{ "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-VL-Embedding-2B scores 55.4 (C) on agent readiness against jina-embeddings-v5-omni-small's 50.2 (D), and leads in 2 of 5 scored categories. jina-embeddings-v5-omni-small leads on documentation and upkeep.", "b": { "slug": "qwen3-vl-embedding-2b", "name": "Qwen3-VL-Embedding-2B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "embeddings", "summary": "Qwen3-VL-Embedding-2B is Alibaba's open-weight multimodal embedding model of 2.1B parameters, built on Qwen3-VL-2B-Instruct, that turns text, images, screenshots and video into one vector of up to 2,048 dimensions, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-2b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-2b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-2b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-vl-embedding-2b.json", "repo": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3-VL-Embedding-2B" } ], "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). The reference code and evaluation scripts are in a public GitHub repository, and ModelScope has a mirror from the same publisher.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 4.3 GB of BF16 files. Alibaba's Model Studio sells a hosted qwen3-vl-embedding model in its Beijing region, but its default of 2,560 dimensions matches neither open size, so it isn't taken to be this model (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-VL-Embedding", "capabilities": [ "embed.text", "embed.multimodal", "embed.multilingual", "model.open-weights" ], "tags": [ "open-weights", "model", "embeddings", "multimodal", "vision", "video", "multilingual", "matryoshka", "apache-2.0", "safetensors", "sentence-transformers", "self-hosted" ], "lastRelease": "2026-04-16", "graded": true, "anchor": { "graded": true, "score": 55.4, "grade": "C", "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": 13, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 50, "interface": 65, "reach": 75, "risk": 45, "upkeep": 36 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-21" }, "negative": 0, "verdict": "A small multimodal embedding cousin that puts text, images, screenshots and video into one vector space, in 4.3 GB of BF16 files that fit any 8 GB machine, under plain Apache 2.0. It loads with Sentence Transformers, vLLM or SGLang. The main gaps are no quantised build from Alibaba, few publisher replies and no safety material.", "bestFor": "Agents that need local multimodal search over text, screenshots, images and video on a small GPU or CPU", "strengths": [ "Apache 2.0 with nothing added, on an Apache 2.0 base, ungated, in Qwen's own organisation", "4.27 GB of BF16 safetensors, about 5 GB in memory", "Text, images, screenshots, video and mixtures in one input, with a task instruction", "Loads with Sentence Transformers 5.4, vLLM's pooling runner and SGLang with no repository code", "Technical report describing training stages, Matryoshka and quantisation-aware training" ], "weaknesses": [ "No quantised build from Alibaba and nothing in Ollama's or LM Studio's own libraries", "Shorter vectors and int8 or binary output are claimed, but how to ask for them isn't documented", "vLLM's image preprocessing differs from the reference code, so vectors aren't identical across runtimes", "One publisher reply in 14 questions, and files unchanged since 16 April 2026", "No safety results, agent guidance or security contact" ], "agentNotes": [ "Start it with `vllm serve Qwen/Qwen3-VL-Embedding-2B --runner pooling --max-model-len 8192`, then POST chat-style `messages` with the instruction in the system turn to /v1/embeddings", "Or `pip install -U sentence-transformers` (5.4 or later) and `SentenceTransformer('Qwen/Qwen3-VL-Embedding-2B').encode(texts, prompt='\u003ctask instruction\u003e')`; images go in as URLs, paths or dicts with text and image", "Write the task instruction in English, even for other languages, and embed queries and documents with matching instructions", "To shorten vectors, cut the first N of 2,048 dimensions and renormalise yourself; no parameter for it is documented", "Don't mix vectors from vLLM and the reference qwen_vl_utils path in one index, since vLLM's docs say image preprocessing differs", "Treat retrieved documents as untrusted before passing them to another model" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 55.4 } ], "editorialScores": { "capability": 50, "interface": 65, "reach": 75, "risk": 45, "upkeep": 36 }, "provenanceScore": 63, "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 -U sentence-transformers # 5.4 or later\npython -c \"from sentence_transformers import SentenceTransformer; m = SentenceTransformer('Qwen/Qwen3-VL-Embedding-2B'); print(m.encode(['A dog on a beach'], prompt='Retrieve images or text relevant to the query.').shape)\"", "http": "# after: vllm serve Qwen/Qwen3-VL-Embedding-2B --runner pooling --max-model-len 8192\ncurl http://localhost:8000/v1/embeddings \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"Qwen/Qwen3-VL-Embedding-2B\", \"encoding_format\": \"float\", \"messages\": [{\"role\": \"system\", \"content\": [{\"type\": \"text\", \"text\": \"Represent the user'\"'\"'s input.\"}]}, {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"A dog on a beach\"}]}]}'" }, "letme": { "capability": "https://letme.dev/embed.text", "tool": "https://letme.dev/qwen3-vl-embedding-2b" }, "sameCompany": [ "qwen3-embedding-0-6b", "qwen3-embedding-4b", "qwen3-embedding-8b", "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-VL-2B-Instruct (fine-tune with LoRA, declared in the card metadata)", "params": "2.13B", "context": 32768, "dimensions": 2048, "goodFor": "Multimodal retrieval and clustering, one vector for text, images, screenshots, video or a mix of them, in 30 or more languages.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Qwen3-VL-Embedding-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B", "who": "publisher", "precision": "bf16", "sizeGB": 4.27, "memoryGB": 8, "format": "safetensors", "runsOn": [ "Sentence Transformers", "vLLM", "SGLang" ] } ], "run": "vllm serve Qwen/Qwen3-VL-Embedding-2B --runner pooling --max-model-len 8192", "runBuild": "Qwen3-VL-Embedding-2B", "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-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-0-6b" }, { "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-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": "Qwen3.5-2B", "url": "https://huggingface.co/Qwen/Qwen3.5-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-2b" }, { "name": "Qwen3.5-2B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-2B-Base", "params": "2B" }, { "name": "Qwen3.6-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B", "params": "35B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b" }, { "name": "Qwen3.6-27B", "url": "https://huggingface.co/Qwen/Qwen3.6-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-27b" }, { "name": "Qwen-AgentWorld-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.8-27B", "url": "https://huggingface.co/Qwen/Qwen3.8-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-27b" }, { "name": "Qwen3.8-2.4T-A95B", "url": "https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B", "params": "2.4T-A95B" }, { "name": "Qwen3.8-Flash-Next", "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 (RDAP names Alibaba Cloud (Singapore) Private Limited as the registrant of qwen.ai)", "domain": "qwen.ai", "domainRegistered": "2023-08-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The GitHub README links the Qwen organisation on Hugging Face and ModelScope. The registrant and domain date are carried from the Qwen3.8-27B check of 20 October 2026.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the Hugging Face commit history, 11 commits from 7 January to 16 April 2026, the last adding Sentence Transformers 5.4 support.", "No SECURITY.md or .github folder in QwenLM/Qwen3-VL-Embedding, and qwen.ai has no security.txt (per the Qwen3.8-27B check)." ], "score": 63 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-2b.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": "2026-04-16", "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 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Qwen3-VL-Embedding-2B scores 55.4 (C) on agent readiness against jina-embeddings-v5-omni-small's 50.2 (D), and leads in 2 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-VL-Embedding-2B?" } ], "goodFor": [ { "aheadOn": [ "Documentation and upkeep, 69 against 36" ], "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, 50 against 33", "Reach, 75 against 65" ], "also": null, "goodFor": "Agents that need local multimodal search over text, screenshots, images and video on a small GPU or CPU", "slug": "qwen3-vl-embedding-2b", "watchFor": "No quantised build from Alibaba and nothing in Ollama's or LM Studio's own libraries" } ], "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-vl-embedding-2b.json", "title": "EmbeddingGemma 300M vs Qwen3-VL-Embedding-2B", "url": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-vl-embedding-2b" }, { "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-vl-embedding-2b.json", "title": "Granite Embedding Small English R2 vs Qwen3-VL-Embedding-2B", "url": "https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-qwen3-vl-embedding-2b" }, { "json": "https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-embedding-0-6b.json", "title": "jina-embeddings-v5-omni-small vs Qwen3-Embedding-0.6B", "url": "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-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-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-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-4b-vs-qwen3-vl-embedding-2b.json", "title": "Qwen3-Embedding-4B vs Qwen3-VL-Embedding-2B", "url": "https://www.anchorterminal.com/compare/qwen3-embedding-4b-vs-qwen3-vl-embedding-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-embedding-8b-vs-qwen3-vl-embedding-2b.json", "title": "Qwen3-Embedding-8B vs Qwen3-VL-Embedding-2B", "url": "https://www.anchorterminal.com/compare/qwen3-embedding-8b-vs-qwen3-vl-embedding-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-embedding-2b-vs-qwen3-vl-embedding-8b.json", "title": "Qwen3-VL-Embedding-2B vs Qwen3-VL-Embedding-8B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-embedding-2b-vs-qwen3-vl-embedding-8b" } ], "scores": [ { "by": 17, "edge": "qwen3-vl-embedding-2b", "jina-embeddings-v5-omni-small": 33, "key": "capability", "name": "Capability", "qwen3-vl-embedding-2b": 50, "weight": 35 }, { "by": 10, "edge": "qwen3-vl-embedding-2b", "jina-embeddings-v5-omni-small": 65, "key": "reach", "name": "Reach", "qwen3-vl-embedding-2b": 75, "weight": 25 }, { "by": 0, "edge": "", "jina-embeddings-v5-omni-small": 45, "key": "risk", "name": "Risk", "qwen3-vl-embedding-2b": 45, "weight": 20 }, { "by": 0, "edge": "", "jina-embeddings-v5-omni-small": 65, "key": "interface", "name": "Agent interface", "qwen3-vl-embedding-2b": 65, "weight": 10 }, { "by": 33, "edge": "jina-embeddings-v5-omni-small", "jina-embeddings-v5-omni-small": 69, "key": "upkeep", "name": "Documentation and upkeep", "qwen3-vl-embedding-2b": 36, "weight": 10 } ], "summary": "Qwen3-VL-Embedding-2B scores 55.4 (C) on agent readiness against jina-embeddings-v5-omni-small's 50.2 (D), and leads in 2 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-vl-embedding-2b": "A small multimodal embedding cousin that puts text, images, screenshots and video into one vector space, in 4.3 GB of BF16 files that fit any 8 GB machine, under plain Apache 2.0. It loads with Sentence Transformers, vLLM or SGLang. The main gaps are no quantised build from Alibaba, few publisher replies and no safety material." } }, "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-vl-embedding-2b", "json": "https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-vl-embedding-2b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-vl-embedding-2b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-vl-embedding-2b.min.md" }, "markdown": "Qwen3-VL-Embedding-2B scores 55.4 (C) on agent readiness against jina-embeddings-v5-omni-small's 50.2 (D), and leads in 2 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-VL-Embedding-2B: grade C, 55.4/100, rank #80 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-vl-embedding-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-vl-embedding-2b.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 36\n\nWatch for: CC BY-NC 4.0, so commercial use needs a separate licence from Jina\n\n### Qwen3-VL-Embedding-2B (C)\n\nGood for: Agents that need local multimodal search over text, screenshots, images and video on a small GPU or CPU\n\nAhead on:\n- Capability, 50 against 33\n- Reach, 75 against 65\n\nWatch for: No quantised build from Alibaba and nothing in Ollama's or LM Studio's own libraries\n\n\n## Score by category\n\n| Category | Weight | jina-embeddings-v5-omni-small | Qwen3-VL-Embedding-2B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 33 | 50 | Qwen3-VL-Embedding-2B +17 |\n| Reach | 25% (25 this run) | 65 | 75 | Qwen3-VL-Embedding-2B +10 |\n| Risk | 20% (20 this run) | 45 | 45 | even |\n| Agent interface | 10% (10 this run) | 65 | 65 | even |\n| Documentation and upkeep | 10% (10 this run) | 69 | 36 | jina-embeddings-v5-omni-small +33 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **50.2 · D** | **55.4 · C** | |\n\n## Facts side by side\n\n| Fact | jina-embeddings-v5-omni-small | Qwen3-VL-Embedding-2B |\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 | 2026-04-16 |\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 (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-VL-Embedding-2B.** A small multimodal embedding cousin that puts text, images, screenshots and video into one vector space, in 4.3 GB of BF16 files that fit any 8 GB machine, under plain Apache 2.0. It loads with Sentence Transformers, vLLM or SGLang. The main gaps are no quantised build from Alibaba, few publisher replies and no safety material.\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-VL-Embedding-2B\n\n1. Start it with `vllm serve Qwen/Qwen3-VL-Embedding-2B --runner pooling --max-model-len 8192`, then POST chat-style `messages` with the instruction in the system turn to /v1/embeddings\n2. Or `pip install -U sentence-transformers` (5.4 or later) and `SentenceTransformer('Qwen/Qwen3-VL-Embedding-2B').encode(texts, prompt='\u003ctask instruction\u003e')`; images go in as URLs, paths or dicts with text and image\n3. Write the task instruction in English, even for other languages, and embed queries and documents with matching instructions\n4. To shorten vectors, cut the first N of 2,048 dimensions and renormalise yourself; no parameter for it is documented\n5. Don't mix vectors from vLLM and the reference qwen_vl_utils path in one index, since vLLM's docs say image preprocessing differs\n6. Treat retrieved documents as untrusted before passing them to another model\n\n## Questions\n\n### Which is better for AI agents, jina-embeddings-v5-omni-small or Qwen3-VL-Embedding-2B?\n\nQwen3-VL-Embedding-2B scores 55.4 (C) on agent readiness against jina-embeddings-v5-omni-small's 50.2 (D), and leads in 2 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-vl-embedding-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-vl-embedding-2b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"jina-embeddings-v5-omni-small\", \"b\": \"qwen3-vl-embedding-2b\"}`. From a terminal: `anchor compare jina-embeddings-v5-omni-small qwen3-vl-embedding-2b`\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-vl-embedding-2b.json\n\n## Other comparisons with jina-embeddings-v5-omni-small or Qwen3-VL-Embedding-2B\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-VL-Embedding-2B](https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-vl-embedding-2b.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-VL-Embedding-2B](https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-qwen3-vl-embedding-2b.md)\n- [jina-embeddings-v5-omni-small vs Qwen3-Embedding-0.6B](https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-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-8B](https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-vl-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-4B vs Qwen3-VL-Embedding-2B](https://www.anchorterminal.com/compare/qwen3-embedding-4b-vs-qwen3-vl-embedding-2b.md)\n- [Qwen3-Embedding-8B vs Qwen3-VL-Embedding-2B](https://www.anchorterminal.com/compare/qwen3-embedding-8b-vs-qwen3-vl-embedding-2b.md)\n- [Qwen3-VL-Embedding-2B vs Qwen3-VL-Embedding-8B](https://www.anchorterminal.com/compare/qwen3-vl-embedding-2b-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-VL-Embedding-2B", "url": "" } ], "description": "Qwen3-VL-Embedding-2B scores 55.4 (C) 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-VL-Embedding-2B C 55.4", "scores" ], "h1": "jina-embeddings-v5-omni-small vs Qwen3-VL-Embedding-2B", "image": "https://www.anchorterminal.com/assets/og/compare-jina-embeddings-v5-omni-small-vs-qwen3-vl-embedding-2b.png", "path": "/compare/jina-embeddings-v5-omni-small-vs-qwen3-vl-embedding-2b", "published": "2026-10-01", "section": "tools", "title": "jina-embeddings-v5-omni-small vs Qwen3-VL-Embedding-2B (2026)", "toc": null, "updated": "2026-10-21", "url": "https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-vl-embedding-2b" }, "tokens": { "markdown": 2300, "slim": 480 }, "version": 1 }