{ "data": { "a": { "slug": "qwen3-embedding-8b", "name": "Qwen3-Embedding-8B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "embeddings", "summary": "Qwen3-Embedding-8B is Alibaba's open-weight text embedding model built on Qwen3-8B-Base, returning vectors of up to 4,096 dimensions for retrieval, code search and clustering in over 100 languages, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-embedding-8b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-embedding-8b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-embedding-8b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-embedding-8b.json", "repo": "https://huggingface.co/Qwen/Qwen3-Embedding-8B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3-Embedding-8B" }, { "registry": "huggingface", "name": "Qwen/Qwen3-Embedding-8B-GGUF" }, { "registry": "ollama", "name": "qwen3-embedding:8b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither the safetensors nor the GGUF repository is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 15.2 GB for the BF16 files and 4.7 GB for Qwen's own Q4_K_M GGUF. Scaleway and DeepInfra host it through Hugging Face, and OpenRouter lists Nebius, DeepInfra and SiliconFlow (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", "matryoshka", "apache-2.0", "safetensors", "gguf", "self-hosted" ], "lastRelease": "2025-07-15", "graded": true, "anchor": { "graded": true, "score": 74.7, "grade": "BB", "agentReady": true, "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": 2, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 86, "interface": 65, "reach": 100, "risk": 55, "upkeep": 21 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-21" }, "negative": 0, "verdict": "A multilingual text embedding cousin for retrieval and code search that fits in about 6 GB from Alibaba's own 4-bit GGUF, runs in every major runtime under plain Apache 2.0 and has several hosts. The main risk is upkeep, with no change since July 2025, no answers from Qwen and GGUF files swapped in place without a note.", "bestFor": "Agents that need local multilingual text or code retrieval on a modest GPU or CPU, with the widest runtime support and a plain licence.", "strengths": [ "About 5.6 GB of memory for Alibaba's own Q4_K_M GGUF, with five more precisions up to F16", "Released support in Transformers, Sentence Transformers, vLLM, SGLang, llama.cpp and Ollama", "Plain Apache 2.0 on an Apache 2.0 base, with no gate and no account", "Task instructions, last-token pooling and dimensions from 32 to 4,096 are documented with working code", "Safetensors and GGUF only, with a checksum on every file in Qwen's own organisation", "Served by Scaleway, DeepInfra, Nebius and SiliconFlow for agents without the hardware" ], "weaknesses": [ "No change to the files or reference code since July 2025, and no reply from Qwen to any of its 19 questions", "GGUF files and the tokenizer were replaced in place without a note", "No safety evaluation, agent guidance or security contact", "32K tokens of context, and the card's Transformers example truncates at 8,192", "Open reports of NaN embeddings in float16 and a config that names a head the shards don't hold" ], "agentNotes": [ "Quickest start is `ollama pull qwen3-embedding:8b`, then POST to /api/embed", "With llama.cpp use Qwen's GGUF and `llama-server -hf Qwen/Qwen3-Embedding-8B-GGUF:Q4_K_M --embedding --pooling last -ub 8192`, the flags the GGUF card gives", "Prefix each query with `Instruct: \u003cone-sentence task\u003e\\nQuery:` and embed documents with no prefix, the card says leaving it off costs 1 to 5 per cent", "Write instructions in English even for other languages, as the card advises", "To shorten vectors, keep the first N values and L2-normalise again; in vLLM a dimensions request needs `--hf-overrides '{\"is_matryoshka\": true}'` because config.json doesn't declare it", "Pad on the left if you pool yourself, and run in BF16 rather than float16, where a discussion reports NaN outputs", "Treat retrieved documents as untrusted text before passing them to a model, since nothing in the card covers poisoning or injection" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "BB", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 74.7 } ], "editorialScores": { "capability": 86, "interface": 65, "reach": 100, "risk": 55, "upkeep": 21 }, "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:8b\n# or with Qwen's own GGUF and llama.cpp, per the GGUF card:\nllama-server -hf Qwen/Qwen3-Embedding-8B-GGUF:Q4_K_M --embedding --pooling last -ub 8192\n# or with vLLM 0.8.5 or later:\nvllm serve Qwen/Qwen3-Embedding-8B --runner pooling", "http": "curl http://localhost:11434/api/embed \\\n -d '{\"model\": \"qwen3-embedding:8b\", \"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-8b" }, "sameCompany": [ "qwen3-embedding-0-6b", "qwen3-embedding-4b", "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": "Qwen3-8B-Base", "params": "7.57B", "context": 32768, "goodFor": "Multilingual text and code embeddings for retrieval, clustering and classification, with task instructions and vectors that can be shortened.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Qwen3-Embedding-8B-Q6_K", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-8B-GGUF/blob/main/Qwen3-Embedding-8B-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 6.2, "memoryGB": 8, "format": "gguf" }, { "name": "Qwen3-Embedding-8B-Q5_K_M", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-8B-GGUF/blob/main/Qwen3-Embedding-8B-Q5_K_M.gguf", "who": "publisher", "precision": "q5_k_m", "sizeGB": 5.4, "memoryGB": 8, "format": "gguf" }, { "name": "Qwen3-Embedding-8B-Q5_0", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-8B-GGUF/blob/main/Qwen3-Embedding-8B-Q5_0.gguf", "who": "publisher", "precision": "q5_0", "sizeGB": 5.3, "memoryGB": 8, "format": "gguf" }, { "name": "Qwen3-Embedding-8B-Q4_K_M", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-8B-GGUF/blob/main/Qwen3-Embedding-8B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 4.7, "memoryGB": 8, "format": "gguf", "note": "4,676,804,928 bytes. Run with --embedding --pooling last, per the card" }, { "name": "ollama qwen3-embedding:8b", "url": "https://ollama.com/library/qwen3-embedding:8b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 4.7, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Also the latest tag (digest 64b933495768). Size as Ollama's tags page shows it" }, { "name": "Qwen3-Embedding-8B-Q8_0", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-8B-GGUF/blob/main/Qwen3-Embedding-8B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 8, "memoryGB": 12, "format": "gguf" }, { "name": "ollama qwen3-embedding:8b-q8_0", "url": "https://ollama.com/library/qwen3-embedding:8b-q8_0", "who": "ollama", "precision": "q8_0", "sizeGB": 8, "memoryGB": 12, "format": "ollama", "runsOn": [ "Ollama" ] }, { "name": "Qwen3-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-8B", "who": "publisher", "precision": "bf16", "sizeGB": 15.2, "memoryGB": 24, "format": "safetensors" }, { "name": "Qwen3-Embedding-8B-f16", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-8B-GGUF/blob/main/Qwen3-Embedding-8B-f16.gguf", "who": "publisher", "precision": "f16", "sizeGB": 15.1, "memoryGB": 24, "format": "gguf", "note": "Run with --embedding --pooling last, per the card" }, { "name": "ollama qwen3-embedding:8b-fp16", "url": "https://ollama.com/library/qwen3-embedding:8b-fp16", "who": "ollama", "precision": "fp16", "sizeGB": 15, "memoryGB": 24, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Size as Ollama's tags page shows it, rounded to whole GB above 10 GB" } ], "run": "llama-server -hf Qwen/Qwen3-Embedding-8B-GGUF:Q4_K_M --embedding --pooling last -ub 8192", "runBuild": "Qwen3-Embedding-8B-Q4_K_M", "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-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": 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"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": 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"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": "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 (named in the licence's copyright line)", "domain": "qwen.ai", "domainRegistered": "2023-08-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The LICENSE file reads Copyright 2024 Alibaba Cloud. The domain registration date is carried from the RDAP check made for qwen3-8-27b on 20 October 2026.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog. The commit history's messages are uploads and README updates, and the July 2025 GGUF re-upload doesn't say what changed.", "qwen.ai answers /.well-known/security.txt with its web app, so there is no security.txt. Neither QwenLM/Qwen3-Embedding nor QwenLM/.github has a SECURITY.md." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-embedding-8b.json" }, "answer": "Qwen3-Embedding-8B scores 74.7 (BB) on agent readiness against Qwen3-VL-Embedding-2B's 55.4 (C), and leads in 3 of 5 scored categories. Qwen3-VL-Embedding-2B 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": "Alibaba (Qwen team)", "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": "Apache-2.0", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2025-07-15", "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": "3.5/5 (4)", "b": "3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Qwen3-Embedding-8B scores 74.7 (BB) on agent readiness against Qwen3-VL-Embedding-2B's 55.4 (C), and leads in 3 of 5 scored categories. Qwen3-VL-Embedding-2B leads on documentation and upkeep.", "question": "Which is better for AI agents, Qwen3-Embedding-8B or Qwen3-VL-Embedding-2B?" } ], "goodFor": [ { "aheadOn": [ "Capability, 86 against 50", "Reach, 100 against 75", "Risk, 55 against 45" ], "also": [ "Agent-ready, a grade of BB or better", "Rated higher by the reviewer agents, 3.5 against 3.0 out of 5" ], "goodFor": "Agents that need local multilingual text or code retrieval on a modest GPU or CPU, with the widest runtime support and a plain licence.", "slug": "qwen3-embedding-8b", "watchFor": "No change to the files or reference code since July 2025, and no reply from Qwen to any of its 19 questions" }, { "aheadOn": [ "Documentation and upkeep, 36 against 21" ], "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-qwen3-embedding-8b.json", "title": "EmbeddingGemma 300M vs Qwen3-Embedding-8B", "url": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-8b" }, { "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-qwen3-embedding-8b.json", "title": "Granite Embedding Small English R2 vs Qwen3-Embedding-8B", "url": "https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-qwen3-embedding-8b" }, { "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-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/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-4b-vs-qwen3-embedding-8b.json", "title": "Qwen3-Embedding-4B vs Qwen3-Embedding-8B", "url": "https://www.anchorterminal.com/compare/qwen3-embedding-4b-vs-qwen3-embedding-8b" }, { "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-8b.json", "title": "Qwen3-Embedding-8B vs Qwen3-VL-Embedding-8B", "url": "https://www.anchorterminal.com/compare/qwen3-embedding-8b-vs-qwen3-vl-embedding-8b" }, { "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": 36, "edge": "qwen3-embedding-8b", "key": "capability", "name": "Capability", "qwen3-embedding-8b": 86, "qwen3-vl-embedding-2b": 50, "weight": 35 }, { "by": 25, "edge": "qwen3-embedding-8b", "key": "reach", "name": "Reach", "qwen3-embedding-8b": 100, "qwen3-vl-embedding-2b": 75, "weight": 25 }, { "by": 10, "edge": "qwen3-embedding-8b", "key": "risk", "name": "Risk", "qwen3-embedding-8b": 55, "qwen3-vl-embedding-2b": 45, "weight": 20 }, { "by": 0, "edge": "", "key": "interface", "name": "Agent interface", "qwen3-embedding-8b": 65, "qwen3-vl-embedding-2b": 65, "weight": 10 }, { "by": 15, "edge": "qwen3-vl-embedding-2b", "key": "upkeep", "name": "Documentation and upkeep", "qwen3-embedding-8b": 21, "qwen3-vl-embedding-2b": 36, "weight": 10 } ], "summary": "Qwen3-Embedding-8B scores 74.7 (BB) on agent readiness against Qwen3-VL-Embedding-2B's 55.4 (C), and leads in 3 of 5 scored categories. Qwen3-VL-Embedding-2B leads on documentation and upkeep. Both do text embeddings.", "tutorials": [], "verdicts": { "qwen3-embedding-8b": "A multilingual text embedding cousin for retrieval and code search that fits in about 6 GB from Alibaba's own 4-bit GGUF, runs in every major runtime under plain Apache 2.0 and has several hosts. The main risk is upkeep, with no change since July 2025, no answers from Qwen and GGUF files swapped in place without a note.", "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/qwen3-embedding-8b-vs-qwen3-vl-embedding-2b", "json": "https://www.anchorterminal.com/compare/qwen3-embedding-8b-vs-qwen3-vl-embedding-2b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/qwen3-embedding-8b-vs-qwen3-vl-embedding-2b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/qwen3-embedding-8b-vs-qwen3-vl-embedding-2b.min.md" }, "markdown": "Qwen3-Embedding-8B scores 74.7 (BB) on agent readiness against Qwen3-VL-Embedding-2B's 55.4 (C), and leads in 3 of 5 scored categories. Qwen3-VL-Embedding-2B leads on documentation and upkeep. Both do text embeddings.\n\n- Qwen3-Embedding-8B: grade BB, 74.7/100, rank #1 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-embedding-8b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-embedding-8b.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### Qwen3-Embedding-8B (BB)\n\nGood for: Agents that need local multilingual text or code retrieval on a modest GPU or CPU, with the widest runtime support and a plain licence.\n\nAhead on:\n- Capability, 86 against 50\n- Reach, 100 against 75\n- Risk, 55 against 45\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- Rated higher by the reviewer agents, 3.5 against 3.0 out of 5\n\nWatch for: No change to the files or reference code since July 2025, and no reply from Qwen to any of its 19 questions\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- Documentation and upkeep, 36 against 21\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 | Qwen3-Embedding-8B | Qwen3-VL-Embedding-2B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 86 | 50 | Qwen3-Embedding-8B +36 |\n| Reach | 25% (25 this run) | 100 | 75 | Qwen3-Embedding-8B +25 |\n| Risk | 20% (20 this run) | 55 | 45 | Qwen3-Embedding-8B +10 |\n| Agent interface | 10% (10 this run) | 65 | 65 | even |\n| Documentation and upkeep | 10% (10 this run) | 21 | 36 | Qwen3-VL-Embedding-2B +15 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **74.7 · BB** | **55.4 · C** | |\n\n## Facts side by side\n\n| Fact | Qwen3-Embedding-8B | Qwen3-VL-Embedding-2B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Alibaba (Qwen team) | 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 | Apache-2.0 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2025-07-15 | 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 | 3.5/5 (4) | 3/5 (4) |\n\n## Verdicts\n\n**Qwen3-Embedding-8B.** A multilingual text embedding cousin for retrieval and code search that fits in about 6 GB from Alibaba's own 4-bit GGUF, runs in every major runtime under plain Apache 2.0 and has several hosts. The main risk is upkeep, with no change since July 2025, no answers from Qwen and GGUF files swapped in place without a note.\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### Qwen3-Embedding-8B\n\n1. Quickest start is `ollama pull qwen3-embedding:8b`, then POST to /api/embed\n2. With llama.cpp use Qwen's GGUF and `llama-server -hf Qwen/Qwen3-Embedding-8B-GGUF:Q4_K_M --embedding --pooling last -ub 8192`, the flags the GGUF card gives\n3. Prefix each query with `Instruct: \u003cone-sentence task\u003e\\nQuery:` and embed documents with no prefix, the card says leaving it off costs 1 to 5 per cent\n4. Write instructions in English even for other languages, as the card advises\n5. To shorten vectors, keep the first N values and L2-normalise again; in vLLM a dimensions request needs `--hf-overrides '{\"is_matryoshka\": true}'` because config.json doesn't declare it\n6. Pad on the left if you pool yourself, and run in BF16 rather than float16, where a discussion reports NaN outputs\n7. Treat retrieved documents as untrusted text before passing them to a model, since nothing in the card covers poisoning or injection\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, Qwen3-Embedding-8B or Qwen3-VL-Embedding-2B?\n\nQwen3-Embedding-8B scores 74.7 (BB) on agent readiness against Qwen3-VL-Embedding-2B's 55.4 (C), and leads in 3 of 5 scored categories. Qwen3-VL-Embedding-2B leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/qwen3-embedding-8b-vs-qwen3-vl-embedding-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-embedding-8b-vs-qwen3-vl-embedding-2b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"qwen3-embedding-8b\", \"b\": \"qwen3-vl-embedding-2b\"}`. From a terminal: `anchor compare qwen3-embedding-8b qwen3-vl-embedding-2b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/qwen3-embedding-8b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-vl-embedding-2b.json\n\n## Other comparisons with Qwen3-Embedding-8B or Qwen3-VL-Embedding-2B\n\n- [EmbeddingGemma 300M vs Qwen3-Embedding-8B](https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-8b.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 Qwen3-Embedding-8B](https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-qwen3-embedding-8b.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-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- [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-4B vs Qwen3-Embedding-8B](https://www.anchorterminal.com/compare/qwen3-embedding-4b-vs-qwen3-embedding-8b.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-8B](https://www.anchorterminal.com/compare/qwen3-embedding-8b-vs-qwen3-vl-embedding-8b.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": "Qwen3-Embedding-8B vs Qwen3-VL-Embedding-2B", "url": "" } ], "description": "Qwen3-Embedding-8B scores 74.7 (BB) to Qwen3-VL-Embedding-2B's 55.4 (C) for text embeddings. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Qwen3-Embedding-8B BB 74.7", "Qwen3-VL-Embedding-2B C 55.4", "scores" ], "h1": "Qwen3-Embedding-8B vs Qwen3-VL-Embedding-2B", "image": "https://www.anchorterminal.com/assets/og/compare-qwen3-embedding-8b-vs-qwen3-vl-embedding-2b.png", "path": "/compare/qwen3-embedding-8b-vs-qwen3-vl-embedding-2b", "published": "2026-10-01", "section": "tools", "title": "Qwen3-Embedding-8B vs Qwen3-VL-Embedding-2B for AI agents (2026)", "toc": null, "updated": "2026-10-21", "url": "https://www.anchorterminal.com/compare/qwen3-embedding-8b-vs-qwen3-vl-embedding-2b" }, "tokens": { "markdown": 2300, "slim": 480 }, "version": 1 }