{ "data": { "a": { "slug": "granite-embedding-small-english-r2", "name": "Granite Embedding Small English R2", "vendor": "IBM", "vendorUrl": "https://www.ibm.com/granite", "kind": "weights", "category": "embeddings", "summary": "Granite Embedding Small English R2 is IBM's open-weight English text embedding model, a 47M-parameter ModernBERT bi-encoder that returns 384-dimension vectors for inputs of up to 8,192 tokens, trained on permissively licensed and IBM data, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/granite-embedding-small-english-r2", "markdownUrl": "https://www.anchorterminal.com/tools/granite-embedding-small-english-r2.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/granite-embedding-small-english-r2.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/granite-embedding-small-english-r2.json", "repo": "https://huggingface.co/ibm-granite/granite-embedding-small-english-r2", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "ibm-granite/granite-embedding-small-english-r2" } ], "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 examples and paper are in the public GitHub repository ibm-granite/granite-embedding-models.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. IBM sells no per-call access to this model; watsonx.ai's embedding list has only the multilingual Granite embedding models. Running it costs almost nothing in hardware, 95 MB of BF16 safetensors (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/ibm-granite/granite-embedding-models", "capabilities": [ "embed.text", "embed.code", "model.open-weights" ], "tags": [ "open-weights", "model", "embeddings", "english", "modernbert", "long-context", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-01-21", "graded": true, "anchor": { "graded": true, "score": 41.7, "grade": "E", "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": 17, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 10, "interface": 60, "reach": 70, "risk": 55, "upkeep": 37 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-21" }, "negative": 0, "verdict": "A tiny English embedding cousin for retrieval over long documents and code, 47M parameters with 384-dimension vectors and an 8,192-token input, under Apache 2.0 with a permissive data story. It runs on any hardware through Sentence Transformers, vLLM or llama.cpp. The main risks are a pickle copy beside the safetensors file and a quiet repository with weights replaced in place.", "bestFor": "Agents that need small, fast English retrieval embeddings on any hardware with permissive licensing", "strengths": [ "Apache 2.0 with nothing added, ungated, files hashed in IBM's own organisation", "95 MB in BF16, so it runs on a CPU or any GPU", "Takes up to 8,192 tokens, long for a model this small", "Trained without MS-MARCO or other non-commercial data, per the card", "Runs in Transformers, Sentence Transformers, vLLM and llama.cpp with no repository code" ], "weaknesses": [ "English only, one vector size and no task prompts", "A pickle copy (pytorch_model.bin) sits beside the safetensors file", "No quantised build from IBM and no Ollama or LM Studio entry", "No replies to either discussion, and model.safetensors was replaced on 6 January 2026 without a note", "No safety results and no training data cut-off" ], "agentNotes": [ "Load it with `SentenceTransformer(\"ibm-granite/granite-embedding-small-english-r2\")`, or serve it with `vllm serve ibm-granite/granite-embedding-small-english-r2 --runner pooling` and call /v1/embeddings", "Pass `normalize_embeddings=True` when you need unit vectors; Sentence Transformers returns them unnormalised", "In raw Transformers, take the CLS token (`model_output[0][:, 0]`) and normalise it yourself", "Don't add query or passage prefixes; the model takes plain text", "Load the safetensors file and ignore pytorch_model.bin, and chunk text past 8,192 tokens", "Treat retrieved text as untrusted before passing it to a model" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "E", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 41.7 } ], "editorialScores": { "capability": 10, "interface": 60, "reach": 70, "risk": 55, "upkeep": 37 }, "provenanceScore": 87, "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\npython -c \"from sentence_transformers import SentenceTransformer; m=SentenceTransformer('ibm-granite/granite-embedding-small-english-r2'); print(m.encode(['summit define'], normalize_embeddings=True).shape)\"\n# or, with vLLM:\nvllm serve ibm-granite/granite-embedding-small-english-r2 --runner pooling", "http": "curl http://localhost:8000/v1/embeddings \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"ibm-granite/granite-embedding-small-english-r2\", \"input\": [\"summit define\"]}'" }, "letme": { "capability": "https://letme.dev/embed.text", "tool": "https://letme.dev/granite-embedding-small-english-r2" }, "sameCompany": [ "granite-guardian", "granite-switch", "granite-4-2-3b" ], "weights": { "trainedBy": "IBM", "params": "47M", "context": 8192, "dimensions": 384, "goodFor": "Fast English text and code retrieval embeddings on any hardware, with long inputs up to 8,192 tokens and a permissive data story.", "runtimes": [ "llama.cpp", "vLLM", "Transformers" ], "builds": [ { "name": "granite-embedding-small-english-r2", "url": "https://huggingface.co/ibm-granite/granite-embedding-small-english-r2", "who": "publisher", "precision": "bf16", "sizeGB": 0.1, "memoryGB": 8, "format": "safetensors", "note": "Size is model.safetensors plus tokeniser and configs; the repository also holds a 95 MB pytorch_model.bin copy, 0.19 GB in all" } ], "run": "vllm serve ibm-granite/granite-embedding-small-english-r2 --runner pooling", "runBuild": "granite-embedding-small-english-r2", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "IBM (Granite)", "family": "Granite Embedding R2", "url": "https://www.anchorterminal.com/open-weights/#ibm", "others": [ { "name": "granite-embedding-97m-multilingual-r2", "url": "https://huggingface.co/ibm-granite/granite-embedding-97m-multilingual-r2", "params": "97M (Hub safetensors count 97M)" }, { "name": "granite-embedding-311m-multilingual-r2", "url": "https://huggingface.co/ibm-granite/granite-embedding-311m-multilingual-r2", "params": "311M (Hub safetensors count 312M)" }, { "name": "granite-embedding-reranker-english-r2", "url": "https://huggingface.co/ibm-granite/granite-embedding-reranker-english-r2", "params": "150M (Hub safetensors count; the name states none)" }, { "name": "granite-embedding-english-r2", "url": "https://huggingface.co/ibm-granite/granite-embedding-english-r2", "params": "149M (Hub safetensors count; the name states none)" } ], "olderCount": 64 }, "area": "models", "provenance": { "legalEntity": "International Business Machines Corporation", "domain": "ibm.com", "domainRegistered": "1986-03-19", "endpointOnVendorDomain": null, "terms": "https://www.apache.org/licenses/LICENSE-2.0", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/ibm-granite/granite-embedding-small-english-r2/commits/main", "securityTxt": "valid", "checked": "2026-10-21", "notes": [ "The legal entity and registration date of ibm.com are carried from the granite-guardian listing's provenance, checked on 8 October 2026. www.ibm.com/granite links to huggingface.co/ibm-granite.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The Hugging Face commit history has seven commits from 17 July 2025 to 21 January 2026. Most messages don't say what changed.", "www.ibm.com/.well-known/security.txt lists IBM PSIRT, HackerOne and an email contact and expires on 21 November 2026. Neither ibm-granite/granite-embedding-models nor the ibm-granite organisation's .github repository has a SECURITY file." ], "score": 87 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/granite-embedding-small-english-r2.json" }, "answer": "Qwen3-Embedding-4B scores 72.9 (BB) on agent readiness against Granite Embedding Small English R2's 41.7 (E), and leads in 3 of 5 scored categories. Granite Embedding Small English R2 leads on documentation and upkeep.", "b": { "slug": "qwen3-embedding-4b", "name": "Qwen3-Embedding-4B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "embeddings", "summary": "Qwen3-Embedding-4B is Alibaba's open-weight text embedding model built on Qwen3-4B-Base, returning vectors of up to 2,560 dimensions for retrieval, code search and clustering in over 100 languages, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-embedding-4b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-embedding-4b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-embedding-4b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-embedding-4b.json", "repo": "https://huggingface.co/Qwen/Qwen3-Embedding-4B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3-Embedding-4B" }, { "registry": "huggingface", "name": "Qwen/Qwen3-Embedding-4B-GGUF" }, { "registry": "ollama", "name": "qwen3-embedding:4b" } ], "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 as the card's metadata states. Running it yourself costs the hardware, 8.1 GB for the BF16 files and 2.5 GB for Qwen's own Q4_K_M GGUF. DeepInfra hosts it through Hugging Face, and SiliconFlow lists it at $0.02 per million input tokens (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": "2025-08-03", "graded": true, "anchor": { "graded": true, "score": 72.9, "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": 3, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 81, "interface": 65, "reach": 100, "risk": 55, "upkeep": 21 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-21" }, "negative": 0, "verdict": "A small multilingual text embedding cousin for retrieval and code search that runs in about 3 GB from Alibaba's own 4-bit GGUF, loads in every major runtime under Apache 2.0 and has two hosts, DeepInfra and SiliconFlow. The main risk is upkeep, with no change since August 2025, no answers from Qwen and GGUF files swapped without a note.", "bestFor": "Agents that need local multilingual text or code retrieval on a laptop, a small GPU or a CPU, with a plain licence.", "strengths": [ "About 3 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", "Apache 2.0 by the card's metadata on an Apache 2.0 base, with no gate and no account", "Task instructions, last-token pooling and dimensions from 32 to 2,560 are documented with working code", "Safetensors and GGUF only, with a checksum on every file in Qwen's own organisation" ], "weaknesses": [ "No change to the files or reference code since August 2025, and no reply from Qwen to any of its 16 questions", "GGUF files and the tokenizer were replaced in place without a note, and the Q8_0 file was truncated for almost three weeks in 2025", "No LICENSE file in the repository or in Qwen's GitHub repository for the series", "No safety evaluation, agent guidance or security contact", "32K tokens of context, and the card's Transformers example truncates at 8,192" ], "agentNotes": [ "Quickest start is `ollama pull qwen3-embedding:4b`, about 2.5 GB, then POST to /api/embed", "With llama.cpp use `llama-server -hf Qwen/Qwen3-Embedding-4B-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, in English even for other languages", "To shorten vectors keep the first N values and L2-normalise again; in vLLM a dimensions request needs `--hf-overrides '{\"is_matryoshka\": true}'`, since config.json doesn't declare it", "If you downloaded the Q8_0 GGUF between 15 July and 3 August 2025, fetch it again and check its SHA-256 against the Hub", "Treat retrieved documents as untrusted text before passing them to a model" ], "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": 72.9 } ], "editorialScores": { "capability": 81, "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:4b\n# or with Qwen's own GGUF and llama.cpp, per the GGUF card:\nllama-server -hf Qwen/Qwen3-Embedding-4B-GGUF:Q4_K_M --embedding --pooling last -ub 8192\n# or with SGLang, as its embedding docs show:\nsglang serve --model-path Qwen/Qwen3-Embedding-4B --is-embedding", "http": "curl http://localhost:11434/api/embed \\\n -d '{\"model\": \"qwen3-embedding:4b\", \"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-4b" }, "sameCompany": [ "qwen3-embedding-0-6b", "qwen3-embedding-8b", "qwen3-vl-embedding-2b", "qwen3-vl-embedding-8b", "qwen3-asr-0-6b", "qwen3-asr-1-7b", "qwen3-tts-1-7b-customvoice", "qwen3-30b-a3b-instruct-2507", "qwen3-4b-instruct-2507", "qwen3-coder-30b-a3b-instruct", "qwen3-coder-next", "qwen3-omni-30b-a3b-instruct", "qwen3-vl-2b-instruct", "qwen3-vl-30b-a3b-instruct", "qwen3-vl-32b-instruct", "qwen3-vl-4b-instruct", "qwen3-vl-8b-instruct", "qwen3-5-0-8b", "qwen3-5-122b-a10b", "qwen3-5-2b", "qwen3-5-397b-a17b", "qwen3-5-4b", "qwen3-5-9b", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "builtOn": "Qwen3-4B-Base", "params": "4.02B", "context": 32768, "goodFor": "Multilingual text and code embeddings for retrieval, clustering and classification on small hardware, with task instructions and vectors that can be shortened.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Qwen3-Embedding-4B-Q8_0", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B-GGUF/blob/main/Qwen3-Embedding-4B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 4.3, "memoryGB": 8, "format": "gguf", "note": "Replaced on 3 August 2025; the copy uploaded on 15 July 2025 was 1.50 GB" }, { "name": "Qwen3-Embedding-4B-Q6_K", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B-GGUF/blob/main/Qwen3-Embedding-4B-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 3.3, "memoryGB": 8, "format": "gguf" }, { "name": "Qwen3-Embedding-4B-Q5_K_M", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B-GGUF/blob/main/Qwen3-Embedding-4B-Q5_K_M.gguf", "who": "publisher", "precision": "q5_k_m", "sizeGB": 2.9, "memoryGB": 8, "format": "gguf" }, { "name": "Qwen3-Embedding-4B-Q5_0", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B-GGUF/blob/main/Qwen3-Embedding-4B-Q5_0.gguf", "who": "publisher", "precision": "q5_0", "sizeGB": 2.8, "memoryGB": 8, "format": "gguf" }, { "name": "Qwen3-Embedding-4B-Q4_K_M", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B-GGUF/blob/main/Qwen3-Embedding-4B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 2.5, "memoryGB": 8, "format": "gguf", "note": "2,496,703,776 bytes. Run with --embedding --pooling last, per the card" }, { "name": "ollama qwen3-embedding:4b", "url": "https://ollama.com/library/qwen3-embedding:4b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 2.5, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Same digest as 4b-q4_K_M (df5bd2e3c74c). Size as Ollama's tags page shows it" }, { "name": "ollama qwen3-embedding:4b-q8_0", "url": "https://ollama.com/library/qwen3-embedding:4b-q8_0", "who": "ollama", "precision": "q8_0", "sizeGB": 4.3, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ] }, { "name": "Qwen3-Embedding-4B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B", "who": "publisher", "precision": "bf16", "sizeGB": 8.1, "memoryGB": 12, "format": "safetensors" }, { "name": "Qwen3-Embedding-4B-f16", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B-GGUF/blob/main/Qwen3-Embedding-4B-f16.gguf", "who": "publisher", "precision": "f16", "sizeGB": 8, "memoryGB": 12, "format": "gguf", "note": "Run with --embedding --pooling last, per the card" }, { "name": "ollama qwen3-embedding:4b-fp16", "url": "https://ollama.com/library/qwen3-embedding:4b-fp16", "who": "ollama", "precision": "fp16", "sizeGB": 8, "memoryGB": 12, "format": "ollama", "runsOn": [ "Ollama" ] } ], "run": "llama-server -hf Qwen/Qwen3-Embedding-4B-GGUF:Q4_K_M --embedding --pooling last -ub 8192", "runBuild": "Qwen3-Embedding-4B-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-8B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-8b" }, { "name": "Qwen3-Reranker-4B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-4B", "params": "4B" }, { "name": "Qwen3-235B-A22B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507", "params": "235B-A22B" }, { "name": "Qwen3-Coder-480B-A35B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct", "params": "480B-A35B" }, { "name": "Qwen3-235B-A22B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-30b-a3b-instruct-2507" }, { "name": "Qwen3-30B-A3B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507", "params": "30B-A3B" }, { "name": "Qwen3-Coder-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct" }, { "name": "Qwen3-4B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507" }, { "name": "Qwen3-4B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507", "params": "4B" }, { "name": "Qwen3-Next-80B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct", "params": "80B-A3B" }, { "name": "Qwen3-Next-80B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking", "params": "80B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Captioner", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Captioner", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-omni-30b-a3b-instruct" }, { "name": "Qwen3-VL-235B-A22B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct", "params": "235B-A22B" }, { "name": "Qwen3-VL-235B-A22B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Thinking", "params": "235B-A22B" }, { "name": "Qwen3Guard-Gen-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Gen-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-4B", "params": "4B" }, { "name": "Qwen3Guard-Gen-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-8B", "params": "8B" }, { "name": "Qwen3Guard-Stream-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Stream-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-4B", "params": "4B" }, { "name": "Qwen3Guard-Stream-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-8B", "params": "8B" }, { "name": "Qwen3-4B-SafeRL", "url": "https://huggingface.co/Qwen/Qwen3-4B-SafeRL", "params": "4B" }, { "name": "Qwen3-VL-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct" }, { "name": "Qwen3-VL-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-VL-4B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct" }, { "name": "Qwen3-VL-4B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Thinking", "params": "4B" }, { "name": "Qwen3-VL-8B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct" }, { "name": "Qwen3-VL-8B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking", "params": "8B" }, { "name": "Qwen3-VL-2B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-2b-instruct" }, { "name": "Qwen3-VL-2B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Thinking", "params": "2B" }, { "name": "Qwen3-VL-32B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct", "params": "32B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct" }, { "name": "Qwen3-VL-32B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Thinking", "params": "32B" }, { "name": "Qwen3-VL-Embedding-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-2b" }, { "name": "Qwen3-VL-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-8b" }, { "name": "Qwen3-VL-Reranker-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-2B", "params": "2B" }, { "name": "Qwen3-VL-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-8B", "params": "8B" }, { "name": "Qwen3-TTS-12Hz-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-0.6B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-TTS-12Hz-1.7B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-tts-1-7b-customvoice" }, { "name": "Qwen3-TTS-12Hz-1.7B-VoiceDesign", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign", "params": "1.7B" }, { "name": "Qwen3-TTS-Tokenizer-12Hz", "url": "https://huggingface.co/Qwen/Qwen3-TTS-Tokenizer-12Hz", "params": "not in name" }, { "name": "Qwen3-ASR-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-0-6b" }, { "name": "Qwen3-ASR-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-1.7B", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-1-7b" }, { "name": "Qwen3-ForcedAligner-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ForcedAligner-0.6B", "params": "0.6B" }, { "name": "Qwen3-Coder-Next", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next", "params": "not in name; Hub safetensors total 79.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-next" }, { "name": "Qwen3-Coder-Next-Base", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next-Base", "params": "not in name; Hub safetensors total 79.7B" }, { "name": "WebWorld-14B", "url": "https://huggingface.co/Qwen/WebWorld-14B", "params": "14B" }, { "name": "WebWorld-32B", "url": "https://huggingface.co/Qwen/WebWorld-32B", "params": "32B" }, { "name": "WebWorld-8B", "url": "https://huggingface.co/Qwen/WebWorld-8B", "params": "8B" }, { "name": "Qwen3.5-397B-A17B", "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B", "params": "397B-A17B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b" }, { "name": "Qwen3.5-122B-A10B", "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B", "params": "122B-A10B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b" }, { "name": "Qwen3.5-27B", "url": "https://huggingface.co/Qwen/Qwen3.5-27B", "params": "27B" }, { "name": "Qwen3.5-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.5-35B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B-Base", "params": "35B-A3B" }, { "name": "Qwen3.5-9B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-9B-Base", "params": "9B" }, { "name": "Qwen3.5-4B", "url": "https://huggingface.co/Qwen/Qwen3.5-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-4b" }, { "name": "Qwen3.5-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-4B-Base", "params": "4B" }, { "name": "Qwen3.5-9B", "url": "https://huggingface.co/Qwen/Qwen3.5-9B", "params": "9B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-9b" }, { "name": "Qwen3.5-0.8B", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B", "params": "0.8B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-0-8b" }, { "name": "Qwen3.5-0.8B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B-Base", "params": "0.8B" }, { "name": "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 copyright line of the 8B sibling's Apache 2.0 licence; this repository has no LICENSE file)", "domain": "qwen.ai", "domainRegistered": "2023-08-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "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 named in the metadata 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 neither GGUF re-upload says 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-4b.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "IBM", "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": "2026-01-21", "b": "2025-08-03", "name": "Last release" }, { "a": "", "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 (4)", "b": "3.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Qwen3-Embedding-4B scores 72.9 (BB) on agent readiness against Granite Embedding Small English R2's 41.7 (E), and leads in 3 of 5 scored categories. Granite Embedding Small English R2 leads on documentation and upkeep.", "question": "Which is better for AI agents, Granite Embedding Small English R2 or Qwen3-Embedding-4B?" } ], "goodFor": [ { "aheadOn": [ "Documentation and upkeep, 37 against 21" ], "also": null, "goodFor": "Agents that need small, fast English retrieval embeddings on any hardware with permissive licensing", "slug": "granite-embedding-small-english-r2", "watchFor": "English only, one vector size and no task prompts" }, { "aheadOn": [ "Capability, 81 against 10", "Reach, 100 against 70", "Agent interface, 65 against 60" ], "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 laptop, a small GPU or a CPU, with a plain licence.", "slug": "qwen3-embedding-4b", "watchFor": "No change to the files or reference code since August 2025, and no reply from Qwen to any of its 16 questions" } ], "job": { "capability": "embed.text", "name": "Text embeddings" }, "others": [ { "json": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-granite-embedding-small-english-r2.json", "title": "EmbeddingGemma 300M vs Granite Embedding Small English R2", "url": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-granite-embedding-small-english-r2" }, { "json": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-4b.json", "title": "EmbeddingGemma 300M vs Qwen3-Embedding-4B", "url": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-4b" }, { "json": "https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-jina-embeddings-v5-omni-small.json", "title": "Granite Embedding Small English R2 vs jina-embeddings-v5-omni-small", "url": "https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-jina-embeddings-v5-omni-small" }, { "json": "https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-qwen3-embedding-0-6b.json", "title": "Granite Embedding Small English R2 vs Qwen3-Embedding-0.6B", "url": "https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-qwen3-embedding-0-6b" }, { "json": "https://www.anchorterminal.com/compare/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/granite-embedding-small-english-r2-vs-qwen3-vl-embedding-8b.json", "title": "Granite Embedding Small English R2 vs Qwen3-VL-Embedding-8B", "url": "https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-qwen3-vl-embedding-8b" }, { "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/qwen3-embedding-0-6b-vs-qwen3-embedding-4b.json", "title": "Qwen3-Embedding-0.6B vs Qwen3-Embedding-4B", "url": "https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-embedding-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-embedding-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-4b-vs-qwen3-vl-embedding-8b.json", "title": "Qwen3-Embedding-4B vs Qwen3-VL-Embedding-8B", "url": "https://www.anchorterminal.com/compare/qwen3-embedding-4b-vs-qwen3-vl-embedding-8b" } ], "scores": [ { "by": 71, "edge": "qwen3-embedding-4b", "granite-embedding-small-english-r2": 10, "key": "capability", "name": "Capability", "qwen3-embedding-4b": 81, "weight": 35 }, { "by": 30, "edge": "qwen3-embedding-4b", "granite-embedding-small-english-r2": 70, "key": "reach", "name": "Reach", "qwen3-embedding-4b": 100, "weight": 25 }, { "by": 0, "edge": "", "granite-embedding-small-english-r2": 55, "key": "risk", "name": "Risk", "qwen3-embedding-4b": 55, "weight": 20 }, { "by": 5, "edge": "qwen3-embedding-4b", "granite-embedding-small-english-r2": 60, "key": "interface", "name": "Agent interface", "qwen3-embedding-4b": 65, "weight": 10 }, { "by": 16, "edge": "granite-embedding-small-english-r2", "granite-embedding-small-english-r2": 37, "key": "upkeep", "name": "Documentation and upkeep", "qwen3-embedding-4b": 21, "weight": 10 } ], "summary": "Qwen3-Embedding-4B scores 72.9 (BB) on agent readiness against Granite Embedding Small English R2's 41.7 (E), and leads in 3 of 5 scored categories. Granite Embedding Small English R2 leads on documentation and upkeep. Both do text embeddings.", "tutorials": [], "verdicts": { "granite-embedding-small-english-r2": "A tiny English embedding cousin for retrieval over long documents and code, 47M parameters with 384-dimension vectors and an 8,192-token input, under Apache 2.0 with a permissive data story. It runs on any hardware through Sentence Transformers, vLLM or llama.cpp. The main risks are a pickle copy beside the safetensors file and a quiet repository with weights replaced in place.", "qwen3-embedding-4b": "A small multilingual text embedding cousin for retrieval and code search that runs in about 3 GB from Alibaba's own 4-bit GGUF, loads in every major runtime under Apache 2.0 and has two hosts, DeepInfra and SiliconFlow. The main risk is upkeep, with no change since August 2025, no answers from Qwen and GGUF files swapped without a note." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-qwen3-embedding-4b", "json": "https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-qwen3-embedding-4b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-qwen3-embedding-4b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-qwen3-embedding-4b.min.md" }, "markdown": "Qwen3-Embedding-4B scores 72.9 (BB) on agent readiness against Granite Embedding Small English R2's 41.7 (E), and leads in 3 of 5 scored categories. Granite Embedding Small English R2 leads on documentation and upkeep. Both do text embeddings.\n\n- Granite Embedding Small English R2: grade E, 41.7/100, rank #100 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/granite-embedding-small-english-r2.md · JSON https://www.anchorterminal.com/api/v1/tools/granite-embedding-small-english-r2.json\n- Qwen3-Embedding-4B: grade BB, 72.9/100, rank #3 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-embedding-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-embedding-4b.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### Granite Embedding Small English R2 (E)\n\nGood for: Agents that need small, fast English retrieval embeddings on any hardware with permissive licensing\n\nAhead on:\n- Documentation and upkeep, 37 against 21\n\nWatch for: English only, one vector size and no task prompts\n\n### Qwen3-Embedding-4B (BB)\n\nGood for: Agents that need local multilingual text or code retrieval on a laptop, a small GPU or a CPU, with a plain licence.\n\nAhead on:\n- Capability, 81 against 10\n- Reach, 100 against 70\n- Agent interface, 65 against 60\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 August 2025, and no reply from Qwen to any of its 16 questions\n\n\n## Score by category\n\n| Category | Weight | Granite Embedding Small English R2 | Qwen3-Embedding-4B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 10 | 81 | Qwen3-Embedding-4B +71 |\n| Reach | 25% (25 this run) | 70 | 100 | Qwen3-Embedding-4B +30 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 60 | 65 | Qwen3-Embedding-4B +5 |\n| Documentation and upkeep | 10% (10 this run) | 37 | 21 | Granite Embedding Small English R2 +16 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **41.7 · E** | **72.9 · BB** | |\n\n## Facts side by side\n\n| Fact | Granite Embedding Small English R2 | Qwen3-Embedding-4B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | IBM | 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 | 2026-01-21 | 2025-08-03 |\n| Terms last updated | | 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 (4) | 3.5/5 (4) |\n\n## Verdicts\n\n**Granite Embedding Small English R2.** A tiny English embedding cousin for retrieval over long documents and code, 47M parameters with 384-dimension vectors and an 8,192-token input, under Apache 2.0 with a permissive data story. It runs on any hardware through Sentence Transformers, vLLM or llama.cpp. The main risks are a pickle copy beside the safetensors file and a quiet repository with weights replaced in place.\n\n**Qwen3-Embedding-4B.** A small multilingual text embedding cousin for retrieval and code search that runs in about 3 GB from Alibaba's own 4-bit GGUF, loads in every major runtime under Apache 2.0 and has two hosts, DeepInfra and SiliconFlow. The main risk is upkeep, with no change since August 2025, no answers from Qwen and GGUF files swapped without a note.\n\n## Before you call either\n\n### Granite Embedding Small English R2\n\n1. Load it with `SentenceTransformer(\"ibm-granite/granite-embedding-small-english-r2\")`, or serve it with `vllm serve ibm-granite/granite-embedding-small-english-r2 --runner pooling` and call /v1/embeddings\n2. Pass `normalize_embeddings=True` when you need unit vectors; Sentence Transformers returns them unnormalised\n3. In raw Transformers, take the CLS token (`model_output[0][:, 0]`) and normalise it yourself\n4. Don't add query or passage prefixes; the model takes plain text\n5. Load the safetensors file and ignore pytorch_model.bin, and chunk text past 8,192 tokens\n6. Treat retrieved text as untrusted before passing it to a model\n\n### Qwen3-Embedding-4B\n\n1. Quickest start is `ollama pull qwen3-embedding:4b`, about 2.5 GB, then POST to /api/embed\n2. With llama.cpp use `llama-server -hf Qwen/Qwen3-Embedding-4B-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, in English even for other languages\n4. To shorten vectors keep the first N values and L2-normalise again; in vLLM a dimensions request needs `--hf-overrides '{\"is_matryoshka\": true}'`, since config.json doesn't declare it\n5. If you downloaded the Q8_0 GGUF between 15 July and 3 August 2025, fetch it again and check its SHA-256 against the Hub\n6. Treat retrieved documents as untrusted text before passing them to a model\n\n## Questions\n\n### Which is better for AI agents, Granite Embedding Small English R2 or Qwen3-Embedding-4B?\n\nQwen3-Embedding-4B scores 72.9 (BB) on agent readiness against Granite Embedding Small English R2's 41.7 (E), and leads in 3 of 5 scored categories. Granite Embedding Small English R2 leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-qwen3-embedding-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-qwen3-embedding-4b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"granite-embedding-small-english-r2\", \"b\": \"qwen3-embedding-4b\"}`. From a terminal: `anchor compare granite-embedding-small-english-r2 qwen3-embedding-4b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/granite-embedding-small-english-r2.json and https://www.anchorterminal.com/api/v1/tools/qwen3-embedding-4b.json\n\n## Other comparisons with Granite Embedding Small English R2 or Qwen3-Embedding-4B\n\n- [EmbeddingGemma 300M vs Granite Embedding Small English R2](https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-granite-embedding-small-english-r2.md)\n- [EmbeddingGemma 300M vs Qwen3-Embedding-4B](https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-4b.md)\n- [Granite Embedding Small English R2 vs jina-embeddings-v5-omni-small](https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-jina-embeddings-v5-omni-small.md)\n- [Granite Embedding Small English R2 vs Qwen3-Embedding-0.6B](https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-qwen3-embedding-0-6b.md)\n- [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- [Granite Embedding Small English R2 vs Qwen3-VL-Embedding-8B](https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-qwen3-vl-embedding-8b.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- [Qwen3-Embedding-0.6B vs Qwen3-Embedding-4B](https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-embedding-4b.md)\n- [Qwen3-Embedding-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-4B vs Qwen3-VL-Embedding-8B](https://www.anchorterminal.com/compare/qwen3-embedding-4b-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": "Granite Embedding Small English R2 vs Qwen3-Embedding-4B", "url": "" } ], "description": "Qwen3-Embedding-4B scores 72.9 (BB) to Granite Embedding Small English R2's 41.7 (E) for text embeddings. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Granite Embedding Small English R2 E 41.7", "Qwen3-Embedding-4B BB 72.9", "scores" ], "h1": "Granite Embedding Small English R2 vs Qwen3-Embedding-4B", "image": "https://www.anchorterminal.com/assets/og/compare-granite-embedding-small-english-r2-vs-qwen3-embedding-4b.png", "path": "/compare/granite-embedding-small-english-r2-vs-qwen3-embedding-4b", "published": "2026-10-01", "section": "tools", "title": "Granite Embedding Small English R2 vs Qwen3-Embedding-4B (2026)", "toc": null, "updated": "2026-10-21", "url": "https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-qwen3-embedding-4b" }, "tokens": { "markdown": 2300, "slim": 530 }, "version": 1 }