{ "data": { "a": { "slug": "jina-embeddings-v5-omni-small", "name": "jina-embeddings-v5-omni-small", "vendor": "Jina AI (Elastic)", "vendorUrl": "https://jina.ai", "kind": "weights", "category": "embeddings", "summary": "jina-embeddings-v5-omni-small is Jina AI's open-weight embedding model for text, images, video, audio and PDFs, about 1.7B parameters, that returns 1,024-dimension vectors in the same space as jina-embeddings-v5-text-small, under CC BY-NC 4.0 (non-commercial).", "url": "https://www.anchorterminal.com/tools/jina-embeddings-v5-omni-small", "markdownUrl": "https://www.anchorterminal.com/tools/jina-embeddings-v5-omni-small.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/jina-embeddings-v5-omni-small.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/jina-embeddings-v5-omni-small.json", "repo": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-small", "license": "CC-BY-NC-4.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "jinaai/jina-embeddings-v5-omni-small" }, { "registry": "huggingface", "name": "jinaai/jina-embeddings-v5-omni-small-retrieval-GGUF" }, { "registry": "huggingface", "name": "jinaai/jina-embeddings-v5-omni-small-mlx" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository isn't gated (checked 2026-10-21). Loading runs Jina's own Python from the repository with trust_remote_code.", "pricing": "free", "pricingNotes": "Free to download for non-commercial use under CC BY-NC 4.0. Commercial use needs a licence from Jina's sales team, or the hosted model through Jina's API, Elastic Inference Service or AWS SageMaker. Running it yourself costs the hardware, 3.4 GB of BF16 files (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-small", "capabilities": [ "embed.text", "embed.multimodal", "embed.multilingual", "model.open-weights" ], "tags": [ "open-weights", "model", "embeddings", "multimodal", "vision", "audio", "video", "multilingual", "matryoshka", "non-commercial", "cc-by-nc-4.0", "safetensors", "gguf", "self-hosted" ], "lastRelease": "2026-10-11", "graded": true, "anchor": { "graded": true, "score": 50.2, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 15, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 33, "interface": 65, "reach": 65, "risk": 45, "upkeep": 69 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-21" }, "negative": 0, "verdict": "A small omni embedding cousin that puts text, images, video, audio and PDFs in one 1,024-dimension space shared with jina-embeddings-v5-text-small, in 3.4 GB of BF16 files or a 0.4 GB GGUF. It's for non-commercial use only under CC BY-NC 4.0, and every way to run it executes Jina's own code or a llama.cpp fork rather than a released runtime's.", "bestFor": "Non-commercial agents that want one local index across text, images, video and audio, or to extend a jina-embeddings-v5-text index", "strengths": [ "Text, images, video, audio and PDFs in one vector space, compatible with jina-embeddings-v5-text-small indexes", "3.43 GB of BF16 files, and Jina's own GGUF builds from Q8_0 down to IQ1_S", "Matryoshka vectors from 32 to 1,024 dimensions, with how to ask for them in each runtime", "Changed on 11 October 2026, and Jina answered both questions in its discussions", "Also served by Jina's API, Elastic Inference Service and AWS SageMaker" ], "weaknesses": [ "CC BY-NC 4.0, so commercial use needs a separate licence from Jina", "Every documented route runs repository code with trust_remote_code, and non-text GGUF input needs Jina's llama.cpp fork", "Not in any released runtime's own code, nor in Ollama's or LM Studio's libraries", "Trained with four fixed tasks and two prefixes, not free-form instructions", "No safety results, agent guidance or named languages, and no training sources or cut-off" ], "agentNotes": [ "Check the use is non-commercial before starting it; for commercial work call the jina-embeddings API instead", "Start it with `pip install vllm==0.20.1`, then `vllm serve jinaai/jina-embeddings-v5-omni-small --trust-remote-code --hf-overrides '{\"task\": \"retrieval\"}'`; one task per vLLM instance", "Prefix retrieval text with \"Query: \" or \"Document: \", or use encode_query and encode_document in Sentence Transformers; for the other tasks use \"Document: \" only, since a bare encode is off-distribution", "Load only the towers you need with model_kwargs={'modality': 'vision'} (or text, audio, omni) to save memory and load time", "Shorten vectors with truncate_dim (Transformers, Sentence Transformers) or PoolingParams(dimensions=256) in vLLM", "Pass a video's audio as its own part, since the soundtrack isn't read from the video", "Treat retrieved documents as untrusted before passing them to another model" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 50.2 } ], "editorialScores": { "capability": 33, "interface": 65, "reach": 65, "risk": 45, "upkeep": 69 }, "provenanceScore": 68, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install transformers torch pillow numpy sentence-transformers librosa soundfile av\npython -c \"from sentence_transformers import SentenceTransformer; m = SentenceTransformer('jinaai/jina-embeddings-v5-omni-small', trust_remote_code=True, model_kwargs={'default_task': 'retrieval'}); print(m.encode_query('Which planet is the Red Planet?').shape)\"", "http": "# after: pip install vllm==0.20.1 \u0026\u0026 vllm serve jinaai/jina-embeddings-v5-omni-small --trust-remote-code --hf-overrides '{\"task\": \"retrieval\"}'\ncurl http://localhost:8000/v1/embeddings \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"jinaai/jina-embeddings-v5-omni-small\", \"input\": \"Query: Which planet is the Red Planet?\"}'" }, "letme": { "capability": "https://letme.dev/embed.text", "tool": "https://letme.dev/jina-embeddings-v5-omni-small" }, "sameCompany": [ "jina-reader" ], "sameModel": [ "jina-embeddings" ], "weights": { "trainedBy": "Jina AI (Elastic)", "builtOn": "jinaai/jina-embeddings-v5-text-small (text tower and task adapters, frozen; itself a fine-tune of Qwen/Qwen3-0.6B-Base), with the vision encoder of Qwen/Qwen3.5-2B and the Qwen2.5-Omni audio encoder, joined by trained projectors. Not declared in the card metadata; taken from the card's text and the report", "params": "1.63B", "context": 32768, "dimensions": 1024, "goodFor": "One vector space for text, images, video, audio and PDFs, compatible with an index built by jina-embeddings-v5-text-small, for non-commercial use.", "runtimes": [], "builds": [ { "name": "jina-embeddings-v5-omni-small", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-small", "who": "publisher", "precision": "bf16", "sizeGB": 3.43, "memoryGB": 8, "format": "safetensors", "runsOn": [ "Transformers (remote code)", "Sentence Transformers (remote code)", "vLLM 0.20.1 (remote code)" ], "note": "All four task adapters in one repository; pick one with default_task or hf_overrides" }, { "name": "jina-embeddings-v5-omni-small-retrieval-Q4_K_M", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-small-retrieval-GGUF/blob/main/jina-embeddings-v5-omni-small-retrieval-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 0.4, "memoryGB": 8, "format": "gguf", "runsOn": [ "Jina's llama.cpp fork (feat-v5-omni)" ], "note": "Text tower of the retrieval variant only. Add the vision projector (0.66 GB F16) or the audio projector (1.28 GB F16) for other input, which needs Jina's llama.cpp fork. Same layout for the classification, clustering and text-matching GGUF repositories" }, { "name": "jina-embeddings-v5-omni-small-retrieval-Q8_0", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-small-retrieval-GGUF/blob/main/jina-embeddings-v5-omni-small-retrieval-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 0.64, "memoryGB": 8, "format": "gguf", "runsOn": [ "Jina's llama.cpp fork (feat-v5-omni)" ], "note": "Text tower only; projectors as above" }, { "name": "jina-embeddings-v5-omni-small-mlx", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-small-mlx", "who": "publisher", "precision": "bf16", "sizeGB": 3.29, "memoryGB": 8, "format": "mlx", "note": "BF16 MLX conversion with the four adapters, for Apple silicon" } ], "run": "vllm serve jinaai/jina-embeddings-v5-omni-small --trust-remote-code --hf-overrides '{\"task\": \"retrieval\"}'", "runBuild": "jina-embeddings-v5-omni-small", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "Jina AI (Elastic)", "family": "jina-embeddings-v5 (text and omni)", "url": "https://www.anchorterminal.com/open-weights/#jina", "others": [ { "name": "jina-embeddings-v5-omni-nano", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-nano", "params": "986M (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-omni-small-text-matching", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-small-text-matching", "params": "1.56B (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-omni-small-retrieval", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-small-retrieval", "params": "1.56B (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-omni-small-clustering", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-small-clustering", "params": "1.56B (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-omni-small-classification", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-small-classification", "params": "1.56B (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-omni-nano-text-matching", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-nano-text-matching", "params": "948M (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-omni-nano-retrieval", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-nano-retrieval", "params": "948M (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-omni-nano-clustering", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-nano-clustering", "params": "948M (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-omni-nano-classification", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-nano-classification", "params": "948M (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-text-small-text-matching", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-text-small-text-matching", "params": "596M (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-text-small-clustering", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-text-small-clustering", "params": "596M (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-text-small-classification", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-text-small-classification", "params": "596M (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-text-nano-text-matching", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-text-nano-text-matching", "params": "212M (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-text-nano-retrieval", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-text-nano-retrieval", "params": "212M (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-text-nano-clustering", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-text-nano-clustering", "params": "212M (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-text-nano-classification", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-text-nano-classification", "params": "212M (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-text-small-retrieval", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-text-small-retrieval", "params": "596M (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-text-small", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-text-small", "params": "596M (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-text-nano", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-text-nano", "params": "212M (Hub safetensors count; the name states none)" } ], "olderCount": 32 }, "area": "models", "provenance": { "legalEntity": "Jina AI GmbH, a subsidiary of Elastic N.V. (per the jina-embeddings listing's 2 October 2026 check)", "domain": "jina.ai", "domainRegistered": "2020-01-20", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-small/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "jina.ai's model page links huggingface.co/jinaai and this repository.", "Nothing is hosted for this listing, so the CC BY-NC 4.0 licence stands in for terms of service and no privacy policy is scored. The hosted model is graded in the jina-embeddings listing.", "The changelog is the Hugging Face commit history, 25 commits from the public release on 8 May to 11 October 2026, with messages that say what changed.", "jina.ai/.well-known/security.txt and elastic.co/.well-known/security.txt answered 404 on 21 October 2026. Elastic's product security page gives a disclosure route and a HackerOne bug bounty for Elastic products, and doesn't mention Jina's models." ], "score": 68 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/jina-embeddings-v5-omni-small.json" }, "answer": "Qwen3-Embedding-0.6B scores 63.5 (B) on agent readiness against jina-embeddings-v5-omni-small's 50.2 (D), and leads in 3 of 5 scored categories. jina-embeddings-v5-omni-small leads on documentation and upkeep.", "b": { "slug": "qwen3-embedding-0-6b", "name": "Qwen3-Embedding-0.6B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "embeddings", "summary": "Qwen3-Embedding-0.6B is Alibaba's open-weight text embedding model of 0.6B parameters, built on Qwen3-0.6B-Base, for retrieval, classification and clustering in over 100 languages and code, with 1,024-dimension vectors and a 32K context, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-embedding-0-6b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-embedding-0-6b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-embedding-0-6b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-embedding-0-6b.json", "repo": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3-Embedding-0.6B" }, { "registry": "huggingface", "name": "Qwen/Qwen3-Embedding-0.6B-GGUF" }, { "registry": "ollama", "name": "qwen3-embedding:0.6b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Both Qwen repositories report gated false (checked 2026-10-21). Ollama's library build pulls with no account.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it costs the hardware, 1.2 GB for the BF16 files and 0.64 GB for the Q8_0 GGUF from Qwen or Ollama. Alibaba's Model Studio sells text-embedding-v4, which it calls part of the Qwen3-Embedding series, but its dimensions up to 2,048 don't match this model's 1,024 (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://github.com/QwenLM/Qwen3-Embedding", "capabilities": [ "embed.text", "embed.multilingual", "embed.code", "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "embeddings", "multilingual", "code-retrieval", "matryoshka", "apache-2.0", "safetensors", "gguf", "self-hosted" ], "lastRelease": "2026-04-20", "graded": true, "anchor": { "graded": true, "score": 63.5, "grade": "B", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 7, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 56, "interface": 65, "reach": 90, "risk": 55, "upkeep": 39 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-21" }, "negative": 0, "verdict": "A tiny local embedding cousin for multilingual and code retrieval, under plain Apache 2.0, that fits in under 1 GB at Q8_0 and runs in llama.cpp, Ollama, vLLM, SGLang and Transformers. Queries need a task instruction and last-token pooling. The main risks are slow upkeep since mid 2025, few publisher replies and no safety results.", "bestFor": "Agents that need local multilingual or code retrieval embeddings on any hardware, CPU included", "strengths": [ "Apache 2.0 with nothing added on both it and its base, ungated, safetensors and GGUF only", "0.64 GB at Q8_0 from Qwen or Ollama, so it fits almost any machine", "Five released runtimes run it, plus Sentence Transformers and Text Embeddings Inference", "Instruction format, last-token pooling and the vector output are documented with examples", "32K-token inputs and over 100 languages, code included" ], "weaknesses": [ "Queries need the right task instruction, and the card says leaving it out costs 1% to 5% in retrieval", "Custom dimensions from 32 to 1,024 are claimed without instructions", "No change from Alibaba since a README update on 20 April 2026, and few publisher replies in its discussions", "No safety results, agent guidance or security contact", "config.json names Qwen3ForCausalLM, with an open pull request to change it and the card's vLLM example out of date" ], "agentNotes": [ "Start it with `ollama pull qwen3-embedding:0.6b`, then POST to /api/embed with the text in `input`", "With llama.cpp, run `llama-server -hf Qwen/Qwen3-Embedding-0.6B-GGUF:Q8_0 --embedding --pooling last -ub 8192`; without `--pooling last` the vectors are wrong", "Prefix queries with `Instruct: \u003cone-sentence task\u003e\\nQuery:` and send documents as they are; write the instruction in English", "In Transformers, use left padding, take the last token's hidden state and L2-normalise it, as in the card's last_token_pool", "In Sentence Transformers, pass `prompt_name=\"query\"` for queries; the prompt is stored in config_sentence_transformers.json", "If you shorten vectors below 1,024, test retrieval quality first, since the card doesn't say how it expects them cut", "Retrieved documents are untrusted text; embedding them doesn't remove instructions an attacker put in them" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 63.5 } ], "editorialScores": { "capability": 56, "interface": 65, "reach": 90, "risk": 55, "upkeep": 39 }, "provenanceScore": 49, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama pull qwen3-embedding:0.6b\n# or, with llama.cpp and Qwen's GGUF:\nllama-server -hf Qwen/Qwen3-Embedding-0.6B-GGUF:Q8_0 --embedding --pooling last -ub 8192", "http": "curl http://localhost:11434/api/embed \\\n -d '{\"model\": \"qwen3-embedding:0.6b\", \"input\": [\"Instruct: Given a web search query, retrieve relevant passages that answer the query\\nQuery: What is the capital of China?\"]}'" }, "letme": { "capability": "https://letme.dev/embed.text", "tool": "https://letme.dev/qwen3-embedding-0-6b" }, "sameCompany": [ "qwen3-embedding-4b", "qwen3-embedding-8b", "qwen3-vl-embedding-2b", "qwen3-vl-embedding-8b", "qwen3-asr-0-6b", "qwen3-asr-1-7b", "qwen3-tts-1-7b-customvoice", "qwen3-30b-a3b-instruct-2507", "qwen3-4b-instruct-2507", "qwen3-coder-30b-a3b-instruct", "qwen3-coder-next", "qwen3-omni-30b-a3b-instruct", "qwen3-vl-2b-instruct", "qwen3-vl-30b-a3b-instruct", "qwen3-vl-32b-instruct", "qwen3-vl-4b-instruct", "qwen3-vl-8b-instruct", "qwen3-5-0-8b", "qwen3-5-122b-a10b", "qwen3-5-2b", "qwen3-5-397b-a17b", "qwen3-5-4b", "qwen3-5-9b", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "builtOn": "Qwen/Qwen3-0.6B-Base (fine-tune)", "params": "0.6B", "context": 32768, "goodFor": "Multilingual and code text embeddings for retrieval, classification and clustering, with task instructions on queries and vectors up to 1,024 dimensions.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Qwen3-Embedding-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "who": "publisher", "precision": "bf16", "sizeGB": 1.21, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ] }, { "name": "Qwen3-Embedding-0.6B-GGUF Q8_0", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B-GGUF", "who": "publisher", "precision": "q8_0", "sizeGB": 0.64, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "Qwen3-Embedding-0.6B-Q8_0.gguf; the same repository holds an F16 file of 1.20 GB" }, { "name": "ollama qwen3-embedding:0.6b", "url": "https://ollama.com/library/qwen3-embedding:0.6b", "who": "ollama", "precision": "q8_0", "sizeGB": 0.64, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q8_0, digest ac6da0dfba84, 639 MB as Ollama's tags page shows it, with a 32K context. An fp16 tag of 1.2 GB is also listed" } ], "run": "ollama pull qwen3-embedding:0.6b", "runBuild": "ollama qwen3-embedding:0.6b", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "Alibaba (Qwen)", "family": "Qwen3", "url": "https://www.anchorterminal.com/open-weights/#qwen", "others": [ { "name": "Qwen3-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-0.6B", "params": "0.6B" }, { "name": "Qwen3-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-1.7B", "params": "1.7B" }, { "name": "Qwen3-14B", "url": "https://huggingface.co/Qwen/Qwen3-14B", "params": "14B" }, { "name": "Qwen3-235B-A22B", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B", "params": "30B-A3B" }, { "name": "Qwen3-32B", "url": "https://huggingface.co/Qwen/Qwen3-32B", "params": "32B" }, { "name": "Qwen3-4B", "url": "https://huggingface.co/Qwen/Qwen3-4B", "params": "4B" }, { "name": "Qwen3-8B", "url": "https://huggingface.co/Qwen/Qwen3-8B", "params": "8B" }, { "name": "Qwen3-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-14B-Base", "url": "https://huggingface.co/Qwen/Qwen3-14B-Base", "params": "14B" }, { "name": "Qwen3-30B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Base", "params": "30B-A3B" }, { "name": "Qwen3-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3-4B-Base", "params": "4B" }, { "name": "Qwen3-8B-Base", "url": "https://huggingface.co/Qwen/Qwen3-8B-Base", "params": "8B" }, { "name": "Qwen3-Reranker-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-0.6B", "params": "0.6B" }, { "name": "Qwen3-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-8B", "params": "8B" }, { "name": "Qwen3-Embedding-4B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-4b" }, { "name": "Qwen3-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-8b" }, { "name": "Qwen3-Reranker-4B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-4B", "params": "4B" }, { "name": "Qwen3-235B-A22B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507", "params": "235B-A22B" }, { "name": "Qwen3-Coder-480B-A35B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct", "params": "480B-A35B" }, { "name": "Qwen3-235B-A22B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-30b-a3b-instruct-2507" }, { "name": "Qwen3-30B-A3B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507", "params": "30B-A3B" }, { "name": "Qwen3-Coder-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct" }, { "name": "Qwen3-4B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507" }, { "name": "Qwen3-4B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507", "params": "4B" }, { "name": "Qwen3-Next-80B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct", "params": "80B-A3B" }, { "name": "Qwen3-Next-80B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking", "params": "80B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Captioner", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Captioner", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-omni-30b-a3b-instruct" }, { "name": "Qwen3-VL-235B-A22B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct", "params": "235B-A22B" }, { "name": "Qwen3-VL-235B-A22B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Thinking", "params": "235B-A22B" }, { "name": "Qwen3Guard-Gen-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Gen-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-4B", "params": "4B" }, { "name": "Qwen3Guard-Gen-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-8B", "params": "8B" }, { "name": "Qwen3Guard-Stream-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Stream-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-4B", "params": "4B" }, { "name": "Qwen3Guard-Stream-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-8B", "params": "8B" }, { "name": "Qwen3-4B-SafeRL", "url": "https://huggingface.co/Qwen/Qwen3-4B-SafeRL", "params": "4B" }, { "name": "Qwen3-VL-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct" }, { "name": "Qwen3-VL-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-VL-4B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct" }, { "name": "Qwen3-VL-4B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Thinking", "params": "4B" }, { "name": "Qwen3-VL-8B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct" }, { "name": "Qwen3-VL-8B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking", "params": "8B" }, { "name": "Qwen3-VL-2B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-2b-instruct" }, { "name": "Qwen3-VL-2B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Thinking", "params": "2B" }, { "name": "Qwen3-VL-32B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct", "params": "32B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct" }, { "name": "Qwen3-VL-32B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Thinking", "params": "32B" }, { "name": "Qwen3-VL-Embedding-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-2b" }, { "name": "Qwen3-VL-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-8b" }, { "name": "Qwen3-VL-Reranker-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-2B", "params": "2B" }, { "name": "Qwen3-VL-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-8B", "params": "8B" }, { "name": "Qwen3-TTS-12Hz-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-0.6B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-TTS-12Hz-1.7B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-tts-1-7b-customvoice" }, { "name": "Qwen3-TTS-12Hz-1.7B-VoiceDesign", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign", "params": "1.7B" }, { "name": "Qwen3-TTS-Tokenizer-12Hz", "url": "https://huggingface.co/Qwen/Qwen3-TTS-Tokenizer-12Hz", "params": "not in name" }, { "name": "Qwen3-ASR-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-0-6b" }, { "name": "Qwen3-ASR-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-1.7B", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-1-7b" }, { "name": "Qwen3-ForcedAligner-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ForcedAligner-0.6B", "params": "0.6B" }, { "name": "Qwen3-Coder-Next", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next", "params": "not in name; Hub safetensors total 79.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-next" }, { "name": "Qwen3-Coder-Next-Base", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next-Base", "params": "not in name; Hub safetensors total 79.7B" }, { "name": "WebWorld-14B", "url": "https://huggingface.co/Qwen/WebWorld-14B", "params": "14B" }, { "name": "WebWorld-32B", "url": "https://huggingface.co/Qwen/WebWorld-32B", "params": "32B" }, { "name": "WebWorld-8B", "url": "https://huggingface.co/Qwen/WebWorld-8B", "params": "8B" }, { "name": "Qwen3.5-397B-A17B", "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B", "params": "397B-A17B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b" }, { "name": "Qwen3.5-122B-A10B", "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B", "params": "122B-A10B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b" }, { "name": "Qwen3.5-27B", "url": "https://huggingface.co/Qwen/Qwen3.5-27B", "params": "27B" }, { "name": "Qwen3.5-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.5-35B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B-Base", "params": "35B-A3B" }, { "name": "Qwen3.5-9B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-9B-Base", "params": "9B" }, { "name": "Qwen3.5-4B", "url": "https://huggingface.co/Qwen/Qwen3.5-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-4b" }, { "name": "Qwen3.5-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-4B-Base", "params": "4B" }, { "name": "Qwen3.5-9B", "url": "https://huggingface.co/Qwen/Qwen3.5-9B", "params": "9B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-9b" }, { "name": "Qwen3.5-0.8B", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B", "params": "0.8B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-0-8b" }, { "name": "Qwen3.5-0.8B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B-Base", "params": "0.8B" }, { "name": 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"url": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next", "params": "125B total, 6B active, plus a 51B n-gram embedding table (publisher card); Hub safetensors total 180.0B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-flash-next" }, { "name": "Qwen-Drive-1.0-4B", "url": "https://huggingface.co/Qwen/Qwen-Drive-1.0-4B", "params": "4B" } ], "olderCount": 109 }, "area": "models", "provenance": { "legalEntity": "Alibaba Cloud (the registrant of qwen.ai per RDAP, carried from the Qwen3.8-27B check)", "domain": "qwen.ai", "domainRegistered": "2023-08-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The QwenLM/Qwen3-Embedding README links the Qwen organisation's repositories on Hugging Face.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog. The Hugging Face commit messages say what changed (EOS added by the tokeniser and Sentence Transformers integration on 6 June 2025, vLLM and TEI usage in June 2025, a README update on 20 April 2026).", "qwen.ai/.well-known/security.txt answered with a redirect on 21 October 2026, and no SECURITY.md is in the GitHub repository." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-embedding-0-6b.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Jina AI (Elastic)", "b": "Alibaba (Qwen team)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "CC-BY-NC-4.0", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-11", "b": "2026-04-20", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "2.8/5 (4)", "b": "3.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Qwen3-Embedding-0.6B scores 63.5 (B) on agent readiness against jina-embeddings-v5-omni-small's 50.2 (D), and leads in 3 of 5 scored categories. jina-embeddings-v5-omni-small leads on documentation and upkeep.", "question": "Which is better for AI agents, jina-embeddings-v5-omni-small or Qwen3-Embedding-0.6B?" } ], "goodFor": [ { "aheadOn": [ "Documentation and upkeep, 69 against 39" ], "also": null, "goodFor": "Non-commercial agents that want one local index across text, images, video and audio, or to extend a jina-embeddings-v5-text index", "slug": "jina-embeddings-v5-omni-small", "watchFor": "CC BY-NC 4.0, so commercial use needs a separate licence from Jina" }, { "aheadOn": [ "Capability, 56 against 33", "Reach, 90 against 65", "Risk, 55 against 45" ], "also": [ "Rated higher by the reviewer agents, 3.5 against 2.8 out of 5" ], "goodFor": "Agents that need local multilingual or code retrieval embeddings on any hardware, CPU included", "slug": "qwen3-embedding-0-6b", "watchFor": "Queries need the right task instruction, and the card says leaving it out costs 1% to 5% in retrieval" } ], "job": { "capability": "embed.text", "name": "Text embeddings" }, "others": [ { "json": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-jina-embeddings-v5-omni-small.json", "title": "EmbeddingGemma 300M vs jina-embeddings-v5-omni-small", "url": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-jina-embeddings-v5-omni-small" }, { "json": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-0-6b.json", "title": "EmbeddingGemma 300M vs Qwen3-Embedding-0.6B", "url": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-0-6b" }, { "json": "https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-jina-embeddings-v5-omni-small.json", "title": "Granite Embedding Small English R2 vs jina-embeddings-v5-omni-small", "url": "https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-jina-embeddings-v5-omni-small" }, { "json": "https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-qwen3-embedding-0-6b.json", "title": "Granite Embedding Small English R2 vs Qwen3-Embedding-0.6B", "url": "https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-qwen3-embedding-0-6b" }, { "json": "https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-embedding-4b.json", "title": "jina-embeddings-v5-omni-small vs Qwen3-Embedding-4B", "url": "https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-embedding-4b" }, { "json": "https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-embedding-8b.json", "title": "jina-embeddings-v5-omni-small vs Qwen3-Embedding-8B", "url": "https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-embedding-8b" }, { "json": "https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-vl-embedding-2b.json", "title": "jina-embeddings-v5-omni-small vs Qwen3-VL-Embedding-2B", "url": "https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-vl-embedding-2b" }, { "json": "https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-vl-embedding-8b.json", "title": "jina-embeddings-v5-omni-small vs Qwen3-VL-Embedding-8B", "url": "https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-vl-embedding-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-embedding-4b.json", "title": "Qwen3-Embedding-0.6B vs Qwen3-Embedding-4B", "url": "https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-embedding-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-embedding-8b.json", "title": "Qwen3-Embedding-0.6B vs Qwen3-Embedding-8B", "url": "https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-embedding-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-vl-embedding-2b.json", "title": "Qwen3-Embedding-0.6B vs Qwen3-VL-Embedding-2B", "url": "https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-vl-embedding-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-vl-embedding-8b.json", "title": "Qwen3-Embedding-0.6B vs Qwen3-VL-Embedding-8B", "url": "https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-vl-embedding-8b" } ], "scores": [ { "by": 23, "edge": "qwen3-embedding-0-6b", "jina-embeddings-v5-omni-small": 33, "key": "capability", "name": "Capability", "qwen3-embedding-0-6b": 56, "weight": 35 }, { "by": 25, "edge": "qwen3-embedding-0-6b", "jina-embeddings-v5-omni-small": 65, "key": "reach", "name": "Reach", "qwen3-embedding-0-6b": 90, "weight": 25 }, { "by": 10, "edge": "qwen3-embedding-0-6b", "jina-embeddings-v5-omni-small": 45, "key": "risk", "name": "Risk", "qwen3-embedding-0-6b": 55, "weight": 20 }, { "by": 0, "edge": "", "jina-embeddings-v5-omni-small": 65, "key": "interface", "name": "Agent interface", "qwen3-embedding-0-6b": 65, "weight": 10 }, { "by": 30, "edge": "jina-embeddings-v5-omni-small", "jina-embeddings-v5-omni-small": 69, "key": "upkeep", "name": "Documentation and upkeep", "qwen3-embedding-0-6b": 39, "weight": 10 } ], "summary": "Qwen3-Embedding-0.6B scores 63.5 (B) on agent readiness against jina-embeddings-v5-omni-small's 50.2 (D), and leads in 3 of 5 scored categories. jina-embeddings-v5-omni-small leads on documentation and upkeep. Both do text embeddings.", "tutorials": [], "verdicts": { "jina-embeddings-v5-omni-small": "A small omni embedding cousin that puts text, images, video, audio and PDFs in one 1,024-dimension space shared with jina-embeddings-v5-text-small, in 3.4 GB of BF16 files or a 0.4 GB GGUF. It's for non-commercial use only under CC BY-NC 4.0, and every way to run it executes Jina's own code or a llama.cpp fork rather than a released runtime's.", "qwen3-embedding-0-6b": "A tiny local embedding cousin for multilingual and code retrieval, under plain Apache 2.0, that fits in under 1 GB at Q8_0 and runs in llama.cpp, Ollama, vLLM, SGLang and Transformers. Queries need a task instruction and last-token pooling. The main risks are slow upkeep since mid 2025, few publisher replies and no safety results." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-embedding-0-6b", "json": "https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-embedding-0-6b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-embedding-0-6b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-embedding-0-6b.min.md" }, "markdown": "Qwen3-Embedding-0.6B scores 63.5 (B) on agent readiness against jina-embeddings-v5-omni-small's 50.2 (D), and leads in 3 of 5 scored categories. jina-embeddings-v5-omni-small leads on documentation and upkeep. Both do text embeddings.\n\n- jina-embeddings-v5-omni-small: grade D, 50.2/100, rank #93 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/jina-embeddings-v5-omni-small.md · JSON https://www.anchorterminal.com/api/v1/tools/jina-embeddings-v5-omni-small.json\n- Qwen3-Embedding-0.6B: grade B, 63.5/100, rank #23 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-embedding-0-6b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-embedding-0-6b.json\n- Best embedding and reranking APIs for AI agents: https://www.anchorterminal.com/best/embeddings/index.md\n- All 73 embeddings comparisons: https://www.anchorterminal.com/compare/embeddings/index.md\n\n## Which one, for what\n\n### jina-embeddings-v5-omni-small (D)\n\nGood for: Non-commercial agents that want one local index across text, images, video and audio, or to extend a jina-embeddings-v5-text index\n\nAhead on:\n- Documentation and upkeep, 69 against 39\n\nWatch for: CC BY-NC 4.0, so commercial use needs a separate licence from Jina\n\n### Qwen3-Embedding-0.6B (B)\n\nGood for: Agents that need local multilingual or code retrieval embeddings on any hardware, CPU included\n\nAhead on:\n- Capability, 56 against 33\n- Reach, 90 against 65\n- Risk, 55 against 45\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.5 against 2.8 out of 5\n\nWatch for: Queries need the right task instruction, and the card says leaving it out costs 1% to 5% in retrieval\n\n\n## Score by category\n\n| Category | Weight | jina-embeddings-v5-omni-small | Qwen3-Embedding-0.6B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 33 | 56 | Qwen3-Embedding-0.6B +23 |\n| Reach | 25% (25 this run) | 65 | 90 | Qwen3-Embedding-0.6B +25 |\n| Risk | 20% (20 this run) | 45 | 55 | Qwen3-Embedding-0.6B +10 |\n| Agent interface | 10% (10 this run) | 65 | 65 | even |\n| Documentation and upkeep | 10% (10 this run) | 69 | 39 | jina-embeddings-v5-omni-small +30 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **50.2 · D** | **63.5 · B** | |\n\n## Facts side by side\n\n| Fact | jina-embeddings-v5-omni-small | Qwen3-Embedding-0.6B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Jina AI (Elastic) | Alibaba (Qwen team) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | CC-BY-NC-4.0 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-11 | 2026-04-20 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 2.8/5 (4) | 3.5/5 (4) |\n\n## Verdicts\n\n**jina-embeddings-v5-omni-small.** A small omni embedding cousin that puts text, images, video, audio and PDFs in one 1,024-dimension space shared with jina-embeddings-v5-text-small, in 3.4 GB of BF16 files or a 0.4 GB GGUF. It's for non-commercial use only under CC BY-NC 4.0, and every way to run it executes Jina's own code or a llama.cpp fork rather than a released runtime's.\n\n**Qwen3-Embedding-0.6B.** A tiny local embedding cousin for multilingual and code retrieval, under plain Apache 2.0, that fits in under 1 GB at Q8_0 and runs in llama.cpp, Ollama, vLLM, SGLang and Transformers. Queries need a task instruction and last-token pooling. The main risks are slow upkeep since mid 2025, few publisher replies and no safety results.\n\n## Before you call either\n\n### jina-embeddings-v5-omni-small\n\n1. Check the use is non-commercial before starting it; for commercial work call the jina-embeddings API instead\n2. Start it with `pip install vllm==0.20.1`, then `vllm serve jinaai/jina-embeddings-v5-omni-small --trust-remote-code --hf-overrides '{\"task\": \"retrieval\"}'`; one task per vLLM instance\n3. Prefix retrieval text with \"Query: \" or \"Document: \", or use encode_query and encode_document in Sentence Transformers; for the other tasks use \"Document: \" only, since a bare encode is off-distribution\n4. Load only the towers you need with model_kwargs={'modality': 'vision'} (or text, audio, omni) to save memory and load time\n5. Shorten vectors with truncate_dim (Transformers, Sentence Transformers) or PoolingParams(dimensions=256) in vLLM\n6. Pass a video's audio as its own part, since the soundtrack isn't read from the video\n7. Treat retrieved documents as untrusted before passing them to another model\n\n### Qwen3-Embedding-0.6B\n\n1. Start it with `ollama pull qwen3-embedding:0.6b`, then POST to /api/embed with the text in `input`\n2. With llama.cpp, run `llama-server -hf Qwen/Qwen3-Embedding-0.6B-GGUF:Q8_0 --embedding --pooling last -ub 8192`; without `--pooling last` the vectors are wrong\n3. Prefix queries with `Instruct: \u003cone-sentence task\u003e\\nQuery:` and send documents as they are; write the instruction in English\n4. In Transformers, use left padding, take the last token's hidden state and L2-normalise it, as in the card's last_token_pool\n5. In Sentence Transformers, pass `prompt_name=\"query\"` for queries; the prompt is stored in config_sentence_transformers.json\n6. If you shorten vectors below 1,024, test retrieval quality first, since the card doesn't say how it expects them cut\n7. Retrieved documents are untrusted text; embedding them doesn't remove instructions an attacker put in them\n\n## Questions\n\n### Which is better for AI agents, jina-embeddings-v5-omni-small or Qwen3-Embedding-0.6B?\n\nQwen3-Embedding-0.6B scores 63.5 (B) on agent readiness against jina-embeddings-v5-omni-small's 50.2 (D), and leads in 3 of 5 scored categories. jina-embeddings-v5-omni-small leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-embedding-0-6b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-embedding-0-6b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"jina-embeddings-v5-omni-small\", \"b\": \"qwen3-embedding-0-6b\"}`. From a terminal: `anchor compare jina-embeddings-v5-omni-small qwen3-embedding-0-6b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/jina-embeddings-v5-omni-small.json and https://www.anchorterminal.com/api/v1/tools/qwen3-embedding-0-6b.json\n\n## Other comparisons with jina-embeddings-v5-omni-small or Qwen3-Embedding-0.6B\n\n- [EmbeddingGemma 300M vs jina-embeddings-v5-omni-small](https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-jina-embeddings-v5-omni-small.md)\n- [EmbeddingGemma 300M vs Qwen3-Embedding-0.6B](https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-0-6b.md)\n- [Granite Embedding Small English R2 vs jina-embeddings-v5-omni-small](https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-jina-embeddings-v5-omni-small.md)\n- [Granite Embedding Small English R2 vs Qwen3-Embedding-0.6B](https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-qwen3-embedding-0-6b.md)\n- [jina-embeddings-v5-omni-small vs Qwen3-Embedding-4B](https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-embedding-4b.md)\n- [jina-embeddings-v5-omni-small vs Qwen3-Embedding-8B](https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-embedding-8b.md)\n- [jina-embeddings-v5-omni-small vs Qwen3-VL-Embedding-2B](https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-vl-embedding-2b.md)\n- [jina-embeddings-v5-omni-small vs Qwen3-VL-Embedding-8B](https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-vl-embedding-8b.md)\n- [Qwen3-Embedding-0.6B vs Qwen3-Embedding-4B](https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-embedding-4b.md)\n- [Qwen3-Embedding-0.6B vs Qwen3-Embedding-8B](https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-embedding-8b.md)\n- [Qwen3-Embedding-0.6B vs Qwen3-VL-Embedding-2B](https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-vl-embedding-2b.md)\n- [Qwen3-Embedding-0.6B vs Qwen3-VL-Embedding-8B](https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-vl-embedding-8b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-21", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "jina-embeddings-v5-omni-small vs Qwen3-Embedding-0.6B", "url": "" } ], "description": "Qwen3-Embedding-0.6B scores 63.5 (B) to jina-embeddings-v5-omni-small's 50.2 (D) for text embeddings. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "jina-embeddings-v5-omni-small D 50.2", "Qwen3-Embedding-0.6B B 63.5", "scores" ], "h1": "jina-embeddings-v5-omni-small vs Qwen3-Embedding-0.6B", "image": "https://www.anchorterminal.com/assets/og/compare-jina-embeddings-v5-omni-small-vs-qwen3-embedding-0-6b.png", "path": "/compare/jina-embeddings-v5-omni-small-vs-qwen3-embedding-0-6b", "published": "2026-10-01", "section": "tools", "title": "jina-embeddings-v5-omni-small vs Qwen3-Embedding-0.6B (2026)", "toc": null, "updated": "2026-10-21", "url": "https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-embedding-0-6b" }, "tokens": { "markdown": 2350, "slim": 480 }, "version": 1 }