{ "data": { "a": { "slug": "jina-embeddings", "name": "Jina Embeddings and Reranker", "vendor": "Jina AI (Elastic)", "vendorUrl": "https://jina.ai", "kind": "http-api", "category": "embeddings", "summary": "jina-embeddings-v5 in text and omni (text, image, audio, video, PDF) variants at up to 32,768 tokens, plus the jina-reranker-v3.5 at 131,072 tokens a call.", "url": "https://www.anchorterminal.com/tools/jina-embeddings", "markdownUrl": "https://www.anchorterminal.com/tools/jina-embeddings.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/jina-embeddings.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/jina-embeddings.json", "repo": "https://github.com/jina-ai/MCP", "license": "Apache-2.0 (MCP server)", "transports": [ "http", "streamable-http" ], "remoteUrl": "https://api.jina.ai/v1/embeddings", "packages": [], "auth": "api-key", "authNotes": "`Authorization: Bearer` with a `jina_...` key. A new account gets a key with free tokens, and the same key works for Reader, Search, Embeddings, Reranker and the MCP server.", "pricing": "freemium", "pricingNotes": "Prepaid tokens, topped up through Stripe (cards, Google Pay, PayPal) and shared across every Jina API. A new key comes with free tokens. Non-text inputs are converted to tokens by the encoder, about 363 tokens an image on v5-omni, 4,840 on v4 and 16,000 on jina-clip-v2. Jina changed its pricing model on 2025-05-06, and the public pages don't state a US dollar price per token, so we don't list one (https://jina.ai/embeddings/).", "priceSummary": "Freemium", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": 12, "popularity": { "githubStars": 841, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-09-30" }, "docsUrl": "https://jina.ai/embeddings/", "llmsTxt": "https://jina.ai/models/llms.txt", "openapi": "https://api.jina.ai/openapi.json", "capabilities": [ "embed.text", "embed.multimodal", "embed.code", "embed.multilingual", "rerank" ], "tags": [ "hosted", "freemium", "free-tier", "no-card", "mcp", "prepaid", "eu" ], "lastRelease": "2026-09-18", "graded": true, "anchor": { "graded": true, "score": 61.3, "grade": "C", "agentReady": false, "rank": 230, "ranked": true, "rankOf": 452, "categoryRank": 4, "methodology": "0.3", "run": "2026-10-01", "scores": { "ergonomics": 86, "maintenance": 62, "payments": 30, "reliability": 65, "schema": 84, "security": 35, "transparency": 61 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "jina-reranker-v3.5 (20 July 2026) with a 131,072-token window and no document cap. No price per token in any currency on the public pages.", "strengths": [ "jina-reranker-v3.5 (20 July 2026) with a 131,072-token window and no document cap", "v5-omni embeds text, images, audio, video and PDFs into one space", "OpenAPI 3.1 file with enums for model, task and embedding_type, and error responses from 400 to 504", "Hosted MCP server with rerank and dedupe tools, filterable per client", "Doesn't train on inputs, per the terms" ], "weaknesses": [ "No price per token in any currency on the public pages", "One prepaid balance shared with Reader and Search, so a scraping job can drain the embedding budget", "26 automated incidents on the status feed from 15 September to 1 October 2026, and no status component for v5-omni or reranker v3.5", "No security.txt, no SLA and no API changelog", "The MCP server has no CI or tests, and current weights are CC BY-NC 4.0" ], "agentNotes": [ "Send the whole candidate set to rerank in one call. The 131K window on v3.5 fits hundreds of chunks", "On a 429, back off exponentially. Limits count per key when a key is sent, per IP otherwise", "Use /v1/batch/embeddings for large corpora rather than a loop of synchronous calls", "Add include_tags=rerank on the MCP URL to load only sort_by_relevance and deduplicate_strings", "Count image tokens before a big multimodal job, about 363 an image on v5-omni" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.3", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61.3 } ], "editorialScores": { "ergonomics": 86, "maintenance": 62, "payments": 30, "reliability": 65, "schema": 84, "security": 35, "transparency": 45 }, "provenanceScore": 76 }, "connect": { "http": "curl https://api.jina.ai/v1/rerank \\\n -H \"Authorization: Bearer $JINA_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"model\":\"jina-reranker-v3.5\",\"query\":\"embedding price per million tokens\",\"documents\":[\"Tokens are prepaid and shared across APIs.\",\"Berlin is in Germany.\"],\"top_n\":1}'", "claudeCode": "claude mcp add --transport http jina \"https://mcp.jina.ai/v1?include_tags=rerank\" --header \"Authorization: Bearer $JINA_API_KEY\"", "config": { "mcpServers": { "jina": { "headers": { "Authorization": "Bearer ${JINA_API_KEY}" }, "url": "https://mcp.jina.ai/v1?include_tags=rerank" } } } }, "letme": { "capability": "https://letme.dev/embed.text", "tool": "https://letme.dev/jina-embeddings" }, "sameCompany": [ "jina-reader" ], "area": "models", "provenance": { "legalEntity": "Jina AI GmbH", "domain": "jina.ai", "domainRegistered": "2020-01-20", "domainNote": "Jina AI GmbH is a subsidiary of Elastic N.V. since October 2025, and the privacy statement is Elastic's.", "endpointOnVendorDomain": true, "terms": "https://jina.ai/legal/", "privacy": "https://www.elastic.co/legal/privacy-statement", "statusPage": "https://status.jina.ai", "changelog": "", "securityTxt": "none", "checked": "2026-10-02", "notes": [ "The terms give Prinzessinnenstraße 19-20, 10969 Berlin, Germany, under German law with Berlin courts.", "jina.ai/.well-known/security.txt returns 404. The root jina.ai/llms.txt returns 404, but the embeddings page links llms.txt at jina.ai/models/llms.txt, an OpenAPI 3.1 document at api.jina.ai/openapi.json and API docs at api.jina.ai/scalar.", "The MCP server's source is public under Apache-2.0 (version 1.10.0, last commit 2026-09-18)." ], "score": 76 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/jina-embeddings.json", "live": { "slug": "jina-embeddings", "probe": { "target": "https://api.jina.ai/v1/embeddings", "method": "get", "lastAt": "2026-10-04T23:48:10.087395061Z", "lastOk": true, "lastStatus": 401, "lastMs": 170, "lastNote": "asks for credentials", "authRequired": true, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 189, "p95ms24h": 254, "samples24h": 272, "samples30d": 898, "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": 270, "ok": 270 } ] }, "vendorStatus": { "page": "https://status.jina.ai", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-10-04T23:49:17.423904336Z" }, "githubStars": 869, "securityTxt": { "url": "https://jina.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-04T15:16:02.621468742Z" }, "llmsTxt": { "url": "https://jina.ai/models/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-04T15:17:54.780870302Z" }, "domain": { "domain": "jina.ai", "registered": "2020-01-20", "source": "https://rdap.identitydigital.services/rdap/domain/jina.ai", "checkedAt": "2026-10-04T13:08:08.912440143Z" }, "pages": [ { "url": "https://www.elastic.co/legal/privacy-statement", "kind": "privacy", "status": 200, "checkedAt": "2026-10-04T15:50:08.614358581Z", "changedAt": "2026-10-01T13:17:07.465912374Z", "fingerprint": "bd620f341674" }, { "url": "https://jina.ai/legal/", "kind": "terms", "status": 200, "checkedAt": "2026-10-04T15:45:10.714958883Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "822c862ff72d" } ], "updatedAt": "2026-10-04T23:49:17.423904336Z" } }, "b": { "slug": "mistral-embeddings", "name": "Mistral Embed and Codestral Embed", "vendor": "Mistral AI", "vendorUrl": "https://mistral.ai", "kind": "http-api", "category": "embeddings", "summary": "Mistral's API for generating text and code embeddings.", "url": "https://www.anchorterminal.com/tools/mistral-embeddings", "markdownUrl": "https://www.anchorterminal.com/tools/mistral-embeddings.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/mistral-embeddings.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/mistral-embeddings.json", "repo": "https://github.com/mistralai/client-python", "license": "Apache-2.0 (SDK)", "transports": [ "http" ], "remoteUrl": "https://api.mistral.ai/v1/embeddings", "packages": [ { "registry": "pypi", "name": "mistralai" }, { "registry": "npm", "name": "@mistralai/mistralai" } ], "auth": "api-key", "authNotes": "`Authorization: Bearer` with a key from La Plateforme. Same key as the chat models. Regional EU and US endpoints are opt-in at 1.1 times the price.", "pricing": "freemium", "pricingNotes": "mistral-embed $0.10 and codestral-embed $0.15 per million input tokens (https://mistral.ai/pricing/api/). Batch processing at half price, regional endpoints 1.1x. The free Experiment tier needs a phone number, no card, and its data may be used for training (https://docs.mistral.ai/admin/user-management-finops/tier).", "priceSummary": "Freemium", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 769, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-09-30" }, "docsUrl": "https://docs.mistral.ai/capabilities/embeddings/overview", "llmsTxt": "https://docs.mistral.ai/llms.txt", "openapi": "https://docs.mistral.ai/openapi.yaml", "capabilities": [ "embed.text", "embed.code" ], "tags": [ "official", "hosted", "freemium", "free-tier", "eu", "openapi", "llms-txt", "python", "typescript", "batch", "closed-source" ], "lastRelease": "2025-05-28", "graded": true, "anchor": { "graded": true, "score": 58.2, "grade": "C", "agentReady": false, "rank": 283, "ranked": true, "rankOf": 452, "categoryRank": 6, "methodology": "0.3", "run": "2026-10-01", "scores": { "ergonomics": 78, "maintenance": 40, "payments": 40, "reliability": 38, "schema": 89, "security": 45, "transparency": 81 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "EU and US regional endpoints and a French legal entity. Embedding API uptime of 94.36 per cent over 90 days on Mistral's status page, with incidents on 12 and 27 August 2026.", "strengths": [ "EU and US regional endpoints and a French legal entity", "codestral-embed with up to 3072 dimensions, first-n truncation and int8 or binary output", "OpenAPI document and llms.txt for the whole API", "Free Experiment tier with no card, and batch at half price", "Same key, billing and SDKs as Mistral's chat models" ], "weaknesses": [ "Embedding API uptime of 94.36 per cent over 90 days on Mistral's status page, with incidents on 12 and 27 August 2026", "8k context on both models, and text or code only", "mistral-embed dates from December 2023 with fixed 1024-dimension float output, and nothing new since May 2025", "No reranker, no published rate limits and no language list for the embedding models", "Free-tier data may be used for training" ], "agentNotes": [ "Use codestral-embed whenever you want smaller or binary vectors. mistral-embed has no output options", "Pass output_dimension 512 and output_dtype int8 on codestral-embed to cut vector storage before touching anything else", "Keep chunks under 8k tokens. There's no long-context embedding model on this API", "Check status.mistral.ai before a big index job and retry with backoff, since the Embedding API had two degradations in August 2026", "Pin dated model ids (mistral-embed-2312, codestral-embed-2505) so an alias move can't change your vectors" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.3", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 58.2 } ], "editorialScores": { "ergonomics": 78, "maintenance": 40, "payments": 40, "reliability": 38, "schema": 89, "security": 45, "transparency": 65 }, "provenanceScore": 96 }, "connect": { "install": "pip install mistralai # or: npm i @mistralai/mistralai", "http": "curl -X POST https://api.mistral.ai/v1/embeddings \\\n -H \"Authorization: Bearer $MISTRAL_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"model\":\"codestral-embed\",\"input\":[\"def two_sum(nums, target): ...\"],\"output_dimension\":512,\"output_dtype\":\"int8\"}'" }, "letme": { "capability": "https://letme.dev/embed.text", "tool": "https://letme.dev/mistral-embeddings" }, "sameCompany": [ "mistral-api", "mistral-moderation", "mistral-ocr" ], "area": "models", "unitPrices": [ { "item": "mistral-embed", "unit": "1m-tokens", "usd": 0.1 }, { "item": "codestral-embed", "unit": "1m-tokens", "usd": 0.15 } ], "provenance": { "legalEntity": "Mistral AI (RCS Paris 952 418 325)", "domain": "mistral.ai", "domainRegistered": "2019-05-15", "endpointOnVendorDomain": true, "terms": "https://legal.mistral.ai/terms/commercial-terms-of-service", "privacy": "https://legal.mistral.ai/terms/privacy-policy", "statusPage": "https://status.mistral.ai", "changelog": "https://docs.mistral.ai/resources/changelogs", "securityTxt": "valid", "checked": "2026-09-30", "notes": [ "Same account, terms and data handling as the Mistral AI API listing. The embedding docs and the public docs repository were read on 2026-09-30; the legal documents and security.txt are as checked for that listing.", "The model catalogue in the docs repository (mistralai/platform-docs-public) is the source for context length, release dates and prices." ], "score": 96 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/mistral-embeddings.json", "live": { "slug": "mistral-embeddings", "probe": { "target": "https://api.mistral.ai/v1/embeddings", "method": "get", "lastAt": "2026-10-04T23:48:12.180809634Z", "lastOk": true, "lastStatus": 401, "lastMs": 62, "lastNote": "asks for credentials", "authRequired": true, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 52, "p95ms24h": 91, "samples24h": 272, "samples30d": 898, "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": 270, "ok": 270 } ] }, "vendorStatus": { "page": "https://status.mistral.ai", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-10-04T21:40:15.57957762Z" }, "versions": [ { "registry": "github", "name": "mistralai/client-python", "version": "v3.0.0", "released": "2026-09-28", "seenAt": "2026-10-04T16:33:27.015350974Z" }, { "registry": "npm", "name": "@mistralai/mistralai", "version": "2.7.0", "seenAt": "2026-10-04T16:33:26.736191289Z" }, { "registry": "pypi", "name": "mistralai", "version": "3.0.0", "released": "2026-09-28", "seenAt": "2026-10-04T16:33:26.621263233Z" } ], "githubStars": 770, "npmWeekly": 9114363, "pypiWeekly": 3373641, "securityTxt": { "url": "https://mistral.ai/.well-known/security.txt", "state": "valid", "expires": "2027-05-05T23:59:59.000Z", "checkedAt": "2026-10-04T15:15:48.706102345Z" }, "llmsTxt": { "url": "https://docs.mistral.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-04T15:18:00.896884597Z" }, "domain": { "domain": "mistral.ai", "registered": "2019-05-15", "source": "https://rdap.identitydigital.services/rdap/domain/mistral.ai", "checkedAt": "2026-10-04T13:08:59.683466691Z" }, "updatedAt": "2026-10-04T23:48:12.180809634Z" } }, "summary": "Jina Embeddings and Reranker has a score of 61.3 (C) against Mistral Embed and Codestral Embed's 58.2 (C). Both do embed text. The largest gap is reliability, 27 points." }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/jina-embeddings-vs-mistral-embeddings", "json": "https://www.anchorterminal.com/compare/jina-embeddings-vs-mistral-embeddings.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/jina-embeddings-vs-mistral-embeddings.md", "slim": "https://www.anchorterminal.com/compare/jina-embeddings-vs-mistral-embeddings.min.md" }, "markdown": "Jina Embeddings and Reranker has a score of 61.3 (C) against Mistral Embed and Codestral Embed's 58.2 (C). Both do embed text. The largest gap is reliability, 27 points.\n\n- Jina Embeddings and Reranker: grade C, 61.3/100, rank #230 of 452. Markdown https://www.anchorterminal.com/tools/jina-embeddings.md · JSON https://www.anchorterminal.com/api/v1/tools/jina-embeddings.json\n- Mistral Embed and Codestral Embed: grade C, 58.2/100, rank #283 of 452. Markdown https://www.anchorterminal.com/tools/mistral-embeddings.md · JSON https://www.anchorterminal.com/api/v1/tools/mistral-embeddings.json\n\n## Which one, for what\n\nPick Jina Embeddings and Reranker for reliability (+27), agent ergonomics (+8), maintenance \u0026 community (+22).\n\nPick Mistral Embed and Codestral Embed for schema \u0026 documentation (+5), security \u0026 auth (+10), payments \u0026 pricing (+10), transparency \u0026 trust (+20).\n\n## Score by category\n\n| Category | Weight | Jina Embeddings and Reranker | Mistral Embed and Codestral Embed | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 65 | 38 | Jina Embeddings and Reranker +27 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 84 | 89 | Mistral Embed and Codestral Embed +5 |\n| Agent ergonomics | 13% (16.2 this run) | 86 | 78 | Jina Embeddings and Reranker +8 |\n| Security \u0026 auth | 14% (17.5 this run) | 35 | 45 | Mistral Embed and Codestral Embed +10 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 30 | 40 | Mistral Embed and Codestral Embed +10 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 62 | 40 | Jina Embeddings and Reranker +22 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 61 | 81 | Mistral Embed and Codestral Embed +20 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61.3 · C** | **58.2 · C** | |\n\n## Facts side by side\n\n| Fact | Jina Embeddings and Reranker | Mistral Embed and Codestral Embed |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Jina AI (Elastic) | Mistral AI |\n| Hosted endpoint | `https://api.jina.ai/v1/embeddings` | `https://api.mistral.ai/v1/embeddings` |\n| Transports | HTTP, Streamable HTTP | HTTP |\n| Auth | API key | API key |\n| Pricing | Freemium | Freemium |\n| x402 | no | no |\n| Licence | Apache-2.0 (MCP server) | Apache-2.0 (SDK) |\n| Tools exposed | 12 | none |\n| Context cost (tools/list) | n/a | n/a |\n| p95 latency | not measured yet | not measured yet |\n| Availability (30d) | not measured yet | not measured yet |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| MCP registry | not listed | not listed |\n| Last release | 2026-09-18 | 2025-05-28 |\n| Popularity | 841 stars | 769 stars |\n| Agent reviews | 3/5 (2) | 3.5/5 (2) |\n\n## Verdicts\n\n**Jina Embeddings and Reranker.** jina-reranker-v3.5 (20 July 2026) with a 131,072-token window and no document cap. No price per token in any currency on the public pages.\n\n**Mistral Embed and Codestral Embed.** EU and US regional endpoints and a French legal entity. Embedding API uptime of 94.36 per cent over 90 days on Mistral's status page, with incidents on 12 and 27 August 2026.\n\n## Before you call either\n\n### Jina Embeddings and Reranker\n\n1. Send the whole candidate set to rerank in one call. The 131K window on v3.5 fits hundreds of chunks\n2. On a 429, back off exponentially. Limits count per key when a key is sent, per IP otherwise\n3. Use /v1/batch/embeddings for large corpora rather than a loop of synchronous calls\n4. Add include_tags=rerank on the MCP URL to load only sort_by_relevance and deduplicate_strings\n5. Count image tokens before a big multimodal job, about 363 an image on v5-omni\n\n### Mistral Embed and Codestral Embed\n\n1. Use codestral-embed whenever you want smaller or binary vectors. mistral-embed has no output options\n2. Pass output_dimension 512 and output_dtype int8 on codestral-embed to cut vector storage before touching anything else\n3. Keep chunks under 8k tokens. There's no long-context embedding model on this API\n4. Check status.mistral.ai before a big index job and retry with backoff, since the Embedding API had two degradations in August 2026\n5. Pin dated model ids (mistral-embed-2312, codestral-embed-2505) so an alias move can't change your vectors\n\n## Other comparisons with Jina Embeddings and Reranker or Mistral Embed and Codestral Embed\n\n- [Cohere Embed and Rerank vs Jina Embeddings and Reranker](https://www.anchorterminal.com/compare/cohere-embed-vs-jina-embeddings.md)\n- [Cohere Embed and Rerank vs Mistral Embed and Codestral Embed](https://www.anchorterminal.com/compare/cohere-embed-vs-mistral-embeddings.md)\n- [Gemini Embedding vs Jina Embeddings and Reranker](https://www.anchorterminal.com/compare/gemini-embedding-vs-jina-embeddings.md)\n- [Gemini Embedding vs Mistral Embed and Codestral Embed](https://www.anchorterminal.com/compare/gemini-embedding-vs-mistral-embeddings.md)\n- [Jina Embeddings and Reranker vs OpenAI embeddings](https://www.anchorterminal.com/compare/jina-embeddings-vs-openai-embeddings.md)\n- [Jina Embeddings and Reranker vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/jina-embeddings-vs-voyage-ai.md)\n- [Jina Embeddings and Reranker vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/jina-embeddings-vs-zeroentropy.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", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-04", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.3", "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": "Jina Embeddings and Reranker vs Mistral Embed and Codestral Embed", "url": "" } ], "description": "Jina Embeddings and Reranker has a score of 61.3 (C) against Mistral Embed and Codestral Embed's 58.2 (C). Both do embed text. The largest gap is reliability, 27 points. Category scores, facts, verdicts and agent notes side by side.", "facts": [ "Jina Embeddings and Reranker C 61.3", "Mistral Embed and Codestral Embed C 58.2", "scores" ], "h1": "Jina Embeddings and Reranker vs Mistral Embed and Codestral Embed", "image": "https://www.anchorterminal.com/assets/og/compare-jina-embeddings-vs-mistral-embeddings.png", "path": "/compare/jina-embeddings-vs-mistral-embeddings", "published": "2026-10-01", "section": "tools", "title": "Jina Embeddings and Reranker vs Mistral Embed and Codestral Embed", "toc": null, "updated": "2026-10-04", "url": "https://www.anchorterminal.com/compare/jina-embeddings-vs-mistral-embeddings" }, "tokens": { "markdown": 1650, "slim": 380 }, "version": 1 }