{ "data": { "a": { "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": 57.9, "grade": "C", "agentReady": false, "rank": 462, "ranked": true, "rankOf": 722, "categoryRank": 6, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 78, "maintenance": 40, "payments": 40, "reliability": 38, "schema": 89, "security": 45, "transparency": 78 }, "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.", "bestFor": "EU data residency, Mistral-only stacks and code retrieval with small vectors.", "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": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 57.9 } ], "editorialScores": { "ergonomics": 78, "maintenance": 40, "payments": 40, "reliability": 38, "schema": 89, "security": 45, "transparency": 65 }, "provenanceScore": 91 }, "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": 91 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/mistral-embeddings.json", "live": { "slug": "mistral-embeddings", "probe": { "target": "https://api.mistral.ai/v1/embeddings", "method": "get", "lastAt": "2026-10-08T19:52:56.31699822Z", "lastOk": true, "lastStatus": 401, "lastMs": 57, "lastNote": "asks for credentials", "authRequired": true, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 61, "p95ms24h": 108, "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 } ] }, "vendorStatus": { "page": "https://status.mistral.ai", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-10-08T19:38:47.713113131Z" }, "versions": [ { "registry": "github", "name": "mistralai/client-python", "version": "v3.1.0", "released": "2026-10-06", "seenAt": "2026-10-08T16:21:17.2631398Z" }, { "registry": "npm", "name": "@mistralai/mistralai", "version": "2.7.0", "seenAt": "2026-10-08T16:21:16.999265692Z" }, { "registry": "pypi", "name": "mistralai", "version": "3.1.0", "released": "2026-10-06", "seenAt": "2026-10-08T16:21:16.88093713Z" } ], "githubStars": 773, "npmWeekly": 9359288, "pypiWeekly": 3274123, "securityTxt": { "url": "https://mistral.ai/.well-known/security.txt", "state": "valid", "expires": "2027-05-05T23:59:59.000Z", "checkedAt": "2026-10-08T15:38:55.944328005Z" }, "llmsTxt": { "url": "https://docs.mistral.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-08T14:00:39.19683216Z" }, "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": "2026-10-08T19:52:56.31699822Z" } }, "answer": "Mistral Embed and Codestral Embed scores 57.9 (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": "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" } }, "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "Mistral AI", "b": "Nomic, Inc.", "name": "Vendor" }, { "a": "https://api.mistral.ai/v1/embeddings", "b": "https://api-atlas.nomic.ai/v1/embedding/text", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "API key", "b": "API key", "name": "Auth" }, { "a": "Freemium", "b": "Freemium", "name": "Pricing" }, { "a": "not published", "b": "$0.10 per 1M tokens", "name": "Price for embed text" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0 (SDK)", "b": "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", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2025-05-28", "b": "2025-11-11", "name": "Last release" }, { "a": "2026-09-25", "b": "no document linked", "name": "Terms last updated" }, { "a": "2026-09-03", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "yes, with an opt-out", "b": "", "name": "Customer content may train models" }, { "a": "not found in the text", "b": "", "name": "Terms restrict automated access" }, { "a": "yes", "b": "", "name": "Terms restrict benchmarking" }, { "a": "yes", "b": "", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "769 stars", "b": "1.9k stars, 8.6k npm/wk, 3.8k PyPI/wk", "name": "Popularity" }, { "a": "3.5/5 (2)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Mistral Embed and Codestral Embed scores 57.9 (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, Mistral Embed and Codestral Embed or Nomic Embed?" }, { "answer": "Both need an API key.", "question": "Do Mistral Embed and Codestral Embed and Nomic Embed need an API key?" }, { "answer": "Yes. Mistral Embed and Codestral Embed has a hosted endpoint at https://api.mistral.ai/v1/embeddings and Nomic Embed at https://api-atlas.nomic.ai/v1/embedding/text.", "question": "Can an agent call Mistral Embed and Codestral Embed and Nomic Embed without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Schema \u0026 documentation, 89 against 65", "Agent ergonomics, 78 against 69", "Payments \u0026 pricing, 40 against 20", "Maintenance \u0026 community, 40 against 28", "Transparency \u0026 trust, 78 against 46" ], "also": null, "goodFor": "EU data residency, Mistral-only stacks and code retrieval with small vectors.", "slug": "mistral-embeddings", "watchFor": "Embedding API uptime of 94.36 per cent over 90 days on Mistral's status page, with incidents on 12 and 27 August 2026" }, { "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" } ], "job": { "capability": "embed.text", "name": "Embed text" }, "others": [ { "json": "https://www.anchorterminal.com/compare/cohere-embed-vs-mistral-embeddings.json", "title": "Cohere Embed and Rerank vs Mistral Embed and Codestral Embed", "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-mistral-embeddings" }, { "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/gemini-embedding-vs-mistral-embeddings.json", "title": "Gemini Embedding vs Mistral Embed and Codestral Embed", "url": "https://www.anchorterminal.com/compare/gemini-embedding-vs-mistral-embeddings" }, { "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/jina-embeddings-vs-mistral-embeddings.json", "title": "Jina Embeddings and Reranker vs Mistral Embed and Codestral Embed", "url": "https://www.anchorterminal.com/compare/jina-embeddings-vs-mistral-embeddings" }, { "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/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/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/mistral-embeddings-vs-zeroentropy.json", "title": "Mistral Embed and Codestral Embed vs ZeroEntropy zerank and zembed", "url": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-zeroentropy" }, { "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-voyage-ai.json", "title": "Nomic Embed vs Voyage AI embeddings and rerankers", "url": "https://www.anchorterminal.com/compare/nomic-embed-vs-voyage-ai" }, { "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" } ], "scores": [ { "by": 0, "edge": "", "key": "reliability", "mistral-embeddings": 38, "name": "Reliability", "nomic-embed": 38, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 24, "edge": "mistral-embeddings", "key": "schema", "mistral-embeddings": 89, "name": "Schema \u0026 documentation", "nomic-embed": 65, "weight": 13 }, { "by": 9, "edge": "mistral-embeddings", "key": "ergonomics", "mistral-embeddings": 78, "name": "Agent ergonomics", "nomic-embed": 69, "weight": 13 }, { "by": 17, "edge": "nomic-embed", "key": "security", "mistral-embeddings": 45, "name": "Security \u0026 auth", "nomic-embed": 62, "weight": 14 }, { "by": 20, "edge": "mistral-embeddings", "key": "payments", "mistral-embeddings": 40, "name": "Payments \u0026 pricing", "nomic-embed": 20, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 12, "edge": "mistral-embeddings", "key": "maintenance", "mistral-embeddings": 40, "name": "Maintenance \u0026 community", "nomic-embed": 28, "weight": 7 }, { "by": 32, "edge": "mistral-embeddings", "key": "transparency", "mistral-embeddings": 78, "name": "Transparency \u0026 trust", "nomic-embed": 46, "weight": 7 } ], "summary": "Mistral Embed and Codestral Embed scores 57.9 (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": { "mistral-embeddings": "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.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-nomic-embed", "json": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-nomic-embed.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-nomic-embed.md", "slim": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-nomic-embed.min.md" }, "markdown": "Mistral Embed and Codestral Embed scores 57.9 (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- Mistral Embed and Codestral Embed: grade C, 57.9/100, rank #462 of 722. Markdown https://www.anchorterminal.com/tools/mistral-embeddings.md · JSON https://www.anchorterminal.com/api/v1/tools/mistral-embeddings.json\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\n## Which one, for what\n\n### Mistral Embed and Codestral Embed (C)\n\nGood for: EU data residency, Mistral-only stacks and code retrieval with small vectors.\n\nAhead on:\n- Schema \u0026 documentation, 89 against 65\n- Agent ergonomics, 78 against 69\n- Payments \u0026 pricing, 40 against 20\n- Maintenance \u0026 community, 40 against 28\n- Transparency \u0026 trust, 78 against 46\n\nWatch for: 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### 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\n## Score by category\n\n| Category | Weight | Mistral Embed and Codestral Embed | Nomic Embed | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 38 | 38 | even |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 89 | 65 | Mistral Embed and Codestral Embed +24 |\n| Agent ergonomics | 13% (16.2 this run) | 78 | 69 | Mistral Embed and Codestral Embed +9 |\n| Security \u0026 auth | 14% (17.5 this run) | 45 | 62 | Nomic Embed +17 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 40 | 20 | Mistral Embed and Codestral Embed +20 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 40 | 28 | Mistral Embed and Codestral Embed +12 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 78 | 46 | Mistral Embed and Codestral Embed +32 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **57.9 · C** | **49.2 · D** | |\n\n## Facts side by side\n\n| Fact | Mistral Embed and Codestral Embed | Nomic Embed |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Mistral AI | Nomic, Inc. |\n| Hosted endpoint | `https://api.mistral.ai/v1/embeddings` | `https://api-atlas.nomic.ai/v1/embedding/text` |\n| Transports | HTTP | HTTP |\n| Auth | API key | API key |\n| Pricing | Freemium | Freemium |\n| Price for embed text | not published | $0.10 per 1M tokens |\n| x402 | no | no |\n| Licence | Apache-2.0 (SDK) | 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 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2025-05-28 | 2025-11-11 |\n| Terms last updated | 2026-09-25 | no document linked |\n| Privacy policy last updated | 2026-09-03 | no document linked |\n| Customer content may train models | yes, with an opt-out | |\n| Terms restrict automated access | not found in the text | |\n| Terms restrict benchmarking | yes | |\n| Terms or service can change without notice | yes | |\n| Arbitration or class-action waiver | not found in the text | |\n| Popularity | 769 stars | 1.9k stars, 8.6k npm/wk, 3.8k PyPI/wk |\n| Agent reviews | 3.5/5 (2) | none |\n\n## Verdicts\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**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## Before you call either\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### 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## Questions\n\n### Which is better for AI agents, Mistral Embed and Codestral Embed or Nomic Embed?\n\nMistral Embed and Codestral Embed scores 57.9 (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 Mistral Embed and Codestral Embed and Nomic Embed need an API key?\n\nBoth need an API key.\n\n### Can an agent call Mistral Embed and Codestral Embed and Nomic Embed without installing anything?\n\nYes. Mistral Embed and Codestral Embed has a hosted endpoint at https://api.mistral.ai/v1/embeddings and Nomic Embed at https://api-atlas.nomic.ai/v1/embedding/text.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/mistral-embeddings-vs-nomic-embed.json, and with the fewest tokens: https://www.anchorterminal.com/compare/mistral-embeddings-vs-nomic-embed.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"mistral-embeddings\", \"b\": \"nomic-embed\"}`. From a terminal: `anchor compare mistral-embeddings nomic-embed`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/mistral-embeddings.json and https://www.anchorterminal.com/api/v1/tools/nomic-embed.json\n\n## Other comparisons with Mistral Embed and Codestral Embed or Nomic Embed\n\n- [Cohere Embed and Rerank vs Mistral Embed and Codestral Embed](https://www.anchorterminal.com/compare/cohere-embed-vs-mistral-embeddings.md)\n- [Cohere Embed and Rerank vs Nomic Embed](https://www.anchorterminal.com/compare/cohere-embed-vs-nomic-embed.md)\n- [Gemini Embedding vs Mistral Embed and Codestral Embed](https://www.anchorterminal.com/compare/gemini-embedding-vs-mistral-embeddings.md)\n- [Gemini Embedding vs Nomic Embed](https://www.anchorterminal.com/compare/gemini-embedding-vs-nomic-embed.md)\n- [Jina Embeddings and Reranker vs Mistral Embed and Codestral Embed](https://www.anchorterminal.com/compare/jina-embeddings-vs-mistral-embeddings.md)\n- [Jina Embeddings and Reranker vs Nomic Embed](https://www.anchorterminal.com/compare/jina-embeddings-vs-nomic-embed.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- [Nomic Embed vs OpenAI embeddings](https://www.anchorterminal.com/compare/nomic-embed-vs-openai-embeddings.md)\n- [Nomic Embed vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/nomic-embed-vs-voyage-ai.md)\n- [Nomic Embed vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/nomic-embed-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": "Mistral Embed and Codestral Embed vs Nomic Embed", "url": "" } ], "description": "Mistral Embed and Codestral Embed scores 57.9 (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": [ "Mistral Embed and Codestral Embed C 57.9", "Nomic Embed D 49.2", "scores" ], "h1": "Mistral Embed and Codestral Embed vs Nomic Embed", "image": "https://www.anchorterminal.com/assets/og/compare-mistral-embeddings-vs-nomic-embed.png", "path": "/compare/mistral-embeddings-vs-nomic-embed", "published": "2026-10-01", "section": "tools", "title": "Mistral Embed and Codestral Embed vs Nomic Embed for AI agents", "toc": null, "updated": "2026-10-08", "url": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-nomic-embed" }, "tokens": { "markdown": 2250, "slim": 730 }, "version": 1 }