{
  "data": {
    "a": {
      "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
          }
        ],
        "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": "2026-10-04T23:32:47.749687618Z",
          "lastOk": true,
          "lastStatus": 404,
          "lastMs": 55,
          "authRequired": false,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 34,
          "p95ms24h": 69,
          "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
            }
          ]
        },
        "versions": [
          {
            "registry": "github",
            "name": "googleapis/python-genai",
            "version": "v2.28.0",
            "released": "2026-10-02",
            "seenAt": "2026-10-04T16:27:52.25299697Z"
          },
          {
            "registry": "npm",
            "name": "@google/genai",
            "version": "2.27.0",
            "seenAt": "2026-10-04T16:27:52.000233538Z"
          },
          {
            "registry": "pypi",
            "name": "google-genai",
            "version": "2.28.0",
            "released": "2026-10-02",
            "seenAt": "2026-10-04T16:27:51.891499993Z"
          }
        ],
        "githubStars": 4002,
        "npmWeekly": 29048793,
        "pypiWeekly": 34122162,
        "securityTxt": {
          "url": "https://google.com/.well-known/security.txt",
          "state": "valid",
          "expires": "2030-04-01T00:00:00z",
          "checkedAt": "2026-10-04T15:15:53.387118101Z"
        },
        "llmsTxt": {
          "url": "https://ai.google.dev/gemini-api/docs/llms.txt",
          "ok": true,
          "status": 200,
          "checkedAt": "2026-10-04T15:17:50.111667415Z"
        },
        "domain": {
          "domain": "google.com",
          "registered": "1997-09-15",
          "source": "https://rdap.verisign.com/com/v1/domain/google.com",
          "checkedAt": "2026-10-04T13:05:50.737985829Z"
        },
        "pages": [
          {
            "url": "https://cloud.google.com/vertex-ai/generative-ai/pricing",
            "kind": "pricing",
            "status": 200,
            "checkedAt": "2026-10-04T15:42:05.896276003Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "793e43bfda77"
          }
        ],
        "updatedAt": "2026-10-04T23:32:47.749687618Z"
      }
    },
    "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": "Gemini Embedding has a score of 71 (BB) against Mistral Embed and Codestral Embed's 58.2 (C). Both do embed text. The largest gap is maintenance \u0026 community, 35 points."
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/gemini-embedding-vs-mistral-embeddings",
    "json": "https://www.anchorterminal.com/compare/gemini-embedding-vs-mistral-embeddings.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/gemini-embedding-vs-mistral-embeddings.md",
    "slim": "https://www.anchorterminal.com/compare/gemini-embedding-vs-mistral-embeddings.min.md"
  },
  "markdown": "Gemini Embedding has a score of 71 (BB) against Mistral Embed and Codestral Embed's 58.2 (C). Both do embed text. The largest gap is maintenance \u0026 community, 35 points.\n\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- 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 Gemini Embedding for reliability (+27), agent ergonomics (+8), security \u0026 auth (+25), maintenance \u0026 community (+35).\n\nPick Mistral Embed and Codestral Embed for payments \u0026 pricing (+10).\n\n## Score by category\n\n| Category | Weight | Gemini Embedding | Mistral Embed and Codestral Embed | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 65 | 38 | Gemini Embedding +27 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 89 | 89 | even |\n| Agent ergonomics | 13% (16.2 this run) | 86 | 78 | Gemini Embedding +8 |\n| Security \u0026 auth | 14% (17.5 this run) | 70 | 45 | Gemini Embedding +25 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 30 | 40 | Mistral Embed and Codestral Embed +10 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 75 | 40 | Gemini Embedding +35 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 80 | 81 | Mistral Embed and Codestral Embed +1 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **71 · BB** | **58.2 · C** | |\n\n## Facts side by side\n\n| Fact | Gemini Embedding | Mistral Embed and Codestral Embed |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Google | Mistral AI |\n| Hosted endpoint | `https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-2:embedContent` | `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 | Apache-2.0 (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 | 2026-04-22 | 2025-05-28 |\n| Popularity | 4k stars | 769 stars |\n| Agent reviews | 3/5 (2) | 3.5/5 (2) |\n\n## Verdicts\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**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### 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### 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 Gemini Embedding 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 Mistral Embed and Codestral Embed](https://www.anchorterminal.com/compare/cohere-embed-vs-mistral-embeddings.md)\n- [Gemini Embedding vs Jina Embeddings and Reranker](https://www.anchorterminal.com/compare/gemini-embedding-vs-jina-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- [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": "Gemini Embedding vs Mistral Embed and Codestral Embed",
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    "description": "Gemini Embedding has a score of 71 (BB) against Mistral Embed and Codestral Embed's 58.2 (C). Both do embed text. The largest gap is maintenance \u0026 community, 35 points. Category scores, facts, verdicts and agent notes side by side.",
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      "Gemini Embedding BB 71",
      "Mistral Embed and Codestral Embed C 58.2",
      "scores"
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    "h1": "Gemini Embedding vs Mistral Embed and Codestral Embed",
    "image": "https://www.anchorterminal.com/assets/og/compare-gemini-embedding-vs-mistral-embeddings.png",
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    "published": "2026-10-01",
    "section": "tools",
    "title": "Gemini Embedding vs Mistral Embed and Codestral Embed for AI agents",
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    "updated": "2026-10-04",
    "url": "https://www.anchorterminal.com/compare/gemini-embedding-vs-mistral-embeddings"
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