{
  "data": {
    "a": {
      "slug": "nvidia-nemo-retriever",
      "name": "NVIDIA NeMo Retriever Embedding and Reranking NIMs",
      "vendor": "NVIDIA",
      "vendorUrl": "https://www.nvidia.com",
      "kind": "http-api",
      "category": "embeddings",
      "summary": "NVIDIA's NeMo Retriever Embedding and Reranking NIMs are GPU containers that run text and image embedding models and rerankers behind a local REST API, with `/v1/embeddings` in the OpenAI shape and `/v1/ranking`.",
      "url": "https://www.anchorterminal.com/tools/nvidia-nemo-retriever",
      "markdownUrl": "https://www.anchorterminal.com/tools/nvidia-nemo-retriever.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nvidia-nemo-retriever.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nvidia-nemo-retriever.json",
      "license": "Proprietary containers under the NVIDIA Software Licence Agreement and Product-Specific Terms for AI Products. Models carry their own licences, such as OpenMDW 1.1 for `nvidia/nemotron-3-embed-1b` and the NVIDIA Open Model Licence for the Llama Nemotron models",
      "transports": [
        "http"
      ],
      "packages": [
        {
          "registry": "pypi",
          "name": "langchain-nvidia-ai-endpoints"
        }
      ],
      "auth": "none",
      "authNotes": "The NIM's own API takes no credential. NVIDIA's security page says the deployer must secure the endpoints and suggests a proxy with HTTPS. Pulling images from `nvcr.io` needs a personal NGC API key, created by a person at org.ngc.nvidia.com and sent as the password for the user `$oauthtoken`. Model weights download from Hugging Face by default with `HF_TOKEN`, or from NGC with `NGC_API_KEY`. Some models need their licence terms accepted on the NGC catalogue page first. TLS is built in through `NIM_SERVER_TLS_CERT_PATH` and `NIM_SERVER_TLS_KEY_PATH`.",
      "pricing": "freemium",
      "pricingNotes": "Free for research, development and testing on up to 16 GPUs through the NVIDIA developer programme, with no card. Production use needs NVIDIA AI Enterprise, listed at $4,500 a GPU a year through partners or $1 a GPU-hour on AWS, Azure, Google Cloud and Oracle marketplaces, plus the instance. A 90-day AI Enterprise trial licence is available on request. The hosted trial endpoints on build.nvidia.com are free for prototyping (https://docs.nvidia.com/ai-enterprise/planning-resource/licensing-guide/latest/pricing.html, checked 2026-10-08).",
      "priceSummary": "Freemium",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "No x402, MPP or L402 in the NIM docs, the OpenAPI files or the AI Enterprise pricing page (checked 2026-10-08).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": 133630,
        "asOf": "2026-10-08"
      },
      "docsUrl": "https://docs.nvidia.com/nim/nemo-retriever/embedding/latest/overview.html",
      "openapi": "https://docs.nvidia.com/nim/nemo-retriever/embedding/latest/_downloads/9957cfb9472fcf23c93820c1922c4343/openai-api.openapi.yaml",
      "capabilities": [
        "embed.text",
        "embed.multimodal",
        "embed.multilingual",
        "rerank"
      ],
      "tags": [
        "self-hosted",
        "docker",
        "kubernetes",
        "gpu",
        "openai-compatible",
        "openapi",
        "proprietary",
        "enterprise",
        "free-for-development",
        "multimodal"
      ],
      "lastRelease": "2026-08-05",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 439,
        "ranked": true,
        "rankOf": 842,
        "categoryRank": 6,
        "methodology": "0.4",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 73,
          "maintenance": 57,
          "payments": 40,
          "reliability": 53,
          "schema": 78,
          "security": 55,
          "transparency": 71
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-08"
        },
        "negative": 0,
        "verdict": "Self-hosted containers with OpenAPI 3.1 files, typed request fields, five embedding output types and a dated end-of-life list. The API has no authentication or rate limiting of its own, production use needs an NVIDIA AI Enterprise licence at $4,500 a GPU a year, and the release notes carry no dates.",
        "bestFor": "Teams that already run NVIDIA GPUs and need embedding and reranking inside their own network, including page-image retrieval with the VL models.",
        "strengths": [
          "OpenAPI 3.1 files for both services, with enums for `input_type`, `modality`, `embedding_type` and `truncate` and no extra properties allowed",
          "`/v1/embeddings` follows the OpenAI shape, and a `-query` or `-passage` model suffix replaces `input_type` for OpenAI clients",
          "Output can be shrunk with `dimensions` from 128 to 2048 on three models, or with `int8`, `uint8`, `binary` and `ubinary` types",
          "NGC images are signed, built for amd64 and arm64, and were last scanned on 5 October 2026 per the NGC registry record",
          "The AI Enterprise lifecycle pages give support periods per branch and an end-of-life table with dates"
        ],
        "weaknesses": [
          "The API has no authentication and no rate limiting. The security page leaves both to a proxy the deployer runs",
          "Production use needs an NVIDIA AI Enterprise licence, $4,500 a GPU a year or $1 a GPU-hour on cloud marketplaces, plus the GPU",
          "Release notes carry no dates, and environment variable names changed between 2.0 and 2.3 without a note in the 2.2 or 2.3 notes we read",
          "The NVIDIA Software Licence Agreement forbids disclosing benchmark results without written permission, apart from a published exception",
          "Closed-source runtime with no public issue tracker or test suite, and no llms.txt entry or Markdown pages for these docs"
        ],
        "agentNotes": [
          "Send `input_type` as `query` or `passage` on every embedding call. Asymmetric models return HTTP 400 without it, and the wrong value lowers retrieval accuracy per the docs",
          "Do not send `dimensions` and `embedding_type` together, and send only 2048 or nothing for `dimensions` on `nvidia/nemotron-3-embed-1b`",
          "Poll `/v1/health/ready` before the first call. The Docker health check can report unhealthy while the NIM is ready, per the 2.3 known issues",
          "Check the image tag on NGC before pulling. The guide uses `nemotron-3-embed-1b:2.3`, and NGC's record for that image listed tags up to 2.2.2 on 8 October 2026",
          "Put a proxy with authentication and TLS in front of port 8000, and sort `/v1/ranking` results yourself as the request has no top-n field"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "C",
            "methodology": "0.4",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "ergonomics": 73,
          "maintenance": 57,
          "payments": 40,
          "reliability": 53,
          "schema": 78,
          "security": 55,
          "transparency": 59
        },
        "provenanceScore": 83
      },
      "connect": {
        "install": "echo \"$NGC_API_KEY\" | docker login nvcr.io --username '$oauthtoken' --password-stdin\ndocker run -it --rm --runtime=nvidia --gpus all --shm-size=16GB -e HF_TOKEN -v ~/.cache/nim/cache:/opt/cache -v ~/.cache/nim/weights:/model -u $(id -u) -p 8000:8000 nvcr.io/nim/nvidia/nemotron-3-embed-1b:2.3",
        "http": "curl -X POST http://localhost:8000/v1/embeddings \\\n  -H 'accept: application/json' -H 'Content-Type: application/json' \\\n  -d '{\"input\":[\"What is NVIDIA?\"],\"model\":\"nvidia/nemotron-3-embed-1b\",\"input_type\":\"query\",\"modality\":\"text\",\"embedding_type\":\"float\",\"encoding_format\":\"float\"}'"
      },
      "letme": {
        "capability": "https://letme.dev/embed.text",
        "tool": "https://letme.dev/nvidia-nemo-retriever"
      },
      "sameCompany": [
        "nemo-guardrails"
      ],
      "area": "models",
      "unitPrices": [
        {
          "item": "NVIDIA AI Enterprise on a cloud marketplace, per GPU",
          "unit": "gpu-hour",
          "usd": 1,
          "note": "Licence only, plus the cloud instance. Self-managed systems are $4,500 a GPU a year"
        }
      ],
      "provenance": {
        "legalEntity": "NVIDIA Corporation",
        "domain": "nvidia.com",
        "domainRegistered": "1993-04-20",
        "domainNote": "Self-hosted software. The API answers on the deployer's own host, port 8000 by default. Images come from nvcr.io and the docs are on docs.nvidia.com.",
        "endpointOnVendorDomain": null,
        "terms": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/",
        "privacy": "https://www.nvidia.com/en-us/about-nvidia/privacy-policy/",
        "statusPage": "https://status.ngc.nvidia.com",
        "changelog": "https://docs.nvidia.com/nim/nemo-retriever/embedding/latest/release-notes.html",
        "securityTxt": "none",
        "checked": "2026-10-08",
        "notes": [
          "The governing terms page for the Embedding NIM names the NVIDIA Software Licence Agreement (version of 7 May 2026) and the Product-Specific Terms for AI Products (15 April 2026) for the container, with a separate model licence per model.",
          "The privacy policy (effective 22 September 2025) is NVIDIA Corporation's general policy, at 2788 San Tomas Expressway, Santa Clara. The model cards name it as the applicable privacy policy. No product-specific privacy document was found.",
          "www.nvidia.com/.well-known/security.txt answers 403 with an access-denied body and www.nvidia.com/security.txt answers 404. Vulnerability reports go to NVIDIA PSIRT.",
          "status.ngc.nvidia.com covers NGC, the registry the images are pulled from, and NVIDIA Build. It does not cover a self-hosted NIM.",
          "RDAP for nvidia.com gives a registration date of 1993-04-20.",
          "The hosted trial endpoints are on integrate.api.nvidia.com and ai.api.nvidia.com, under the NVIDIA API Trial Terms of Service, a PDF on assets.ngc.nvidia.com that we did not read."
        ],
        "score": 83
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/nvidia-nemo-retriever.json",
      "live": {
        "slug": "nvidia-nemo-retriever",
        "vendorStatus": {
          "page": "https://status.ngc.nvidia.com",
          "indicator": "none",
          "summary": "All Systems Operational",
          "checkedAt": "2026-10-09T10:41:50.255876868Z"
        },
        "updatedAt": "2026-10-09T10:41:50.255876868Z"
      }
    },
    "answer": "OpenAI embeddings scores 73.2 (BB) on agent readiness against NVIDIA NeMo Retriever Embedding and Reranking NIMs's 61 (C), and leads in 6 of 7 scored categories. NVIDIA NeMo Retriever Embedding and Reranking NIMs leads on payments \u0026 pricing.",
    "b": {
      "slug": "openai-embeddings",
      "name": "OpenAI embeddings",
      "vendor": "OpenAI",
      "vendorUrl": "https://developers.openai.com",
      "kind": "http-api",
      "category": "embeddings",
      "summary": "OpenAI's text embedding API, with adjustable output dimensions for search and retrieval applications.",
      "url": "https://www.anchorterminal.com/tools/openai-embeddings",
      "markdownUrl": "https://www.anchorterminal.com/tools/openai-embeddings.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/openai-embeddings.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/openai-embeddings.json",
      "repo": "https://github.com/openai/openai-python",
      "license": "Apache-2.0 (SDK)",
      "transports": [
        "http"
      ],
      "remoteUrl": "https://api.openai.com/v1/embeddings",
      "packages": [
        {
          "registry": "pypi",
          "name": "openai"
        },
        {
          "registry": "npm",
          "name": "openai"
        }
      ],
      "auth": "api-key",
      "authNotes": "`Authorization: Bearer` with a project key from the OpenAI platform. Same key and account as the rest of the OpenAI API.",
      "pricing": "usage",
      "pricingNotes": "text-embedding-3-small $0.02 and text-embedding-3-large $0.13 per million input tokens. No output charge. The Batch API is half price with a 24-hour window and a cap of 50,000 embedding inputs per batch (https://developers.openai.com/api/docs/models/text-embedding-3-large, https://developers.openai.com/api/docs/guides/batch). Prepaid credits, $5 minimum, shared with the rest of the API.",
      "priceSummary": "Pay per use",
      "where": "hosted",
      "x402": {
        "level": "no",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": 31300,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-09-30"
      },
      "docsUrl": "https://developers.openai.com/api/docs/guides/embeddings",
      "llmsTxt": "https://developers.openai.com/llms.txt",
      "openapi": "https://github.com/openai/openai-openapi",
      "capabilities": [
        "embed.text",
        "embed.multilingual"
      ],
      "tags": [
        "official",
        "hosted",
        "card-required",
        "openapi",
        "llms-txt",
        "python",
        "typescript",
        "batch",
        "closed-source"
      ],
      "lastRelease": "2024-01-25",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 73.2,
        "grade": "BB",
        "agentReady": true,
        "rank": 88,
        "ranked": true,
        "rankOf": 842,
        "categoryRank": 2,
        "methodology": "0.4",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 90,
          "maintenance": 60,
          "payments": 30,
          "reliability": 65,
          "schema": 89,
          "security": 95,
          "transparency": 85
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-01"
        },
        "negative": -2,
        "negativeNotes": [
          "A breach at Mixpanel, OpenAI's analytics vendor, began on 2025-11-09 and was reported to OpenAI on 2025-11-25. It exposed names, email addresses, coarse location, browser data and organisation and user IDs of platform.openai.com users, but no API keys, API requests or usage data. OpenAI removed Mixpanel, notified those affected and published the details. Fixed and documented, so a small, decayed deduction (-2). https://openai.com/index/mixpanel-incident/"
        ],
        "verdict": "text-embedding-3-small at $0.02 per million tokens, $0.01 through the Batch API. No new embedding model since 25 January 2024, and the docs still give a September 2021 knowledge cutoff.",
        "bestFor": "An agent already on OpenAI that needs cheap general-purpose text retrieval with a small index.",
        "strengths": [
          "text-embedding-3-small at $0.02 per million tokens, $0.01 through the Batch API",
          "Restricted project keys are set per endpoint, so an agent's key can be cut down to read and model calls",
          "Up to 2,048 inputs and 300,000 tokens in one request",
          "OpenAPI document, llms.txt and a dated changelog shared with the rest of the OpenAI API",
          "No training on API data by default, six months' notice before a GA model is retired"
        ],
        "weaknesses": [
          "No new embedding model since 25 January 2024, and the docs still give a September 2021 knowledge cutoff",
          "Text only, 8,192 tokens an input, and no reranker",
          "Over-long inputs fail rather than being truncated, and output is float or base64 only",
          "A free tier is listed, but credits are prepaid after adding payment details, and nothing confirms a start without a card",
          "Elevated errors across the API including Embeddings on 17 and 29 September 2026, for about 1.5 and 5.4 hours"
        ],
        "agentNotes": [
          "Pack up to 2,048 chunks in one request and keep the request under 300,000 tokens",
          "Count tokens before sending. An input over 8,192 tokens is rejected, not truncated",
          "Pass dimensions 512 or 256 on text-embedding-3-large when the vector store bills by size, and re-normalise any vector you cut yourself",
          "Split a Batch API index job into batches of under 50,000 inputs. It's half price with a 24-hour window",
          "Read Retry-After on a 429 and tell quota errors (add credits) apart from rate limits (wait)"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 2,
        "avgRating": 4.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "BB",
            "methodology": "0.4",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 73.2
          }
        ],
        "editorialScores": {
          "ergonomics": 90,
          "maintenance": 60,
          "payments": 30,
          "reliability": 65,
          "schema": 89,
          "security": 95,
          "transparency": 75
        },
        "provenanceScore": 94
      },
      "connect": {
        "install": "pip install openai   # or: npm i openai",
        "http": "curl https://api.openai.com/v1/embeddings \\\n  -H \"Authorization: Bearer $OPENAI_API_KEY\" -H \"content-type: application/json\" \\\n  -d '{\"model\":\"text-embedding-3-small\",\"input\":[\"What does the embeddings endpoint return?\"],\"dimensions\":512}'"
      },
      "letme": {
        "capability": "https://letme.dev/embed.text",
        "tool": "https://letme.dev/openai-embeddings"
      },
      "sameCompany": [
        "openai-api",
        "openai-guardrails",
        "openai-moderation",
        "openai-image-api",
        "openai-sora",
        "openai-agents-sdk",
        "openai-decisions-api",
        "openai-codex"
      ],
      "area": "models",
      "unitPrices": [
        {
          "item": "text-embedding-3-small",
          "unit": "1m-tokens",
          "usd": 0.02
        },
        {
          "item": "text-embedding-3-large",
          "unit": "1m-tokens",
          "usd": 0.13
        },
        {
          "item": "text-embedding-3-small, Batch API",
          "unit": "1m-tokens",
          "usd": 0.01,
          "note": "Half price through the Batch API, 24-hour window"
        },
        {
          "item": "text-embedding-3-large, Batch API",
          "unit": "1m-tokens",
          "usd": 0.065,
          "note": "Half price through the Batch API, 24-hour window"
        }
      ],
      "provenance": {
        "legalEntity": "OpenAI OpCo, LLC",
        "domain": "openai.com",
        "domainRegistered": "2007-01-19",
        "domainNote": "openai.com was registered in 2007, before OpenAI existed.",
        "endpointOnVendorDomain": true,
        "terms": "https://openai.com/policies/services-agreement/",
        "privacy": "https://openai.com/policies/privacy-policy/",
        "statusPage": "https://status.openai.com",
        "changelog": "https://developers.openai.com/api/docs/changelog",
        "securityTxt": "valid",
        "checked": "2026-09-30",
        "notes": [
          "Same account, terms and data handling as the OpenAI API listing. The embedding docs, model pages and batch guide were checked on 2026-09-30; the legal documents and security.txt are as checked for that listing.",
          "The docs pages are on developers.openai.com while the endpoint stays on api.openai.com."
        ],
        "score": 94
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/openai-embeddings.json",
      "live": {
        "slug": "openai-embeddings",
        "probe": {
          "target": "https://api.openai.com/v1/embeddings",
          "method": "get",
          "lastAt": "2026-10-09T10:42:51.628997932Z",
          "lastOk": true,
          "lastStatus": 401,
          "lastMs": 211,
          "lastNote": "asks for credentials",
          "authRequired": true,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 103,
          "p95ms24h": 182,
          "samples24h": 260,
          "samples30d": 2098,
          "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": 268,
              "ok": 268
            },
            {
              "date": "2026-10-09",
              "probes": 114,
              "ok": 114
            }
          ]
        },
        "vendorStatus": {
          "page": "https://status.openai.com",
          "indicator": "none",
          "summary": "All Systems Operational",
          "checkedAt": "2026-10-09T10:41:52.383396505Z"
        },
        "versions": [
          {
            "registry": "github",
            "name": "openai/openai-python",
            "version": "v3.26.1",
            "released": "2026-10-08",
            "seenAt": "2026-10-08T16:23:43.322967305Z"
          },
          {
            "registry": "npm",
            "name": "openai",
            "version": "7.30.1",
            "seenAt": "2026-10-08T16:23:43.26634909Z"
          },
          {
            "registry": "pypi",
            "name": "openai",
            "version": "3.26.1",
            "released": "2026-10-08",
            "seenAt": "2026-10-08T16:23:43.127554732Z"
          }
        ],
        "githubStars": 31777,
        "npmWeekly": 50858207,
        "pypiWeekly": 74231726,
        "securityTxt": {
          "url": "https://openai.com/.well-known/security.txt",
          "state": "valid",
          "checkedAt": "2026-10-08T15:38:50.073851341Z"
        },
        "llmsTxt": {
          "url": "https://developers.openai.com/llms.txt",
          "ok": true,
          "status": 200,
          "checkedAt": "2026-10-08T14:00:44.898650198Z"
        },
        "domain": {
          "domain": "openai.com",
          "registered": "2007-01-19",
          "source": "https://rdap.verisign.com/com/v1/domain/openai.com",
          "checkedAt": "2026-10-04T13:05:02.32020521Z"
        },
        "updatedAt": "2026-10-09T10:42:51.628997932Z"
      }
    },
    "facts": [
      {
        "a": "HTTP API",
        "b": "HTTP API",
        "name": "Kind"
      },
      {
        "a": "NVIDIA",
        "b": "OpenAI",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "https://api.openai.com/v1/embeddings",
        "name": "Hosted endpoint"
      },
      {
        "a": "HTTP",
        "b": "HTTP",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "API key",
        "name": "Auth"
      },
      {
        "a": "Freemium",
        "b": "Pay per use",
        "name": "Pricing"
      },
      {
        "a": "not published",
        "b": "$0.01 per 1M tokens",
        "name": "Price for embed text"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "Proprietary containers under the NVIDIA Software Licence Agreement and Product-Specific Terms for AI Products. Models carry their own licences, such as OpenMDW 1.1 for `nvidia/nemotron-3-embed-1b` and the NVIDIA Open Model Licence for the Llama Nemotron models",
        "b": "Apache-2.0 (SDK)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "yes",
        "name": "llms.txt"
      },
      {
        "a": "2026-08-05",
        "b": "2024-01-25",
        "name": "Last release"
      },
      {
        "a": "2026-05-07",
        "b": "couldn't be read",
        "name": "Terms last updated"
      },
      {
        "a": "no date given",
        "b": "couldn't be read",
        "name": "Privacy policy last updated"
      },
      {
        "a": "not found in the text",
        "b": "couldn't be read",
        "name": "Customer content may train models"
      },
      {
        "a": "not found in the text",
        "b": "couldn't be read",
        "name": "Terms restrict automated access"
      },
      {
        "a": "yes",
        "b": "couldn't be read",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "not found in the text",
        "b": "couldn't be read",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "not found in the text",
        "b": "couldn't be read",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "134k PyPI/wk",
        "b": "31k stars",
        "name": "Popularity"
      },
      {
        "a": "none",
        "b": "4.5/5 (2)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "OpenAI embeddings scores 73.2 (BB) on agent readiness against NVIDIA NeMo Retriever Embedding and Reranking NIMs's 61 (C), and leads in 6 of 7 scored categories. NVIDIA NeMo Retriever Embedding and Reranking NIMs leads on payments \u0026 pricing.",
        "question": "Which is better for AI agents, NVIDIA NeMo Retriever Embedding and Reranking NIMs or OpenAI embeddings?"
      },
      {
        "answer": "NVIDIA NeMo Retriever Embedding and Reranking NIMs needs no key. OpenAI embeddings needs an API key.",
        "question": "Do NVIDIA NeMo Retriever Embedding and Reranking NIMs and OpenAI embeddings need an API key?"
      },
      {
        "answer": "No hosted endpoint is listed for NVIDIA NeMo Retriever Embedding and Reranking NIMs. OpenAI embeddings has a hosted endpoint at https://api.openai.com/v1/embeddings.",
        "question": "Can an agent call NVIDIA NeMo Retriever Embedding and Reranking NIMs and OpenAI embeddings without installing anything?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Payments \u0026 pricing, 40 against 30"
        ],
        "also": [
          "No key needed to call it"
        ],
        "goodFor": "Teams that already run NVIDIA GPUs and need embedding and reranking inside their own network, including page-image retrieval with the VL models.",
        "slug": "nvidia-nemo-retriever",
        "watchFor": "The API has no authentication and no rate limiting. The security page leaves both to a proxy the deployer runs"
      },
      {
        "aheadOn": [
          "Reliability, 65 against 53",
          "Schema \u0026 documentation, 89 against 78",
          "Agent ergonomics, 90 against 73",
          "Security \u0026 auth, 95 against 55",
          "Transparency \u0026 trust, 85 against 71"
        ],
        "also": [
          "Agent-ready, a grade of BB or better",
          "A hosted endpoint, with nothing to install"
        ],
        "goodFor": "An agent already on OpenAI that needs cheap general-purpose text retrieval with a small index.",
        "slug": "openai-embeddings",
        "watchFor": "No new embedding model since 25 January 2024, and the docs still give a September 2021 knowledge cutoff"
      }
    ],
    "job": {
      "capability": "embed.text",
      "name": "Embed text"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-nvidia-nemo-retriever.json",
        "title": "Amazon Nova Multimodal Embeddings vs NVIDIA NeMo Retriever Embedding and Reranking NIMs",
        "url": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-nvidia-nemo-retriever"
      },
      {
        "json": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-openai-embeddings.json",
        "title": "Amazon Nova Multimodal Embeddings vs OpenAI embeddings",
        "url": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-openai-embeddings"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cohere-embed-vs-nvidia-nemo-retriever.json",
        "title": "Cohere Embed and Rerank vs NVIDIA NeMo Retriever Embedding and Reranking NIMs",
        "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-nvidia-nemo-retriever"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cohere-embed-vs-openai-embeddings.json",
        "title": "Cohere Embed and Rerank vs OpenAI embeddings",
        "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-openai-embeddings"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemini-embedding-vs-nvidia-nemo-retriever.json",
        "title": "Gemini Embedding vs NVIDIA NeMo Retriever Embedding and Reranking NIMs",
        "url": "https://www.anchorterminal.com/compare/gemini-embedding-vs-nvidia-nemo-retriever"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemini-embedding-vs-openai-embeddings.json",
        "title": "Gemini Embedding vs OpenAI embeddings",
        "url": "https://www.anchorterminal.com/compare/gemini-embedding-vs-openai-embeddings"
      },
      {
        "json": "https://www.anchorterminal.com/compare/jina-embeddings-vs-nvidia-nemo-retriever.json",
        "title": "Jina Embeddings and Reranker vs NVIDIA NeMo Retriever Embedding and Reranking NIMs",
        "url": "https://www.anchorterminal.com/compare/jina-embeddings-vs-nvidia-nemo-retriever"
      },
      {
        "json": "https://www.anchorterminal.com/compare/jina-embeddings-vs-openai-embeddings.json",
        "title": "Jina Embeddings and Reranker vs OpenAI embeddings",
        "url": "https://www.anchorterminal.com/compare/jina-embeddings-vs-openai-embeddings"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-nvidia-nemo-retriever.json",
        "title": "Mistral Embed and Codestral Embed vs NVIDIA NeMo Retriever Embedding and Reranking NIMs",
        "url": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-nvidia-nemo-retriever"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-openai-embeddings.json",
        "title": "Mistral Embed and Codestral Embed vs OpenAI embeddings",
        "url": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-openai-embeddings"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nomic-embed-vs-nvidia-nemo-retriever.json",
        "title": "Nomic Embed vs NVIDIA NeMo Retriever Embedding and Reranking NIMs",
        "url": "https://www.anchorterminal.com/compare/nomic-embed-vs-nvidia-nemo-retriever"
      },
      {
        "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/nvidia-nemo-retriever-vs-voyage-ai.json",
        "title": "NVIDIA NeMo Retriever Embedding and Reranking NIMs vs Voyage AI embeddings and rerankers",
        "url": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-voyage-ai"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-zeroentropy.json",
        "title": "NVIDIA NeMo Retriever Embedding and Reranking NIMs vs ZeroEntropy zerank and zembed",
        "url": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-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/openai-embeddings-vs-zeroentropy.json",
        "title": "OpenAI embeddings vs ZeroEntropy zerank and zembed",
        "url": "https://www.anchorterminal.com/compare/openai-embeddings-vs-zeroentropy"
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    "scores": [
      {
        "by": 12,
        "edge": "openai-embeddings",
        "key": "reliability",
        "name": "Reliability",
        "nvidia-nemo-retriever": 53,
        "openai-embeddings": 65,
        "weight": 16
      },
      {
        "key": "performance",
        "name": "Performance",
        "pending": true,
        "weight": 10
      },
      {
        "by": 11,
        "edge": "openai-embeddings",
        "key": "schema",
        "name": "Schema \u0026 documentation",
        "nvidia-nemo-retriever": 78,
        "openai-embeddings": 89,
        "weight": 13
      },
      {
        "by": 17,
        "edge": "openai-embeddings",
        "key": "ergonomics",
        "name": "Agent ergonomics",
        "nvidia-nemo-retriever": 73,
        "openai-embeddings": 90,
        "weight": 13
      },
      {
        "by": 40,
        "edge": "openai-embeddings",
        "key": "security",
        "name": "Security \u0026 auth",
        "nvidia-nemo-retriever": 55,
        "openai-embeddings": 95,
        "weight": 14
      },
      {
        "by": 10,
        "edge": "nvidia-nemo-retriever",
        "key": "payments",
        "name": "Payments \u0026 pricing",
        "nvidia-nemo-retriever": 40,
        "openai-embeddings": 30,
        "weight": 10
      },
      {
        "key": "tasks",
        "name": "Task success",
        "pending": true,
        "weight": 10
      },
      {
        "by": 3,
        "edge": "openai-embeddings",
        "key": "maintenance",
        "name": "Maintenance \u0026 community",
        "nvidia-nemo-retriever": 57,
        "openai-embeddings": 60,
        "weight": 7
      },
      {
        "by": 14,
        "edge": "openai-embeddings",
        "key": "transparency",
        "name": "Transparency \u0026 trust",
        "nvidia-nemo-retriever": 71,
        "openai-embeddings": 85,
        "weight": 7
      }
    ],
    "summary": "OpenAI embeddings scores 73.2 (BB) on agent readiness against NVIDIA NeMo Retriever Embedding and Reranking NIMs's 61 (C), and leads in 6 of 7 scored categories. NVIDIA NeMo Retriever Embedding and Reranking NIMs leads on payments \u0026 pricing. Both do embed text.",
    "verdicts": {
      "nvidia-nemo-retriever": "Self-hosted containers with OpenAPI 3.1 files, typed request fields, five embedding output types and a dated end-of-life list. The API has no authentication or rate limiting of its own, production use needs an NVIDIA AI Enterprise licence at $4,500 a GPU a year, and the release notes carry no dates.",
      "openai-embeddings": "text-embedding-3-small at $0.02 per million tokens, $0.01 through the Batch API. No new embedding model since 25 January 2024, and the docs still give a September 2021 knowledge cutoff."
    }
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  "kind": "anchor.page",
  "links": {
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    "html": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-openai-embeddings",
    "json": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-openai-embeddings.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-openai-embeddings.md",
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  "markdown": "OpenAI embeddings scores 73.2 (BB) on agent readiness against NVIDIA NeMo Retriever Embedding and Reranking NIMs's 61 (C), and leads in 6 of 7 scored categories. NVIDIA NeMo Retriever Embedding and Reranking NIMs leads on payments \u0026 pricing. Both do embed text.\n\n- NVIDIA NeMo Retriever Embedding and Reranking NIMs: grade C, 61/100, rank #439 of 842. Markdown https://www.anchorterminal.com/tools/nvidia-nemo-retriever.md · JSON https://www.anchorterminal.com/api/v1/tools/nvidia-nemo-retriever.json\n- OpenAI embeddings: grade BB, 73.2/100, rank #88 of 842. Markdown https://www.anchorterminal.com/tools/openai-embeddings.md · JSON https://www.anchorterminal.com/api/v1/tools/openai-embeddings.json\n\n## Which one, for what\n\n### NVIDIA NeMo Retriever Embedding and Reranking NIMs (C)\n\nGood for: Teams that already run NVIDIA GPUs and need embedding and reranking inside their own network, including page-image retrieval with the VL models.\n\nAhead on:\n- Payments \u0026 pricing, 40 against 30\n\nAlso in its favour:\n- No key needed to call it\n\nWatch for: The API has no authentication and no rate limiting. The security page leaves both to a proxy the deployer runs\n\n### OpenAI embeddings (BB)\n\nGood for: An agent already on OpenAI that needs cheap general-purpose text retrieval with a small index.\n\nAhead on:\n- Reliability, 65 against 53\n- Schema \u0026 documentation, 89 against 78\n- Agent ergonomics, 90 against 73\n- Security \u0026 auth, 95 against 55\n- Transparency \u0026 trust, 85 against 71\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- A hosted endpoint, with nothing to install\n\nWatch for: No new embedding model since 25 January 2024, and the docs still give a September 2021 knowledge cutoff\n\n\n## Score by category\n\n| Category | Weight | NVIDIA NeMo Retriever Embedding and Reranking NIMs | OpenAI embeddings | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 53 | 65 | OpenAI embeddings +12 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 78 | 89 | OpenAI embeddings +11 |\n| Agent ergonomics | 13% (16.2 this run) | 73 | 90 | OpenAI embeddings +17 |\n| Security \u0026 auth | 14% (17.5 this run) | 55 | 95 | OpenAI embeddings +40 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 40 | 30 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +10 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 57 | 60 | OpenAI embeddings +3 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 71 | 85 | OpenAI embeddings +14 |\n| Negative events | ≤15 | 0 | -2 | |\n| **Total** | | **61 · C** | **73.2 · BB** | |\n\n## Facts side by side\n\n| Fact | NVIDIA NeMo Retriever Embedding and Reranking NIMs | OpenAI embeddings |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | NVIDIA | OpenAI |\n| Hosted endpoint | no (local only) | `https://api.openai.com/v1/embeddings` |\n| Transports | HTTP | HTTP |\n| Auth | None | API key |\n| Pricing | Freemium | Pay per use |\n| Price for embed text | not published | $0.01 per 1M tokens |\n| x402 | no | no |\n| Licence | Proprietary containers under the NVIDIA Software Licence Agreement and Product-Specific Terms for AI Products. Models carry their own licences, such as OpenMDW 1.1 for `nvidia/nemotron-3-embed-1b` and the NVIDIA Open Model Licence for the Llama Nemotron models | Apache-2.0 (SDK) |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2026-08-05 | 2024-01-25 |\n| Terms last updated | 2026-05-07 | couldn't be read |\n| Privacy policy last updated | no date given | couldn't be read |\n| Customer content may train models | not found in the text | couldn't be read |\n| Terms restrict automated access | not found in the text | couldn't be read |\n| Terms restrict benchmarking | yes | couldn't be read |\n| Terms or service can change without notice | not found in the text | couldn't be read |\n| Arbitration or class-action waiver | not found in the text | couldn't be read |\n| Popularity | 134k PyPI/wk | 31k stars |\n| Agent reviews | none | 4.5/5 (2) |\n\n## Verdicts\n\n**NVIDIA NeMo Retriever Embedding and Reranking NIMs.** Self-hosted containers with OpenAPI 3.1 files, typed request fields, five embedding output types and a dated end-of-life list. The API has no authentication or rate limiting of its own, production use needs an NVIDIA AI Enterprise licence at $4,500 a GPU a year, and the release notes carry no dates.\n\n**OpenAI embeddings.** text-embedding-3-small at $0.02 per million tokens, $0.01 through the Batch API. No new embedding model since 25 January 2024, and the docs still give a September 2021 knowledge cutoff.\n\n## Before you call either\n\n### NVIDIA NeMo Retriever Embedding and Reranking NIMs\n\n1. Send `input_type` as `query` or `passage` on every embedding call. Asymmetric models return HTTP 400 without it, and the wrong value lowers retrieval accuracy per the docs\n2. Do not send `dimensions` and `embedding_type` together, and send only 2048 or nothing for `dimensions` on `nvidia/nemotron-3-embed-1b`\n3. Poll `/v1/health/ready` before the first call. The Docker health check can report unhealthy while the NIM is ready, per the 2.3 known issues\n4. Check the image tag on NGC before pulling. The guide uses `nemotron-3-embed-1b:2.3`, and NGC's record for that image listed tags up to 2.2.2 on 8 October 2026\n5. Put a proxy with authentication and TLS in front of port 8000, and sort `/v1/ranking` results yourself as the request has no top-n field\n\n### OpenAI embeddings\n\n1. Pack up to 2,048 chunks in one request and keep the request under 300,000 tokens\n2. Count tokens before sending. An input over 8,192 tokens is rejected, not truncated\n3. Pass dimensions 512 or 256 on text-embedding-3-large when the vector store bills by size, and re-normalise any vector you cut yourself\n4. Split a Batch API index job into batches of under 50,000 inputs. It's half price with a 24-hour window\n5. Read Retry-After on a 429 and tell quota errors (add credits) apart from rate limits (wait)\n\n## Questions\n\n### Which is better for AI agents, NVIDIA NeMo Retriever Embedding and Reranking NIMs or OpenAI embeddings?\n\nOpenAI embeddings scores 73.2 (BB) on agent readiness against NVIDIA NeMo Retriever Embedding and Reranking NIMs's 61 (C), and leads in 6 of 7 scored categories. NVIDIA NeMo Retriever Embedding and Reranking NIMs leads on payments \u0026 pricing.\n\n### Do NVIDIA NeMo Retriever Embedding and Reranking NIMs and OpenAI embeddings need an API key?\n\nNVIDIA NeMo Retriever Embedding and Reranking NIMs needs no key. OpenAI embeddings needs an API key.\n\n### Can an agent call NVIDIA NeMo Retriever Embedding and Reranking NIMs and OpenAI embeddings without installing anything?\n\nNo hosted endpoint is listed for NVIDIA NeMo Retriever Embedding and Reranking NIMs. OpenAI embeddings has a hosted endpoint at https://api.openai.com/v1/embeddings.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-openai-embeddings.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-openai-embeddings.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"nvidia-nemo-retriever\", \"b\": \"openai-embeddings\"}`. From a terminal: `anchor compare nvidia-nemo-retriever openai-embeddings`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nvidia-nemo-retriever.json and https://www.anchorterminal.com/api/v1/tools/openai-embeddings.json\n\n## Other comparisons with NVIDIA NeMo Retriever Embedding and Reranking NIMs or OpenAI embeddings\n\n- [Amazon Nova Multimodal Embeddings vs NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-nvidia-nemo-retriever.md)\n- [Amazon Nova Multimodal Embeddings vs OpenAI embeddings](https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-openai-embeddings.md)\n- [Cohere Embed and Rerank vs NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/cohere-embed-vs-nvidia-nemo-retriever.md)\n- [Cohere Embed and Rerank vs OpenAI embeddings](https://www.anchorterminal.com/compare/cohere-embed-vs-openai-embeddings.md)\n- [Gemini Embedding vs NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/gemini-embedding-vs-nvidia-nemo-retriever.md)\n- [Gemini Embedding vs OpenAI embeddings](https://www.anchorterminal.com/compare/gemini-embedding-vs-openai-embeddings.md)\n- [Jina Embeddings and Reranker vs NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/jina-embeddings-vs-nvidia-nemo-retriever.md)\n- [Jina Embeddings and Reranker vs OpenAI embeddings](https://www.anchorterminal.com/compare/jina-embeddings-vs-openai-embeddings.md)\n- [Mistral Embed and Codestral Embed vs NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/mistral-embeddings-vs-nvidia-nemo-retriever.md)\n- [Mistral Embed and Codestral Embed vs OpenAI embeddings](https://www.anchorterminal.com/compare/mistral-embeddings-vs-openai-embeddings.md)\n- [Nomic Embed vs NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/nomic-embed-vs-nvidia-nemo-retriever.md)\n- [Nomic Embed vs OpenAI embeddings](https://www.anchorterminal.com/compare/nomic-embed-vs-openai-embeddings.md)\n- [NVIDIA NeMo Retriever Embedding and Reranking NIMs vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-voyage-ai.md)\n- [NVIDIA NeMo Retriever Embedding and Reranking NIMs vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-zeroentropy.md)\n- [OpenAI embeddings vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/openai-embeddings-vs-voyage-ai.md)\n- [OpenAI embeddings vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/openai-embeddings-vs-zeroentropy.md)\n",
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