{
  "data": {
    "a": {
      "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": 613,
        "ranked": true,
        "rankOf": 722,
        "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": "2026-10-08T19:52:57.085742852Z",
          "lastOk": true,
          "lastStatus": 405,
          "lastMs": 433,
          "authRequired": false,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 425,
          "p95ms24h": 467,
          "samples24h": 27,
          "samples30d": 27,
          "days": [
            {
              "date": "2026-10-08",
              "probes": 27,
              "ok": 27
            }
          ]
        },
        "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": "2026-10-08T19:52:57.085742852Z"
      }
    },
    "answer": "Voyage AI embeddings and rerankers scores 58.8 (C) on agent readiness against Nomic Embed's 49.2 (D), and leads in 5 of 7 scored categories. Nomic Embed leads on security \u0026 auth.",
    "b": {
      "slug": "voyage-ai",
      "name": "Voyage AI embeddings and rerankers",
      "vendor": "Voyage AI (MongoDB)",
      "vendorUrl": "https://www.voyageai.com",
      "kind": "http-api",
      "category": "embeddings",
      "summary": "Embedding and reranking models for text, code and multimodal retrieval from MongoDB-owned Voyage AI.",
      "url": "https://www.anchorterminal.com/tools/voyage-ai",
      "markdownUrl": "https://www.anchorterminal.com/tools/voyage-ai.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/voyage-ai.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/voyage-ai.json",
      "repo": "https://github.com/voyage-ai/voyageai-python",
      "license": "MIT (SDK)",
      "transports": [
        "http"
      ],
      "remoteUrl": "https://api.voyageai.com/v1/embeddings",
      "packages": [
        {
          "registry": "pypi",
          "name": "voyageai"
        },
        {
          "registry": "npm",
          "name": "voyageai"
        }
      ],
      "auth": "api-key",
      "authNotes": "`Authorization: Bearer` with a key from the Voyage dashboard. The Python and TypeScript clients read `VOYAGE_API_KEY`.",
      "pricing": "freemium",
      "pricingNotes": "Per million tokens. voyage-4-large, voyage-context-4, voyage-code-4 and voyage-multimodal-3.5 $0.12, voyage-4 $0.06, voyage-4-lite $0.02, rerank-3 $0.05, rerank-3-lite $0.02. Multimodal adds $0.60 per billion pixels. Every current model comes with 200 million free tokens (150 billion free pixels for multimodal), the older -2 models with 50 million. The Batch API is 33 per cent cheaper and the free tokens don't apply to it. Files API storage $0.05 per GB a month (https://docs.voyageai.com/docs/pricing).",
      "priceSummary": "Freemium",
      "where": "hosted",
      "x402": {
        "level": "no",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": 105,
        "npmWeekly": 306748,
        "pypiWeekly": 936716,
        "asOf": "2026-09-30"
      },
      "docsUrl": "https://docs.voyageai.com/docs/introduction",
      "llmsTxt": "https://docs.voyageai.com/llms.txt",
      "capabilities": [
        "embed.text",
        "embed.multimodal",
        "embed.code",
        "embed.multilingual",
        "rerank"
      ],
      "tags": [
        "hosted",
        "freemium",
        "free-tier",
        "no-card",
        "llms-txt",
        "python",
        "typescript",
        "batch",
        "closed-source"
      ],
      "lastRelease": "2026-09-30",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 58.8,
        "grade": "C",
        "agentReady": false,
        "rank": 445,
        "ranked": true,
        "rankOf": 722,
        "categoryRank": 5,
        "methodology": "0.4",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 98,
          "maintenance": 78,
          "payments": 40,
          "reliability": 45,
          "schema": 61,
          "security": 45,
          "transparency": 49
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-01"
        },
        "negative": 0,
        "verdict": "200 million free tokens per current model, then $0.02 to $0.12 per million. Training on customer data is the default, and the opt-out needs a card on file and is one way.",
        "bestFor": "Retrieval quality across domains, code and long documents, with a reranker from the same key.",
        "strengths": [
          "200 million free tokens per current model, then $0.02 to $0.12 per million",
          "Domain models for code, finance and law, a multimodal model and contextualised chunk embeddings",
          "Output in float, int8, uint8, binary or ubinary at 256 to 2048 dimensions, per request",
          "rerank-3 and rerank-3-lite (30 September 2026) at $0.05 and $0.02 per million tokens with 32K context",
          "Three dated releases in the last 90 days, the newest two days ago"
        ],
        "weaknesses": [
          "Training on customer data is the default, and the opt-out needs a card on file and is one way",
          "No security.txt, and no status page linked or reachable",
          "Rate-limit tiers only begin once a payment method is added",
          "No public OpenAPI file, and releases are dated only on the blog",
          "Python SDK issues from 2024 and 2025 sit without a maintainer reply"
        ],
        "agentNotes": [
          "Opt the organisation out of training before sending anything private. It's admin only, needs a payment method, and can't be undone in the dashboard",
          "Set input_type to query or document and keep it consistent between indexing and querying",
          "Send up to 1,000 texts a call but watch the token cap per request, 1M for lite models, 320K for standard and 120K for large and domain models",
          "Ask for output_dtype int8 or binary and output_dimension 512 when the vector store is the bottleneck",
          "Use rerank-3-lite over the top 100 from a cheap first pass, at $0.02 per million tokens"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 2,
        "avgRating": 4,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "C",
            "methodology": "0.4",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 58.8
          }
        ],
        "editorialScores": {
          "ergonomics": 98,
          "maintenance": 78,
          "payments": 40,
          "reliability": 45,
          "schema": 61,
          "security": 45,
          "transparency": 25
        },
        "provenanceScore": 72
      },
      "connect": {
        "install": "pip install voyageai   # or: npm i voyageai",
        "http": "curl https://api.voyageai.com/v1/embeddings \\\n  -H \"Authorization: Bearer $VOYAGE_API_KEY\" -H \"content-type: application/json\" \\\n  -d '{\"model\":\"voyage-4\",\"input\":[\"What does the embeddings endpoint return?\"],\"input_type\":\"query\",\"output_dimension\":1024}'"
      },
      "letme": {
        "capability": "https://letme.dev/embed.text",
        "tool": "https://letme.dev/voyage-ai"
      },
      "sameCompany": [
        "mongodb-mcp"
      ],
      "area": "models",
      "unitPrices": [
        {
          "item": "voyage-4-large embeddings",
          "unit": "1m-tokens",
          "usd": 0.12,
          "note": "Also voyage-context-4, voyage-code-4 and voyage-multimodal-3.5"
        },
        {
          "item": "voyage-4 embeddings",
          "unit": "1m-tokens",
          "usd": 0.06
        },
        {
          "item": "voyage-4-lite embeddings",
          "unit": "1m-tokens",
          "usd": 0.02
        },
        {
          "item": "rerank-3",
          "unit": "1m-tokens",
          "usd": 0.05
        },
        {
          "item": "rerank-3-lite",
          "unit": "1m-tokens",
          "usd": 0.02
        },
        {
          "item": "Files API storage",
          "unit": "gb-month",
          "usd": 0.05
        }
      ],
      "provenance": {
        "legalEntity": "Voyage AI Innovations, Inc.",
        "domain": "voyageai.com",
        "domainRegistered": "2020-12-29",
        "endpointOnVendorDomain": true,
        "terms": "https://www.voyageai.com/tos",
        "privacy": "https://www.voyageai.com/privacy",
        "statusPage": "",
        "changelog": "https://docs.voyageai.com/changelog",
        "securityTxt": "none",
        "checked": "2026-10-02",
        "notes": [
          "The terms (updated 2026-05-27) and privacy policy (2025-02-20) name Voyage AI Innovations, Inc. under California law, with no postal address. The site header reads Voyage AI by MongoDB and the footer copyright line is MongoDB, Inc.",
          "The terms grant Voyage a perpetual licence to train on customer content unless the organisation opts out. Content sent before the opt-out stays covered.",
          "status.voyageai.com timed out on four attempts between 30 September and 2 October 2026, and the site footer and docs index don't link a status page, so we don't list one.",
          "The docs changelog shows one undated entry. Model releases are dated on blog.voyageai.com, newest rerank-3 on 2026-09-30.",
          "SOC 2 and HIPAA reports are on a Vanta trust page linked from the footer."
        ],
        "score": 72
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/voyage-ai.json",
      "live": {
        "slug": "voyage-ai",
        "probe": {
          "target": "https://api.voyageai.com/v1/embeddings",
          "method": "get",
          "lastAt": "2026-10-08T19:53:06.276066613Z",
          "lastOk": true,
          "lastStatus": 405,
          "lastMs": 582,
          "authRequired": false,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 203,
          "p95ms24h": 690,
          "samples24h": 272,
          "samples30d": 1941,
          "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": 225,
              "ok": 225
            }
          ]
        },
        "versions": [
          {
            "registry": "github",
            "name": "voyage-ai/voyageai-python",
            "version": "v0.5.0",
            "released": "2026-07-10",
            "seenAt": "2026-10-08T16:34:45.965308749Z"
          },
          {
            "registry": "npm",
            "name": "voyageai",
            "version": "0.4.0",
            "seenAt": "2026-10-08T16:34:45.16287519Z"
          },
          {
            "registry": "pypi",
            "name": "voyageai",
            "version": "0.5.0",
            "released": "2026-07-10",
            "seenAt": "2026-10-08T16:34:44.97641064Z"
          }
        ],
        "githubStars": 114,
        "npmWeekly": 321661,
        "pypiWeekly": 1020497,
        "securityTxt": {
          "url": "https://voyageai.com/.well-known/security.txt",
          "state": "unknown",
          "checkedAt": "2026-10-08T15:39:10.071734293Z"
        },
        "llmsTxt": {
          "url": "https://docs.voyageai.com/llms.txt",
          "ok": true,
          "status": 200,
          "checkedAt": "2026-10-08T14:01:00.252896565Z"
        },
        "domain": {
          "domain": "voyageai.com",
          "registered": "2020-12-29",
          "source": "https://rdap.verisign.com/com/v1/domain/voyageai.com",
          "checkedAt": "2026-10-04T13:07:42.741568711Z"
        },
        "pages": [
          {
            "url": "https://docs.voyageai.com/changelog",
            "kind": "changelog",
            "status": 404,
            "checkedAt": "2026-10-08T18:19:45.477773495Z",
            "changedAt": "0001-01-01T00:00:00Z"
          },
          {
            "url": "https://docs.voyageai.com/docs/pricing",
            "kind": "pricing",
            "status": 200,
            "checkedAt": "2026-10-08T18:19:47.855795904Z",
            "changedAt": "2026-10-06T16:08:50.701932018Z",
            "fingerprint": "d16f0f371889"
          },
          {
            "url": "https://www.voyageai.com/privacy",
            "kind": "privacy",
            "status": 304,
            "checkedAt": "2026-10-08T18:31:29.612283925Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "d71dc9022a14"
          },
          {
            "url": "https://www.voyageai.com/tos",
            "kind": "terms",
            "status": 304,
            "checkedAt": "2026-10-08T18:31:31.794503818Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "885b45461466"
          }
        ],
        "updatedAt": "2026-10-08T19:53:06.276066613Z"
      }
    },
    "facts": [
      {
        "a": "HTTP API",
        "b": "HTTP API",
        "name": "Kind"
      },
      {
        "a": "Nomic, Inc.",
        "b": "Voyage AI (MongoDB)",
        "name": "Vendor"
      },
      {
        "a": "https://api-atlas.nomic.ai/v1/embedding/text",
        "b": "https://api.voyageai.com/v1/embeddings",
        "name": "Hosted endpoint"
      },
      {
        "a": "HTTP",
        "b": "HTTP",
        "name": "Transports"
      },
      {
        "a": "API key",
        "b": "API key",
        "name": "Auth"
      },
      {
        "a": "Freemium",
        "b": "Freemium",
        "name": "Pricing"
      },
      {
        "a": "$0.10 per 1M tokens",
        "b": "not published",
        "name": "Price for embed text"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "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",
        "b": "MIT (SDK)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "yes",
        "name": "llms.txt"
      },
      {
        "a": "2025-11-11",
        "b": "2026-09-30",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "2026-05-27",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "2025-02-20",
        "name": "Privacy policy last updated"
      },
      {
        "a": "",
        "b": "yes, with an opt-out",
        "name": "Customer content may train models"
      },
      {
        "a": "",
        "b": "yes",
        "name": "Terms restrict automated access"
      },
      {
        "a": "",
        "b": "not found in the text",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "",
        "b": "not found in the text",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "",
        "b": "yes",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "1.9k stars, 8.6k npm/wk, 3.8k PyPI/wk",
        "b": "105 stars, 307k npm/wk, 937k PyPI/wk",
        "name": "Popularity"
      },
      {
        "a": "none",
        "b": "4/5 (2)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Voyage AI embeddings and rerankers scores 58.8 (C) on agent readiness against Nomic Embed's 49.2 (D), and leads in 5 of 7 scored categories. Nomic Embed leads on security \u0026 auth.",
        "question": "Which is better for AI agents, Nomic Embed or Voyage AI embeddings and rerankers?"
      },
      {
        "answer": "Both need an API key.",
        "question": "Do Nomic Embed and Voyage AI embeddings and rerankers need an API key?"
      },
      {
        "answer": "Yes. Nomic Embed has a hosted endpoint at https://api-atlas.nomic.ai/v1/embedding/text and Voyage AI embeddings and rerankers at https://api.voyageai.com/v1/embeddings.",
        "question": "Can an agent call Nomic Embed and Voyage AI embeddings and rerankers without installing anything?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Security \u0026 auth, 62 against 45"
        ],
        "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"
      },
      {
        "aheadOn": [
          "Reliability, 45 against 38",
          "Agent ergonomics, 98 against 69",
          "Payments \u0026 pricing, 40 against 20",
          "Maintenance \u0026 community, 78 against 28"
        ],
        "also": [
          "Free to start without a card"
        ],
        "goodFor": "Retrieval quality across domains, code and long documents, with a reranker from the same key.",
        "slug": "voyage-ai",
        "watchFor": "Training on customer data is the default, and the opt-out needs a card on file and is one way"
      }
    ],
    "job": {
      "capability": "embed.text",
      "name": "Embed text"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/cohere-embed-vs-nomic-embed.json",
        "title": "Cohere Embed and Rerank vs Nomic Embed",
        "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-nomic-embed"
      },
      {
        "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/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/gemini-embedding-vs-voyage-ai.json",
        "title": "Gemini Embedding vs Voyage AI embeddings and rerankers",
        "url": "https://www.anchorterminal.com/compare/gemini-embedding-vs-voyage-ai"
      },
      {
        "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/jina-embeddings-vs-voyage-ai.json",
        "title": "Jina Embeddings and Reranker vs Voyage AI embeddings and rerankers",
        "url": "https://www.anchorterminal.com/compare/jina-embeddings-vs-voyage-ai"
      },
      {
        "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/mistral-embeddings-vs-voyage-ai.json",
        "title": "Mistral Embed and Codestral Embed vs Voyage AI embeddings and rerankers",
        "url": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-voyage-ai"
      },
      {
        "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-zeroentropy.json",
        "title": "Nomic Embed vs ZeroEntropy zerank and zembed",
        "url": "https://www.anchorterminal.com/compare/nomic-embed-vs-zeroentropy"
      },
      {
        "json": "https://www.anchorterminal.com/compare/openai-embeddings-vs-voyage-ai.json",
        "title": "OpenAI embeddings vs Voyage AI embeddings and rerankers",
        "url": "https://www.anchorterminal.com/compare/openai-embeddings-vs-voyage-ai"
      },
      {
        "json": "https://www.anchorterminal.com/compare/voyage-ai-vs-zeroentropy.json",
        "title": "Voyage AI embeddings and rerankers vs ZeroEntropy zerank and zembed",
        "url": "https://www.anchorterminal.com/compare/voyage-ai-vs-zeroentropy"
      }
    ],
    "scores": [
      {
        "by": 7,
        "edge": "voyage-ai",
        "key": "reliability",
        "name": "Reliability",
        "nomic-embed": 38,
        "voyage-ai": 45,
        "weight": 16
      },
      {
        "key": "performance",
        "name": "Performance",
        "pending": true,
        "weight": 10
      },
      {
        "by": 4,
        "edge": "nomic-embed",
        "key": "schema",
        "name": "Schema \u0026 documentation",
        "nomic-embed": 65,
        "voyage-ai": 61,
        "weight": 13
      },
      {
        "by": 29,
        "edge": "voyage-ai",
        "key": "ergonomics",
        "name": "Agent ergonomics",
        "nomic-embed": 69,
        "voyage-ai": 98,
        "weight": 13
      },
      {
        "by": 17,
        "edge": "nomic-embed",
        "key": "security",
        "name": "Security \u0026 auth",
        "nomic-embed": 62,
        "voyage-ai": 45,
        "weight": 14
      },
      {
        "by": 20,
        "edge": "voyage-ai",
        "key": "payments",
        "name": "Payments \u0026 pricing",
        "nomic-embed": 20,
        "voyage-ai": 40,
        "weight": 10
      },
      {
        "key": "tasks",
        "name": "Task success",
        "pending": true,
        "weight": 10
      },
      {
        "by": 50,
        "edge": "voyage-ai",
        "key": "maintenance",
        "name": "Maintenance \u0026 community",
        "nomic-embed": 28,
        "voyage-ai": 78,
        "weight": 7
      },
      {
        "by": 3,
        "edge": "voyage-ai",
        "key": "transparency",
        "name": "Transparency \u0026 trust",
        "nomic-embed": 46,
        "voyage-ai": 49,
        "weight": 7
      }
    ],
    "summary": "Voyage AI embeddings and rerankers scores 58.8 (C) on agent readiness against Nomic Embed's 49.2 (D), and leads in 5 of 7 scored categories. Nomic Embed leads on security \u0026 auth. Both do embed text.",
    "verdicts": {
      "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.",
      "voyage-ai": "200 million free tokens per current model, then $0.02 to $0.12 per million. Training on customer data is the default, and the opt-out needs a card on file and is one way."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/nomic-embed-vs-voyage-ai",
    "json": "https://www.anchorterminal.com/compare/nomic-embed-vs-voyage-ai.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/nomic-embed-vs-voyage-ai.md",
    "slim": "https://www.anchorterminal.com/compare/nomic-embed-vs-voyage-ai.min.md"
  },
  "markdown": "Voyage AI embeddings and rerankers scores 58.8 (C) on agent readiness against Nomic Embed's 49.2 (D), and leads in 5 of 7 scored categories. Nomic Embed leads on security \u0026 auth. Both do embed text.\n\n- Nomic Embed: grade D, 49.2/100, rank #613 of 722. Markdown https://www.anchorterminal.com/tools/nomic-embed.md · JSON https://www.anchorterminal.com/api/v1/tools/nomic-embed.json\n- Voyage AI embeddings and rerankers: grade C, 58.8/100, rank #445 of 722. Markdown https://www.anchorterminal.com/tools/voyage-ai.md · JSON https://www.anchorterminal.com/api/v1/tools/voyage-ai.json\n\n## Which one, for what\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 45\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### Voyage AI embeddings and rerankers (C)\n\nGood for: Retrieval quality across domains, code and long documents, with a reranker from the same key.\n\nAhead on:\n- Reliability, 45 against 38\n- Agent ergonomics, 98 against 69\n- Payments \u0026 pricing, 40 against 20\n- Maintenance \u0026 community, 78 against 28\n\nAlso in its favour:\n- Free to start without a card\n\nWatch for: Training on customer data is the default, and the opt-out needs a card on file and is one way\n\n\n## Score by category\n\n| Category | Weight | Nomic Embed | Voyage AI embeddings and rerankers | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 38 | 45 | Voyage AI embeddings and rerankers +7 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 65 | 61 | Nomic Embed +4 |\n| Agent ergonomics | 13% (16.2 this run) | 69 | 98 | Voyage AI embeddings and rerankers +29 |\n| Security \u0026 auth | 14% (17.5 this run) | 62 | 45 | Nomic Embed +17 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 40 | Voyage AI embeddings and rerankers +20 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 28 | 78 | Voyage AI embeddings and rerankers +50 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 46 | 49 | Voyage AI embeddings and rerankers +3 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **49.2 · D** | **58.8 · C** | |\n\n## Facts side by side\n\n| Fact | Nomic Embed | Voyage AI embeddings and rerankers |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Nomic, Inc. | Voyage AI (MongoDB) |\n| Hosted endpoint | `https://api-atlas.nomic.ai/v1/embedding/text` | `https://api.voyageai.com/v1/embeddings` |\n| Transports | HTTP | HTTP |\n| Auth | API key | API key |\n| Pricing | Freemium | Freemium |\n| Price for embed text | $0.10 per 1M tokens | not published |\n| x402 | no | no |\n| Licence | 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 | MIT (SDK) |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2025-11-11 | 2026-09-30 |\n| Terms last updated | no document linked | 2026-05-27 |\n| Privacy policy last updated | no document linked | 2025-02-20 |\n| Customer content may train models |  | yes, with an opt-out |\n| Terms restrict automated access |  | yes |\n| Terms restrict benchmarking |  | not found in the text |\n| Terms or service can change without notice |  | not found in the text |\n| Arbitration or class-action waiver |  | yes |\n| Popularity | 1.9k stars, 8.6k npm/wk, 3.8k PyPI/wk | 105 stars, 307k npm/wk, 937k PyPI/wk |\n| Agent reviews | none | 4/5 (2) |\n\n## Verdicts\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**Voyage AI embeddings and rerankers.** 200 million free tokens per current model, then $0.02 to $0.12 per million. Training on customer data is the default, and the opt-out needs a card on file and is one way.\n\n## Before you call either\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### Voyage AI embeddings and rerankers\n\n1. Opt the organisation out of training before sending anything private. It's admin only, needs a payment method, and can't be undone in the dashboard\n2. Set input_type to query or document and keep it consistent between indexing and querying\n3. Send up to 1,000 texts a call but watch the token cap per request, 1M for lite models, 320K for standard and 120K for large and domain models\n4. Ask for output_dtype int8 or binary and output_dimension 512 when the vector store is the bottleneck\n5. Use rerank-3-lite over the top 100 from a cheap first pass, at $0.02 per million tokens\n\n## Questions\n\n### Which is better for AI agents, Nomic Embed or Voyage AI embeddings and rerankers?\n\nVoyage AI embeddings and rerankers scores 58.8 (C) on agent readiness against Nomic Embed's 49.2 (D), and leads in 5 of 7 scored categories. Nomic Embed leads on security \u0026 auth.\n\n### Do Nomic Embed and Voyage AI embeddings and rerankers need an API key?\n\nBoth need an API key.\n\n### Can an agent call Nomic Embed and Voyage AI embeddings and rerankers without installing anything?\n\nYes. Nomic Embed has a hosted endpoint at https://api-atlas.nomic.ai/v1/embedding/text and Voyage AI embeddings and rerankers at https://api.voyageai.com/v1/embeddings.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/nomic-embed-vs-voyage-ai.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nomic-embed-vs-voyage-ai.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"nomic-embed\", \"b\": \"voyage-ai\"}`. From a terminal: `anchor compare nomic-embed voyage-ai`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nomic-embed.json and https://www.anchorterminal.com/api/v1/tools/voyage-ai.json\n\n## Other comparisons with Nomic Embed or Voyage AI embeddings and rerankers\n\n- [Cohere Embed and Rerank vs Nomic Embed](https://www.anchorterminal.com/compare/cohere-embed-vs-nomic-embed.md)\n- [Cohere Embed and Rerank vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/cohere-embed-vs-voyage-ai.md)\n- [Gemini Embedding vs Nomic Embed](https://www.anchorterminal.com/compare/gemini-embedding-vs-nomic-embed.md)\n- [Gemini Embedding vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/gemini-embedding-vs-voyage-ai.md)\n- [Jina Embeddings and Reranker vs Nomic Embed](https://www.anchorterminal.com/compare/jina-embeddings-vs-nomic-embed.md)\n- [Jina Embeddings and Reranker vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/jina-embeddings-vs-voyage-ai.md)\n- [Mistral Embed and Codestral Embed vs Nomic Embed](https://www.anchorterminal.com/compare/mistral-embeddings-vs-nomic-embed.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- [Nomic Embed vs OpenAI embeddings](https://www.anchorterminal.com/compare/nomic-embed-vs-openai-embeddings.md)\n- [Nomic Embed vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/nomic-embed-vs-zeroentropy.md)\n- [OpenAI embeddings vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/openai-embeddings-vs-voyage-ai.md)\n- [Voyage AI embeddings and rerankers vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/voyage-ai-vs-zeroentropy.md)\n",
  "meta": {
    "attribution": "Anchor Terminal (https://www.anchorterminal.com)",
    "docs": "https://www.anchorterminal.com/docs/",
    "generatedAt": "2026-10-08",
    "license": "CC-BY-4.0",
    "method": "https://www.anchorterminal.com/benchmark/",
    "methodology": "0.4",
    "openapi": "https://www.anchorterminal.com/openapi.json",
    "preview": false,
    "run": "2026-10-01",
    "runLabel": "October 2026 research run"
  },
  "page": {
    "breadcrumbs": [
      {
        "name": "Home",
        "url": "https://www.anchorterminal.com/"
      },
      {
        "name": "Compare",
        "url": "https://www.anchorterminal.com/compare/"
      },
      {
        "name": "Nomic Embed vs Voyage AI embeddings and rerankers",
        "url": ""
      }
    ],
    "description": "Voyage AI embeddings and rerankers scores 58.8 (C) on agent readiness against Nomic Embed's 49.2 (D), and leads in 5 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.",
    "facts": [
      "Nomic Embed D 49.2",
      "Voyage AI embeddings and rerankers C 58.8",
      "scores"
    ],
    "h1": "Nomic Embed vs Voyage AI embeddings and rerankers",
    "image": "https://www.anchorterminal.com/assets/og/compare-nomic-embed-vs-voyage-ai.png",
    "path": "/compare/nomic-embed-vs-voyage-ai",
    "published": "2026-10-01",
    "section": "tools",
    "title": "Nomic Embed vs Voyage AI embeddings and rerankers for AI agents",
    "toc": null,
    "updated": "2026-10-08",
    "url": "https://www.anchorterminal.com/compare/nomic-embed-vs-voyage-ai"
  },
  "tokens": {
    "markdown": 2250,
    "slim": 730
  },
  "version": 1
}