{
  "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
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        ],
        "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": "2026-10-05T03:17:24.233091959Z",
          "lastOk": true,
          "lastStatus": 401,
          "lastMs": 135,
          "lastNote": "asks for credentials",
          "authRequired": true,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 138,
          "p95ms24h": 230,
          "samples24h": 273,
          "samples30d": 938,
          "days": [
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              "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": 38,
              "ok": 38
            }
          ]
        },
        "vendorStatus": {
          "page": "https://status.cohere.com",
          "indicator": "none",
          "summary": "All Systems Operational",
          "checkedAt": "2026-10-05T03:18:58.491500512Z"
        },
        "versions": [
          {
            "registry": "github",
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            "released": "2026-08-26",
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          },
          {
            "registry": "npm",
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          },
          {
            "registry": "pypi",
            "name": "cohere",
            "version": "7.2.0",
            "released": "2026-09-28",
            "seenAt": "2026-10-04T16:24:09.333076918Z"
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        ],
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        "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",
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            "kind": "changelog",
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            "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"
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          {
            "url": "https://cohere.com/privacy",
            "kind": "privacy",
            "status": 304,
            "checkedAt": "2026-10-04T15:42:02.190831807Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "006a1fc7471b"
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          {
            "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"
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        ],
        "updatedAt": "2026-10-05T03:18:58.491500512Z"
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    },
    "b": {
      "slug": "gemini-embedding",
      "name": "Gemini Embedding",
      "vendor": "Google",
      "vendorUrl": "https://ai.google.dev",
      "kind": "http-api",
      "category": "embeddings",
      "summary": "gemini-embedding-2, Google's multimodal embedding model, takes text, images, video, audio and PDFs into one 3072-dimension space (truncatable to 128) at 8,192 input tokens in 100+ languages.",
      "url": "https://www.anchorterminal.com/tools/gemini-embedding",
      "markdownUrl": "https://www.anchorterminal.com/tools/gemini-embedding.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemini-embedding.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemini-embedding.json",
      "repo": "https://github.com/googleapis/python-genai",
      "license": "Apache-2.0 (SDK)",
      "transports": [
        "http"
      ],
      "remoteUrl": "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-2:embedContent",
      "packages": [
        {
          "registry": "pypi",
          "name": "google-genai"
        },
        {
          "registry": "npm",
          "name": "@google/genai"
        }
      ],
      "auth": "api-key",
      "authNotes": "`x-goog-api-key` header with a key from AI Studio on the Gemini Developer API. On Vertex AI it's a Google Cloud OAuth token and a project.",
      "pricing": "freemium",
      "pricingNotes": "On Vertex AI, Gemini Embedding 2 text input is $0.20 per million tokens online and $0.10 in batch, image input $0.45 per million tokens, video $12.00 and audio $6.50 per million tokens, with no output charge (https://cloud.google.com/vertex-ai/generative-ai/pricing). The Gemini Developer API pricing page lists the embedding models further down a page too long for our fetch to read, so we quote Vertex. Google's blog puts the Batch API at 50 per cent of the standard embedding price (https://developers.googleblog.com/building-with-gemini-embedding-2/).",
      "priceSummary": "Freemium",
      "where": "hosted",
      "x402": {
        "level": "no",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": 3992,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-09-30"
      },
      "docsUrl": "https://ai.google.dev/gemini-api/docs/embeddings",
      "llmsTxt": "https://ai.google.dev/gemini-api/docs/llms.txt",
      "capabilities": [
        "embed.text",
        "embed.multimodal",
        "embed.code",
        "embed.multilingual"
      ],
      "tags": [
        "official",
        "hosted",
        "freemium",
        "llms-txt",
        "python",
        "typescript",
        "batch",
        "closed-source"
      ],
      "lastRelease": "2026-04-22",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 71,
        "grade": "BB",
        "agentReady": true,
        "rank": 90,
        "ranked": true,
        "rankOf": 452,
        "categoryRank": 3,
        "methodology": "0.3",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 86,
          "maintenance": 75,
          "payments": 30,
          "reliability": 65,
          "schema": 89,
          "security": 70,
          "transparency": 80
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-01"
        },
        "negative": 0,
        "verdict": "Text, images, video, audio and PDFs interleaved in one request and one vector space. $0.20 per million text tokens, against $0.02 for OpenAI's small model.",
        "strengths": [
          "Text, images, video, audio and PDFs interleaved in one request and one vector space",
          "Any output size from 128 to 3072, with truncated vectors returned normalised",
          "Batch API at half the standard embedding price",
          "Keys can be restricted to the Gemini API and to IPs or apps, and Vertex AI adds IAM roles and audit logs",
          "llms.txt with Markdown copies of every docs page, and a public Discovery document"
        ],
        "weaknesses": [
          "$0.20 per million text tokens, against $0.02 for OpenAI's small model",
          "8,192 input tokens and float output only",
          "No reranker on the Gemini API",
          "Rate limits for embedding models are only visible in the AI Studio dashboard",
          "Free-tier data is used to improve Google products, and zero retention is Vertex-only"
        ],
        "agentNotes": [
          "Don't send task_type to gemini-embedding-2. Prefix the text instead, `task: search result | query: ...` for queries and `title: ... | text: ...` for documents",
          "Ask for output_dimensionality 768 unless you need 3072. Google recommends 768, 1536 or 3072, and the shorter vectors come back normalised",
          "Use batchEmbedContents for indexing, and the Batch API for anything large, at half price",
          "Cap a request at 6 images, 120 seconds of video, 180 seconds of audio and one 6-page PDF. Split longer media first",
          "Don't mix vectors from gemini-embedding-001 and gemini-embedding-2 in one index"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 2,
        "avgRating": 3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "BB",
            "methodology": "0.3",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 71
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        ],
        "editorialScores": {
          "ergonomics": 86,
          "maintenance": 75,
          "payments": 30,
          "reliability": 65,
          "schema": 89,
          "security": 70,
          "transparency": 60
        },
        "provenanceScore": 100
      },
      "connect": {
        "install": "pip install google-genai   # or: npm i @google/genai",
        "http": "curl \"https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-2:embedContent\" \\\n  -H \"x-goog-api-key: $GEMINI_API_KEY\" -H \"content-type: application/json\" \\\n  -d '{\"content\":{\"parts\":[{\"text\":\"task: search result | query: What does the embeddings endpoint return?\"}]},\"output_dimensionality\":768}'"
      },
      "letme": {
        "capability": "https://letme.dev/embed.text",
        "tool": "https://letme.dev/gemini-embedding"
      },
      "sameCompany": [
        "gemini-api",
        "vertex-ai-tuning",
        "google-model-armor",
        "google-imagen",
        "google-veo",
        "google-lyria",
        "google-speech-to-text",
        "google-adk",
        "google-secret-manager",
        "google-weather-api",
        "chrome-devtools-mcp",
        "google-maps-platform",
        "google-cloud-translation",
        "google-calendar-api",
        "google-drive-api",
        "gemini-cli"
      ],
      "area": "models",
      "unitPrices": [
        {
          "item": "gemini-embedding-2 text input (Vertex AI)",
          "unit": "1m-tokens",
          "usd": 0.2
        },
        {
          "item": "gemini-embedding-2 text input, batch (Vertex AI)",
          "unit": "1m-tokens",
          "usd": 0.1
        },
        {
          "item": "gemini-embedding-2 image input (Vertex AI)",
          "unit": "1m-tokens",
          "usd": 0.45
        },
        {
          "item": "gemini-embedding-2 audio input (Vertex AI)",
          "unit": "1m-tokens",
          "usd": 6.5
        },
        {
          "item": "gemini-embedding-2 video input (Vertex AI)",
          "unit": "1m-tokens",
          "usd": 12
        }
      ],
      "provenance": {
        "legalEntity": "Google LLC",
        "domain": "google.com",
        "domainRegistered": "1997-09-15",
        "domainNote": "The endpoint is on googleapis.com, Google's API domain. google.com was registered in 1997.",
        "endpointOnVendorDomain": true,
        "terms": "https://ai.google.dev/gemini-api/terms",
        "privacy": "https://policies.google.com/privacy",
        "statusPage": "https://aistudio.google.com/status",
        "changelog": "https://ai.google.dev/gemini-api/docs/changelog",
        "securityTxt": "valid",
        "checked": "2026-09-30",
        "notes": [
          "Same terms, privacy and data handling as the Gemini Developer API listing. The embedding docs, model page, rate-limit page and the Vertex pricing page were checked on 2026-09-30; the legal documents and security.txt are as checked for that listing.",
          "Prices quoted are Vertex AI's. The Developer API pricing page couldn't be read to the embedding section.",
          "The Vertex AI pricing page still labels Gemini Embedding 2 as preview while Google's blog announced GA on 2026-04-30."
        ],
        "score": 100
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/gemini-embedding.json",
      "live": {
        "slug": "gemini-embedding",
        "probe": {
          "target": "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-2:embedContent",
          "method": "get",
          "lastAt": "2026-10-05T03:17:26.409603804Z",
          "lastOk": true,
          "lastStatus": 404,
          "lastMs": 25,
          "authRequired": false,
          "uptime24h": 100,
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          "p95ms24h": 67,
          "samples24h": 273,
          "samples30d": 938,
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              "date": "2026-10-01",
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              "ok": 109
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            {
              "date": "2026-10-02",
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              "ok": 248
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            {
              "date": "2026-10-03",
              "probes": 271,
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            {
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              "ok": 272
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            {
              "date": "2026-10-05",
              "probes": 38,
              "ok": 38
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        "securityTxt": {
          "url": "https://google.com/.well-known/security.txt",
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          "expires": "2030-04-01T00:00:00z",
          "checkedAt": "2026-10-04T15:15:53.387118101Z"
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          "url": "https://ai.google.dev/gemini-api/docs/llms.txt",
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  "markdown": "Cohere Embed and Rerank has a score of 72.5 (BB) against Gemini Embedding's 71 (BB). Both do embed text. The largest gap is security \u0026 auth, 20 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- Gemini Embedding: grade BB, 71/100, rank #90 of 452. Markdown https://www.anchorterminal.com/tools/gemini-embedding.md · JSON https://www.anchorterminal.com/api/v1/tools/gemini-embedding.json\n\n## Which one, for what\n\nPick Cohere Embed and Rerank for reliability (+18), payments \u0026 pricing (+5), maintenance \u0026 community (+12).\n\nPick Gemini Embedding for security \u0026 auth (+20), transparency \u0026 trust (+11).\n\n## Score by category\n\n| Category | Weight | Cohere Embed and Rerank | Gemini Embedding | 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 | 86 | Cohere Embed and Rerank +1 |\n| Security \u0026 auth | 14% (17.5 this run) | 50 | 70 | Gemini Embedding +20 |\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 | 75 | Cohere Embed and Rerank +12 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 69 | 80 | Gemini Embedding +11 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **72.5 · BB** | **71 · BB** | |\n\n## Facts side by side\n\n| Fact | Cohere Embed and Rerank | Gemini Embedding |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Cohere | Google |\n| Hosted endpoint | `https://api.cohere.com/v2/embed` | `https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-2:embedContent` |\n| Transports | HTTP | HTTP |\n| Auth | API key | API key |\n| Pricing | Freemium | Freemium |\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 | 2026-04-22 |\n| Popularity | 400 stars, 556k npm/wk, 2.6M PyPI/wk | 4k 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**Gemini Embedding.** Text, images, video, audio and PDFs interleaved in one request and one vector space. $0.20 per million text tokens, against $0.02 for OpenAI's small model.\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### Gemini Embedding\n\n1. Don't send task_type to gemini-embedding-2. Prefix the text instead, `task: search result | query: ...` for queries and `title: ... | text: ...` for documents\n2. Ask for output_dimensionality 768 unless you need 3072. Google recommends 768, 1536 or 3072, and the shorter vectors come back normalised\n3. Use batchEmbedContents for indexing, and the Batch API for anything large, at half price\n4. Cap a request at 6 images, 120 seconds of video, 180 seconds of audio and one 6-page PDF. Split longer media first\n5. Don't mix vectors from gemini-embedding-001 and gemini-embedding-2 in one index\n\n## Other comparisons with Cohere Embed and Rerank or Gemini Embedding\n\n- [Cohere Embed and Rerank vs Jina Embeddings and Reranker](https://www.anchorterminal.com/compare/cohere-embed-vs-jina-embeddings.md)\n- [Cohere Embed and Rerank vs 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- [Gemini Embedding vs Mistral Embed and Codestral Embed](https://www.anchorterminal.com/compare/gemini-embedding-vs-mistral-embeddings.md)\n- [Gemini Embedding vs OpenAI embeddings](https://www.anchorterminal.com/compare/gemini-embedding-vs-openai-embeddings.md)\n- [Gemini Embedding vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/gemini-embedding-vs-voyage-ai.md)\n- [Gemini Embedding vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/gemini-embedding-vs-zeroentropy.md)\n",
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        "name": "Cohere Embed and Rerank vs Gemini Embedding",
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    "description": "Cohere Embed and Rerank has a score of 72.5 (BB) against Gemini Embedding's 71 (BB). Both do embed text. The largest gap is security \u0026 auth, 20 points. Category scores, facts, verdicts and agent notes side by side.",
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