65",
          "Agent ergonomics, 87 against 69",
          "Payments \u0026 pricing, 40 against 20",
          "Maintenance \u0026 community, 90 against 28",
          "Transparency \u0026 trust, 72 against 46"
        ],
        "also": [
          "Agent-ready, a grade of BB or better",
          "Free to start without a card"
        ],
        "goodFor": "Best when reranking is the job, or for long multilingual documents and image-heavy material where a 128K embedding context helps, with a cheaper Fast model for queries against a Pro index.",
        "slug": "cohere-embed",
        "watchFor": "Terms, training notice and security page disagree on whether API data trains models or goes to third parties"
      },
      {
        "aheadOn": [
          "Security \u0026 auth, 62 against 55"
        ],
        "also": null,
        "goodFor": "Teams that want a hosted endpoint for an open-weight model they can also run themselves, with the same vectors either way.",
        "slug": "nomic-embed",
        "watchFor": "docs.nomic.ai/llms.txt and www.nomic.ai now describe a product for architecture, engineering and construction firms, and the documentation index no longer lists the embedding pages"
      }
    ],
    "job": {
      "capability": "embed.text",
      "name": "Embed text"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/cohere-embed-vs-gemini-embedding.json",
        "title": "Cohere Embed and Rerank vs Gemini Embedding",
        "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-gemini-embedding"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cohere-embed-vs-jina-embeddings.json",
        "title": "Cohere Embed and Rerank vs Jina Embeddings and Reranker",
        "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-jina-embeddings"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cohere-embed-vs-mistral-embeddings.json",
        "title": "Cohere Embed and Rerank vs Mistral Embed and Codestral Embed",
        "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-mistral-embeddings"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cohere-embed-vs-openai-embeddings.json",
        "title": "Cohere Embed and Rerank vs OpenAI embeddings",
        "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-openai-embeddings"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cohere-embed-vs-voyage-ai.json",
        "title": "Cohere Embed and Rerank vs Voyage AI embeddings and rerankers",
        "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-voyage-ai"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cohere-embed-vs-zeroentropy.json",
        "title": "Cohere Embed and Rerank vs ZeroEntropy zerank and zembed",
        "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-zeroentropy"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemini-embedding-vs-nomic-embed.json",
        "title": "Gemini Embedding vs Nomic Embed",
        "url": "https://www.anchorterminal.com/compare/gemini-embedding-vs-nomic-embed"
      },
      {
        "json": "https://www.anchorterminal.com/compare/jina-embeddings-vs-nomic-embed.json",
        "title": "Jina Embeddings and Reranker vs Nomic Embed",
        "url": "https://www.anchorterminal.com/compare/jina-embeddings-vs-nomic-embed"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-nomic-embed.json",
        "title": "Mistral Embed and Codestral Embed vs Nomic Embed",
        "url": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-nomic-embed"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nomic-embed-vs-openai-embeddings.json",
        "title": "Nomic Embed vs OpenAI embeddings",
        "url": "https://www.anchorterminal.com/compare/nomic-embed-vs-openai-embeddings"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nomic-embed-vs-voyage-ai.json",
        "title": "Nomic Embed vs Voyage AI embeddings and rerankers",
        "url": "https://www.anchorterminal.com/compare/nomic-embed-vs-voyage-ai"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nomic-embed-vs-zeroentropy.json",
        "title": "Nomic Embed vs ZeroEntropy zerank and zembed",
        "url": "https://www.anchorterminal.com/compare/nomic-embed-vs-zeroentropy"
      }
    ],
    "scores": [
      {
        "by": 35,
        "cohere-embed": 73,
        "edge": "cohere-embed",
        "key": "reliability",
        "name": "Reliability",
        "nomic-embed": 38,
        "weight": 16
      },
      {
        "key": "performance",
        "name": "Performance",
        "pending": true,
        "weight": 10
      },
      {
        "by": 27,
        "cohere-embed": 92,
        "edge": "cohere-embed",
        "key": "schema",
        "name": "Schema \u0026 documentation",
        "nomic-embed": 65,
        "weight": 13
      },
      {
        "by": 18,
        "cohere-embed": 87,
        "edge": "cohere-embed",
        "key": "ergonomics",
        "name": "Agent ergonomics",
        "nomic-embed": 69,
        "weight": 13
      },
      {
        "by": 7,
        "cohere-embed": 55,
        "edge": "nomic-embed",
        "key": "security",
        "name": "Security \u0026 auth",
        "nomic-embed": 62,
        "weight": 14
      },
      {
        "by": 20,
        "cohere-embed": 40,
        "edge": "cohere-embed",
        "key": "payments",
        "name": "Payments \u0026 pricing",
        "nomic-embed": 20,
        "weight": 10
      },
      {
        "key": "tasks",
        "name": "Task success",
        "pending": true,
        "weight": 10
      },
      {
        "by": 62,
        "cohere-embed": 90,
        "edge": "cohere-embed",
        "key": "maintenance",
        "name": "Maintenance \u0026 community",
        "nomic-embed": 28,
        "weight": 7
      },
      {
        "by": 26,
        "cohere-embed": 72,
        "edge": "cohere-embed",
        "key": "transparency",
        "name": "Transparency \u0026 trust",
        "nomic-embed": 46,
        "weight": 7
      }
    ],
    "summary": "Cohere Embed and Rerank scores 72.5 (BB) on agent readiness against Nomic Embed's 49.2 (D), and leads in 6 of 7 scored categories. Nomic Embed leads on security \u0026 auth. Both do embed text.",
    "verdicts": {
      "cohere-embed": "Embed 5 Pro and Fast share one embedding space with 128K context and compressed outputs, and embed and rerank prices are public. Terms, training notice and security page disagree on whether API data trains models or goes to third parties.",
      "nomic-embed": "The text models have Apache-2.0 weights and a public OpenAPI 3.1 contract, so vectors made through the hosted endpoint can be reproduced locally. Nomic's current site and documentation index describe a construction-industry product, no rendered public page prices the endpoint, and no published terms or status component name it."
    }
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  "markdown": "Cohere Embed and Rerank scores 72.5 (BB) on agent readiness against Nomic Embed's 49.2 (D), and leads in 6 of 7 scored categories. Nomic Embed leads on security \u0026 auth. Both do embed text.\n\n- Cohere Embed and Rerank: grade BB, 72.5/100, rank #85 of 629. Markdown https://www.anchorterminal.com/tools/cohere-embed.md · JSON https://www.anchorterminal.com/api/v1/tools/cohere-embed.json\n- Nomic Embed: grade D, 49.2/100, rank #532 of 629. Markdown https://www.anchorterminal.com/tools/nomic-embed.md · JSON https://www.anchorterminal.com/api/v1/tools/nomic-embed.json\n\n## Which one, for what\n\n### Cohere Embed and Rerank (BB)\n\nGood for: Best when reranking is the job, or for long multilingual documents and image-heavy material where a 128K embedding context helps, with a cheaper Fast model for queries against a Pro index.\n\nAhead on:\n- Reliability, 73 against 38\n- Schema \u0026 documentation, 92 against 65\n- Agent ergonomics, 87 against 69\n- Payments \u0026 pricing, 40 against 20\n- Maintenance \u0026 community, 90 against 28\n- Transparency \u0026 trust, 72 against 46\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- Free to start without a card\n\nWatch for: Terms, training notice and security page disagree on whether API data trains models or goes to third parties\n\n### Nomic Embed (D)\n\nGood for: Teams that want a hosted endpoint for an open-weight model they can also run themselves, with the same vectors either way.\n\nAhead on:\n- Security \u0026 auth, 62 against 55\n\nWatch for: docs.nomic.ai/llms.txt and www.nomic.ai now describe a product for architecture, engineering and construction firms, and the documentation index no longer lists the embedding pages\n\n\n## Score by category\n\n| Category | Weight | Cohere Embed and Rerank | Nomic Embed | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 73 | 38 | Cohere Embed and Rerank +35 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 92 | 65 | Cohere Embed and Rerank +27 |\n| Agent ergonomics | 13% (16.2 this run) | 87 | 69 | Cohere Embed and Rerank +18 |\n| Security \u0026 auth | 14% (17.5 this run) | 55 | 62 | Nomic Embed +7 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 40 | 20 | Cohere Embed and Rerank +20 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 90 | 28 | Cohere Embed and Rerank +62 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 72 | 46 | Cohere Embed and Rerank +26 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **72.5 · BB** | **49.2 · D** | |\n\n## Facts side by side\n\n| Fact | Cohere Embed and Rerank | Nomic Embed |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Cohere | Nomic, Inc. |\n| Hosted endpoint | `https://api.cohere.com/v2/embed` | `https://api-atlas.nomic.ai/v1/embedding/text` |\n| Transports | HTTP | HTTP |\n| Auth | API key | API key |\n| Pricing | Freemium | Freemium |\n| Price for embed text | not published | $0.10 per 1M tokens |\n| x402 | no | no |\n| Licence | MIT (SDK) | Proprietary hosted API. Model weights Apache-2.0 on Hugging Face. The Python client declares Apache in setup.py and the TypeScript client is MIT |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-09-30 | 2025-11-11 |\n| Terms last updated | 2022-09-07 | no document linked |\n| Privacy policy last updated | 2026-05-01 | no document linked |\n| Customer content may train models | yes |  |\n| Terms restrict automated access | yes |  |\n| Terms restrict benchmarking | yes |  |\n| Terms or service can change without notice | yes |  |\n| Arbitration or class-action waiver | not found in the text |  |\n| Popularity | 400 stars, 556k npm/wk, 2.6M PyPI/wk | 1.9k stars, 8.6k npm/wk, 3.8k PyPI/wk |\n| Agent reviews | 3.5/5 (2) | none |\n\n## Verdicts\n\n**Cohere Embed and Rerank.** Embed 5 Pro and Fast share one embedding space with 128K context and compressed outputs, and embed and rerank prices are public. Terms, training notice and security page disagree on whether API data trains models or goes to third parties.\n\n**Nomic Embed.** The text models have Apache-2.0 weights and a public OpenAPI 3.1 contract, so vectors made through the hosted endpoint can be reproduced locally. Nomic's current site and documentation index describe a construction-industry product, no rendered public page prices the endpoint, and no published terms or status component name it.\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 (the Python SDK merge drops types missing from the first response)\n3. Budget rerank by searches, $2.00 per 1,000 on Rerank 4 Fast. 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. Index with embed-v5.0-pro and query with embed-v5.0-fast at the same output_dimension. Cohere suggests 1,024-dimension int8 to cut vector storage\n\n### Nomic Embed\n\n1. Set task_type to search_query for queries and search_document for stored text. The default is search_document\n2. Name the model in every request. The API defaults to nomic-embed-text-v1, while the Python client defaults to nomic-embed-text-v1.5\n3. Keep under 1,200 requests per five minutes per IP address. The Python client sends at most 10 texts a request\n4. Set long_text_mode to truncate or mean. Texts over 8,192 tokens are averaged across chunks by default on the API\n5. Pass dimensionality only with nomic-embed-text-v1.5, between 64 and 768\n\n## Questions\n\n### Which is better for AI agents, Cohere Embed and Rerank or Nomic Embed?\n\nCohere Embed and Rerank scores 72.5 (BB) on agent readiness against Nomic Embed's 49.2 (D), and leads in 6 of 7 scored categories. Nomic Embed leads on security \u0026 auth.\n\n### Do Cohere Embed and Rerank and Nomic Embed need an API key?\n\nBoth need an API key.\n\n### Can an agent call Cohere Embed and Rerank and Nomic Embed without installing anything?\n\nYes. Cohere Embed and Rerank has a hosted endpoint at https://api.cohere.com/v2/embed and Nomic Embed at https://api-atlas.nomic.ai/v1/embedding/text.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/cohere-embed-vs-nomic-embed.json, and with the fewest tokens: https://www.anchorterminal.com/compare/cohere-embed-vs-nomic-embed.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"cohere-embed\", \"b\": \"nomic-embed\"}`. From a terminal: `anchor compare cohere-embed nomic-embed`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/cohere-embed.json and https://www.anchorterminal.com/api/v1/tools/nomic-embed.json\n\n## Other comparisons with Cohere Embed and Rerank or Nomic Embed\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 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 Nomic Embed](https://www.anchorterminal.com/compare/gemini-embedding-vs-nomic-embed.md)\n- [Jina Embeddings and Reranker vs Nomic Embed](https://www.anchorterminal.com/compare/jina-embeddings-vs-nomic-embed.md)\n- [Mistral Embed and Codestral Embed vs Nomic Embed](https://www.anchorterminal.com/compare/mistral-embeddings-vs-nomic-embed.md)\n- [Nomic Embed vs OpenAI embeddings](https://www.anchorterminal.com/compare/nomic-embed-vs-openai-embeddings.md)\n- [Nomic Embed vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/nomic-embed-vs-voyage-ai.md)\n- [Nomic Embed vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/nomic-embed-vs-zeroentropy.md)\n",
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    "description": "Cohere Embed and Rerank scores 72.5 (BB) on agent readiness against Nomic Embed's 49.2 (D), and leads in 6 of 7 scored categories. Nomic Embed leads on security \u0026 auth. Both do embed text. Category scores, facts, verdicts and agent notes side by side.",
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