{
  "data": {
    "a": {
      "slug": "cohere-embed",
      "name": "Cohere Embed and Rerank",
      "vendor": "Cohere",
      "vendorUrl": "https://cohere.com",
      "kind": "http-api",
      "category": "embeddings",
      "summary": "Embed 5 (Pro and Fast, released 2026-09-30) embeds text, images and parsed PDFs at 128K context in 100+ languages, at $0.08 to $0.12 per million tokens.",
      "url": "https://www.anchorterminal.com/tools/cohere-embed",
      "markdownUrl": "https://www.anchorterminal.com/tools/cohere-embed.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/cohere-embed.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/cohere-embed.json",
      "repo": "https://github.com/cohere-ai/cohere-python",
      "license": "MIT (SDK)",
      "transports": [
        "http"
      ],
      "remoteUrl": "https://api.cohere.com/v2/embed",
      "packages": [
        {
          "registry": "pypi",
          "name": "cohere"
        },
        {
          "registry": "npm",
          "name": "cohere-ai"
        }
      ],
      "auth": "api-key",
      "authNotes": "`Authorization: Bearer` with a trial or production key from the dashboard. Trial keys are free, rate limited and not for commercial use. Production keys bill monthly.",
      "pricing": "freemium",
      "pricingNotes": "Embed 5 Pro $0.12 and Embed 5 Fast $0.08 per million text tokens, $0.40 per million image tokens on both (https://cohere.com/blog/embed-5). Rerank is billed per search, one query with up to 100 documents, and a document over 500 tokens is split into chunks that each count as a document. The per-search rate on the pricing page renders client-side and we couldn't read it. On Amazon Bedrock, Rerank 3.5 is $2.00 per 1,000 queries (https://aws.amazon.com/bedrock/pricing/). Model Vault dedicated instances run $3 to $10 an hour or $2,000 to $6,500 a month. Trial keys are free, need no card, and are capped at 1,000 calls a month. Bills issue monthly or at $250 outstanding (https://cohere.com/pricing).",
      "priceSummary": "$2 / 1k req",
      "where": "hosted",
      "x402": {
        "level": "no",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": 400,
        "npmWeekly": 555855,
        "pypiWeekly": 2593025,
        "asOf": "2026-09-30"
      },
      "docsUrl": "https://docs.cohere.com/docs/embeddings",
      "llmsTxt": "https://docs.cohere.com/llms.txt",
      "capabilities": [
        "embed.text",
        "embed.multimodal",
        "embed.multilingual",
        "rerank"
      ],
      "tags": [
        "hosted",
        "freemium",
        "free-tier",
        "no-card",
        "llms-txt",
        "python",
        "typescript",
        "enterprise",
        "closed-source"
      ],
      "lastRelease": "2026-09-30",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 72.5,
        "grade": "BB",
        "agentReady": true,
        "rank": 69,
        "ranked": true,
        "rankOf": 452,
        "categoryRank": 2,
        "methodology": "0.3",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 87,
          "maintenance": 87,
          "payments": 35,
          "reliability": 83,
          "schema": 92,
          "security": 50,
          "transparency": 69
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-01"
        },
        "negative": 0,
        "verdict": "Rerank 4 Pro and Fast with 32K context and top_n, tracked per model on the status page. Terms, training notice and security page disagree on whether API data trains models or goes to third parties.",
        "strengths": [
          "Rerank 4 Pro and Fast with 32K context and top_n, tracked per model on the status page",
          "Embed 5 at 128K context with six output sizes and int8, binary and base64 output",
          "Free trial keys at signup with no card",
          "Public OpenAPI file, llms.txt and a dated changelog",
          "Embed 5 Fast at $0.08 per million text tokens"
        ],
        "weaknesses": [
          "Terms, training notice and security page disagree on whether API data trains models or goes to third parties",
          "The per-search rerank price didn't render on the pricing page",
          "96 inputs a call, and input_type is required",
          "One unscoped key reaches every Cohere endpoint, including delete operations",
          "No security.txt, no published subprocessor list found, no SLA"
        ],
        "agentNotes": [
          "Send input_type on every embed call, search_document when indexing and search_query when querying. The endpoint rejects a call without it",
          "Batch 96 inputs a call, the maximum, stay under 2,000 inputs a minute, and check every batch returns every embedding type you asked for (an open SDK bug drops types missing from the first response)",
          "Budget rerank by searches. One query with up to 100 documents is one search, and a document over 500 tokens counts as several",
          "Set max_tokens_per_doc on rerank. The default of 4,096 truncates long documents even on the 32K models",
          "Ask for int8 or binary embedding_types and a smaller output_dimension before scaling the vector store"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 2,
        "avgRating": 3.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "BB",
            "methodology": "0.3",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 72.5
          }
        ],
        "editorialScores": {
          "ergonomics": 87,
          "maintenance": 87,
          "payments": 35,
          "reliability": 83,
          "schema": 92,
          "security": 50,
          "transparency": 47
        },
        "provenanceScore": 90
      },
      "connect": {
        "install": "pip install cohere   # or: npm i cohere-ai",
        "http": "curl -X POST https://api.cohere.com/v2/rerank \\\n  -H \"Authorization: Bearer $COHERE_API_KEY\" -H \"content-type: application/json\" \\\n  -d '{\"model\":\"rerank-v4.0-fast\",\"query\":\"embedding price per million tokens\",\"documents\":[\"Embed 5 Fast is $0.08 per million tokens.\",\"Toronto is in Ontario.\"],\"top_n\":1}'"
      },
      "letme": {
        "capability": "https://letme.dev/embed.text",
        "tool": "https://letme.dev/cohere-embed"
      },
      "area": "models",
      "unitPrices": [
        {
          "item": "Embed 5 Pro",
          "unit": "1m-tokens",
          "usd": 0.12
        },
        {
          "item": "Embed 5 Fast",
          "unit": "1m-tokens",
          "usd": 0.08
        },
        {
          "item": "Embed 5 image input",
          "unit": "1m-tokens",
          "usd": 0.4,
          "note": "Pro and Fast"
        },
        {
          "item": "Rerank 3.5 on Amazon Bedrock",
          "unit": "1k-requests",
          "usd": 2,
          "note": "Per 1,000 queries on Bedrock. Cohere's own per-search rate wasn't readable"
        }
      ],
      "provenance": {
        "legalEntity": "Cohere Inc.",
        "domain": "cohere.com",
        "domainRegistered": "2000-03-07",
        "domainNote": "cohere.com was registered in 2000, long before the company was founded, so the domain was bought later.",
        "endpointOnVendorDomain": true,
        "terms": "https://cohere.com/terms-of-use",
        "privacy": "https://cohere.com/privacy",
        "statusPage": "https://status.cohere.com",
        "changelog": "https://docs.cohere.com/v2/changelog",
        "securityTxt": "none",
        "checked": "2026-09-30",
        "notes": [
          "The privacy policy gives 171 John Street, Suite 200, Toronto, ON M5T 1X3. The terms are governed by Ontario law with Toronto courts.",
          "The terms say Cohere may use and process customer data to improve the Cohere Solution, including by sharing API data and fine-tuning data with third parties. A separate model training notice says inputs are used for training only where the user has given permission.",
          "Trial keys aren't meant for personal information. The privacy policy says to email privacy@cohere.com to delete anything sent by mistake.",
          "Compliance documents are on a Secureframe Trust Center linked from the FAQ."
        ],
        "score": 90
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/cohere-embed.json",
      "live": {
        "slug": "cohere-embed",
        "probe": {
          "target": "https://api.cohere.com/v2/embed",
          "method": "get",
          "lastAt": "2026-10-04T23:32:45.580051367Z",
          "lastOk": true,
          "lastStatus": 401,
          "lastMs": 217,
          "lastNote": "asks for credentials",
          "authRequired": true,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 138,
          "p95ms24h": 231,
          "samples24h": 272,
          "samples30d": 895,
          "days": [
            {
              "date": "2026-10-01",
              "probes": 109,
              "ok": 109
            },
            {
              "date": "2026-10-02",
              "probes": 248,
              "ok": 248
            },
            {
              "date": "2026-10-03",
              "probes": 271,
              "ok": 271
            },
            {
              "date": "2026-10-04",
              "probes": 267,
              "ok": 267
            }
          ]
        },
        "vendorStatus": {
          "page": "https://status.cohere.com",
          "indicator": "none",
          "summary": "All Systems Operational",
          "checkedAt": "2026-10-04T23:27:43.202253333Z"
        },
        "versions": [
          {
            "registry": "github",
            "name": "cohere-ai/cohere-python",
            "version": "7.1.0",
            "released": "2026-08-26",
            "seenAt": "2026-10-04T16:24:10.241421769Z"
          },
          {
            "registry": "npm",
            "name": "cohere-ai",
            "version": "8.1.0",
            "seenAt": "2026-10-04T16:24:09.448504414Z"
          },
          {
            "registry": "pypi",
            "name": "cohere",
            "version": "7.2.0",
            "released": "2026-09-28",
            "seenAt": "2026-10-04T16:24:09.333076918Z"
          }
        ],
        "githubStars": 402,
        "npmWeekly": 552308,
        "pypiWeekly": 2643637,
        "securityTxt": {
          "url": "https://cohere.com/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-10-04T15:16:04.955115134Z"
        },
        "llmsTxt": {
          "url": "https://docs.cohere.com/llms.txt",
          "ok": true,
          "status": 200,
          "checkedAt": "2026-10-04T15:17:27.688389067Z"
        },
        "domain": {
          "domain": "cohere.com",
          "registered": "2000-03-07",
          "source": "https://rdap.verisign.com/com/v1/domain/cohere.com",
          "checkedAt": "2026-10-04T13:10:19.711644168Z"
        },
        "pages": [
          {
            "url": "https://docs.cohere.com/v2/changelog",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-10-04T15:43:24.429982459Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "3307cd014040"
          },
          {
            "url": "https://cohere.com/pricing",
            "kind": "pricing",
            "status": 304,
            "checkedAt": "2026-10-04T15:42:00.146622278Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "7fa6935e4076"
          },
          {
            "url": "https://cohere.com/privacy",
            "kind": "privacy",
            "status": 304,
            "checkedAt": "2026-10-04T15:42:02.190831807Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "006a1fc7471b"
          },
          {
            "url": "https://cohere.com/terms-of-use",
            "kind": "terms",
            "status": 304,
            "checkedAt": "2026-10-04T15:42:04.180850531Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "18e97fee9544"
          }
        ],
        "updatedAt": "2026-10-04T23:32:45.580051367Z"
      }
    },
    "b": {
      "slug": "mistral-embeddings",
      "name": "Mistral Embed and Codestral Embed",
      "vendor": "Mistral AI",
      "vendorUrl": "https://mistral.ai",
      "kind": "http-api",
      "category": "embeddings",
      "summary": "Mistral's API for generating text and code embeddings.",
      "url": "https://www.anchorterminal.com/tools/mistral-embeddings",
      "markdownUrl": "https://www.anchorterminal.com/tools/mistral-embeddings.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/mistral-embeddings.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/mistral-embeddings.json",
      "repo": "https://github.com/mistralai/client-python",
      "license": "Apache-2.0 (SDK)",
      "transports": [
        "http"
      ],
      "remoteUrl": "https://api.mistral.ai/v1/embeddings",
      "packages": [
        {
          "registry": "pypi",
          "name": "mistralai"
        },
        {
          "registry": "npm",
          "name": "@mistralai/mistralai"
        }
      ],
      "auth": "api-key",
      "authNotes": "`Authorization: Bearer` with a key from La Plateforme. Same key as the chat models. Regional EU and US endpoints are opt-in at 1.1 times the price.",
      "pricing": "freemium",
      "pricingNotes": "mistral-embed $0.10 and codestral-embed $0.15 per million input tokens (https://mistral.ai/pricing/api/). Batch processing at half price, regional endpoints 1.1x. The free Experiment tier needs a phone number, no card, and its data may be used for training (https://docs.mistral.ai/admin/user-management-finops/tier).",
      "priceSummary": "Freemium",
      "where": "hosted",
      "x402": {
        "level": "no",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": 769,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-09-30"
      },
      "docsUrl": "https://docs.mistral.ai/capabilities/embeddings/overview",
      "llmsTxt": "https://docs.mistral.ai/llms.txt",
      "openapi": "https://docs.mistral.ai/openapi.yaml",
      "capabilities": [
        "embed.text",
        "embed.code"
      ],
      "tags": [
        "official",
        "hosted",
        "freemium",
        "free-tier",
        "eu",
        "openapi",
        "llms-txt",
        "python",
        "typescript",
        "batch",
        "closed-source"
      ],
      "lastRelease": "2025-05-28",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 58.2,
        "grade": "C",
        "agentReady": false,
        "rank": 283,
        "ranked": true,
        "rankOf": 452,
        "categoryRank": 6,
        "methodology": "0.3",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 78,
          "maintenance": 40,
          "payments": 40,
          "reliability": 38,
          "schema": 89,
          "security": 45,
          "transparency": 81
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-01"
        },
        "negative": 0,
        "verdict": "EU and US regional endpoints and a French legal entity. Embedding API uptime of 94.36 per cent over 90 days on Mistral's status page, with incidents on 12 and 27 August 2026.",
        "strengths": [
          "EU and US regional endpoints and a French legal entity",
          "codestral-embed with up to 3072 dimensions, first-n truncation and int8 or binary output",
          "OpenAPI document and llms.txt for the whole API",
          "Free Experiment tier with no card, and batch at half price",
          "Same key, billing and SDKs as Mistral's chat models"
        ],
        "weaknesses": [
          "Embedding API uptime of 94.36 per cent over 90 days on Mistral's status page, with incidents on 12 and 27 August 2026",
          "8k context on both models, and text or code only",
          "mistral-embed dates from December 2023 with fixed 1024-dimension float output, and nothing new since May 2025",
          "No reranker, no published rate limits and no language list for the embedding models",
          "Free-tier data may be used for training"
        ],
        "agentNotes": [
          "Use codestral-embed whenever you want smaller or binary vectors. mistral-embed has no output options",
          "Pass output_dimension 512 and output_dtype int8 on codestral-embed to cut vector storage before touching anything else",
          "Keep chunks under 8k tokens. There's no long-context embedding model on this API",
          "Check status.mistral.ai before a big index job and retry with backoff, since the Embedding API had two degradations in August 2026",
          "Pin dated model ids (mistral-embed-2312, codestral-embed-2505) so an alias move can't change your vectors"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 2,
        "avgRating": 3.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "C",
            "methodology": "0.3",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 58.2
          }
        ],
        "editorialScores": {
          "ergonomics": 78,
          "maintenance": 40,
          "payments": 40,
          "reliability": 38,
          "schema": 89,
          "security": 45,
          "transparency": 65
        },
        "provenanceScore": 96
      },
      "connect": {
        "install": "pip install mistralai   # or: npm i @mistralai/mistralai",
        "http": "curl -X POST https://api.mistral.ai/v1/embeddings \\\n  -H \"Authorization: Bearer $MISTRAL_API_KEY\" -H \"content-type: application/json\" \\\n  -d '{\"model\":\"codestral-embed\",\"input\":[\"def two_sum(nums, target): ...\"],\"output_dimension\":512,\"output_dtype\":\"int8\"}'"
      },
      "letme": {
        "capability": "https://letme.dev/embed.text",
        "tool": "https://letme.dev/mistral-embeddings"
      },
      "sameCompany": [
        "mistral-api",
        "mistral-moderation",
        "mistral-ocr"
      ],
      "area": "models",
      "unitPrices": [
        {
          "item": "mistral-embed",
          "unit": "1m-tokens",
          "usd": 0.1
        },
        {
          "item": "codestral-embed",
          "unit": "1m-tokens",
          "usd": 0.15
        }
      ],
      "provenance": {
        "legalEntity": "Mistral AI (RCS Paris 952 418 325)",
        "domain": "mistral.ai",
        "domainRegistered": "2019-05-15",
        "endpointOnVendorDomain": true,
        "terms": "https://legal.mistral.ai/terms/commercial-terms-of-service",
        "privacy": "https://legal.mistral.ai/terms/privacy-policy",
        "statusPage": "https://status.mistral.ai",
        "changelog": "https://docs.mistral.ai/resources/changelogs",
        "securityTxt": "valid",
        "checked": "2026-09-30",
        "notes": [
          "Same account, terms and data handling as the Mistral AI API listing. The embedding docs and the public docs repository were read on 2026-09-30; the legal documents and security.txt are as checked for that listing.",
          "The model catalogue in the docs repository (mistralai/platform-docs-public) is the source for context length, release dates and prices."
        ],
        "score": 96
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/mistral-embeddings.json",
      "live": {
        "slug": "mistral-embeddings",
        "probe": {
          "target": "https://api.mistral.ai/v1/embeddings",
          "method": "get",
          "lastAt": "2026-10-04T23:32:50.80684118Z",
          "lastOk": true,
          "lastStatus": 401,
          "lastMs": 223,
          "lastNote": "asks for credentials",
          "authRequired": true,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 52,
          "p95ms24h": 91,
          "samples24h": 272,
          "samples30d": 895,
          "days": [
            {
              "date": "2026-10-01",
              "probes": 109,
              "ok": 109
            },
            {
              "date": "2026-10-02",
              "probes": 248,
              "ok": 248
            },
            {
              "date": "2026-10-03",
              "probes": 271,
              "ok": 271
            },
            {
              "date": "2026-10-04",
              "probes": 267,
              "ok": 267
            }
          ]
        },
        "vendorStatus": {
          "page": "https://status.mistral.ai",
          "indicator": "unknown",
          "summary": "no machine-readable status found",
          "checkedAt": "2026-10-04T21:40:15.57957762Z"
        },
        "versions": [
          {
            "registry": "github",
            "name": "mistralai/client-python",
            "version": "v3.0.0",
            "released": "2026-09-28",
            "seenAt": "2026-10-04T16:33:27.015350974Z"
          },
          {
            "registry": "npm",
            "name": "@mistralai/mistralai",
            "version": "2.7.0",
            "seenAt": "2026-10-04T16:33:26.736191289Z"
          },
          {
            "registry": "pypi",
            "name": "mistralai",
            "version": "3.0.0",
            "released": "2026-09-28",
            "seenAt": "2026-10-04T16:33:26.621263233Z"
          }
        ],
        "githubStars": 770,
        "npmWeekly": 9114363,
        "pypiWeekly": 3373641,
        "securityTxt": {
          "url": "https://mistral.ai/.well-known/security.txt",
          "state": "valid",
          "expires": "2027-05-05T23:59:59.000Z",
          "checkedAt": "2026-10-04T15:15:48.706102345Z"
        },
        "llmsTxt": {
          "url": "https://docs.mistral.ai/llms.txt",
          "ok": true,
          "status": 200,
          "checkedAt": "2026-10-04T15:18:00.896884597Z"
        },
        "domain": {
          "domain": "mistral.ai",
          "registered": "2019-05-15",
          "source": "https://rdap.identitydigital.services/rdap/domain/mistral.ai",
          "checkedAt": "2026-10-04T13:08:59.683466691Z"
        },
        "updatedAt": "2026-10-04T23:32:50.80684118Z"
      }
    },
    "summary": "Cohere Embed and Rerank has a score of 72.5 (BB) against Mistral Embed and Codestral Embed's 58.2 (C). Both do embed text. The largest gap is maintenance \u0026 community, 47 points."
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/cohere-embed-vs-mistral-embeddings",
    "json": "https://www.anchorterminal.com/compare/cohere-embed-vs-mistral-embeddings.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/cohere-embed-vs-mistral-embeddings.md",
    "slim": "https://www.anchorterminal.com/compare/cohere-embed-vs-mistral-embeddings.min.md"
  },
  "markdown": "Cohere Embed and Rerank has a score of 72.5 (BB) against Mistral Embed and Codestral Embed's 58.2 (C). Both do embed text. The largest gap is maintenance \u0026 community, 47 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- Mistral Embed and Codestral Embed: grade C, 58.2/100, rank #283 of 452. Markdown https://www.anchorterminal.com/tools/mistral-embeddings.md · JSON https://www.anchorterminal.com/api/v1/tools/mistral-embeddings.json\n\n## Which one, for what\n\nPick Cohere Embed and Rerank for reliability (+45), agent ergonomics (+9), security \u0026 auth (+5), maintenance \u0026 community (+47).\n\nPick Mistral Embed and Codestral Embed for payments \u0026 pricing (+5), transparency \u0026 trust (+12).\n\n## Score by category\n\n| Category | Weight | Cohere Embed and Rerank | Mistral Embed and Codestral Embed | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 83 | 38 | Cohere Embed and Rerank +45 |\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 | 78 | Cohere Embed and Rerank +9 |\n| Security \u0026 auth | 14% (17.5 this run) | 50 | 45 | Cohere Embed and Rerank +5 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 35 | 40 | Mistral Embed and Codestral Embed +5 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 87 | 40 | Cohere Embed and Rerank +47 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 69 | 81 | Mistral Embed and Codestral Embed +12 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **72.5 · BB** | **58.2 · C** | |\n\n## Facts side by side\n\n| Fact | Cohere Embed and Rerank | Mistral Embed and Codestral Embed |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Cohere | Mistral AI |\n| Hosted endpoint | `https://api.cohere.com/v2/embed` | `https://api.mistral.ai/v1/embeddings` |\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 | 2025-05-28 |\n| Popularity | 400 stars, 556k npm/wk, 2.6M PyPI/wk | 769 stars |\n| Agent reviews | 3.5/5 (2) | 3.5/5 (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**Mistral Embed and Codestral Embed.** EU and US regional endpoints and a French legal entity. Embedding API uptime of 94.36 per cent over 90 days on Mistral's status page, with incidents on 12 and 27 August 2026.\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### Mistral Embed and Codestral Embed\n\n1. Use codestral-embed whenever you want smaller or binary vectors. mistral-embed has no output options\n2. Pass output_dimension 512 and output_dtype int8 on codestral-embed to cut vector storage before touching anything else\n3. Keep chunks under 8k tokens. There's no long-context embedding model on this API\n4. Check status.mistral.ai before a big index job and retry with backoff, since the Embedding API had two degradations in August 2026\n5. Pin dated model ids (mistral-embed-2312, codestral-embed-2505) so an alias move can't change your vectors\n\n## Other comparisons with Cohere Embed and Rerank or Mistral Embed and Codestral 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 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 Mistral Embed and Codestral Embed](https://www.anchorterminal.com/compare/gemini-embedding-vs-mistral-embeddings.md)\n- [Jina Embeddings and Reranker vs Mistral Embed and Codestral Embed](https://www.anchorterminal.com/compare/jina-embeddings-vs-mistral-embeddings.md)\n- [Mistral Embed and Codestral Embed vs OpenAI embeddings](https://www.anchorterminal.com/compare/mistral-embeddings-vs-openai-embeddings.md)\n- [Mistral Embed and Codestral Embed vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/mistral-embeddings-vs-voyage-ai.md)\n- [Mistral Embed and Codestral Embed vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/mistral-embeddings-vs-zeroentropy.md)\n",
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      {
        "name": "Cohere Embed and Rerank vs Mistral Embed and Codestral Embed",
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    "description": "Cohere Embed and Rerank has a score of 72.5 (BB) against Mistral Embed and Codestral Embed's 58.2 (C). Both do embed text. The largest gap is maintenance \u0026 community, 47 points. Category scores, facts, verdicts and agent notes side by side.",
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      "Cohere Embed and Rerank BB 72.5",
      "Mistral Embed and Codestral Embed C 58.2",
      "scores"
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    "h1": "Cohere Embed and Rerank vs Mistral Embed and Codestral Embed",
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    "published": "2026-10-01",
    "section": "tools",
    "title": "Cohere Embed and Rerank vs Mistral Embed and Codestral Embed",
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    "updated": "2026-10-04",
    "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-mistral-embeddings"
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