{ "data": { "a": { "slug": "exaone-4-5-33b", "name": "EXAONE 4.5 33B", "vendor": "LG AI Research", "vendorUrl": "https://www.lgresearch.ai", "kind": "weights", "category": "open-weights", "summary": "EXAONE 4.5 33B is LG AI Research's open-weight vision-language model, 34B parameters, with tool calling, switchable reasoning and a 256K context, strong on documents and Korean. Its licence allows research and education only, with LG's own GGUF from 18 GB.", "url": "https://www.anchorterminal.com/tools/exaone-4-5-33b", "markdownUrl": "https://www.anchorterminal.com/tools/exaone-4-5-33b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/exaone-4-5-33b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/exaone-4-5-33b.json", "dns": "exaone-4-5-33b.grade.anchorterminal.com", "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B", "license": "EXAONE AI Model License Agreement 1.2 - NC (research and education only)", "transports": [], "packages": [ { "registry": "huggingface", "name": "LGAI-EXAONE/EXAONE-4.5-33B" }, { "registry": "huggingface", "name": "LGAI-EXAONE/EXAONE-4.5-33B-GGUF" }, { "registry": "huggingface", "name": "LGAI-EXAONE/EXAONE-4.5-33B-AWQ" }, { "registry": "huggingface", "name": "LGAI-EXAONE/EXAONE-4.5-33B-FP8" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account or token. None of the four repositories is gated, though the licence says downloading is agreeing to it (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download for research and education. The EXAONE AI Model License Agreement 1.2 - NC prohibits commercial use of the model, its derivatives and its output without a separate agreement with LG. Running it yourself takes 20.6 GB at IQ4_XS with its projector, up to 68.7 GB in BF16 (checked 2026-11-10).", "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 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://github.com/LG-AI-EXAONE/EXAONE-4.5", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "multimodal", "vision", "documents", "korean", "reasoning", "tool-calling", "long-context", "non-commercial", "safetensors", "gguf", "awq", "fp8", "self-hosted" ], "lastRelease": "2026-07-06", "graded": true, "anchor": { "graded": true, "score": 61, "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": 1518, "categoryRank": 51, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 85, "reach": 65, "risk": 50, "upkeep": 50 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A well-documented vision-language model with a full set of builds, tool calling, switchable reasoning, a 256K context and a stated knowledge cut-off. The licence decides whether you can use it: research and education only, output included, and LG can change the terms. Nothing answered on the repository and no safety results.", "bestFor": "Researchers and students working on documents, Korean or multimodal agents who don't need commercial rights", "strengths": [ "A complete card, down to a knowledge cut-off", "GGUF, AWQ and FP8 builds from LG", "Released vLLM, Transformers and llama.cpp run it with no repository code", "Tool calling and reasoning control documented with parsers", "A 256K context, served on one large GPU" ], "weaknesses": [ "Research and education only. No commercial use of the model or its output", "LG can amend the licence at any time, and it's revocable", "No building models that compete with LG's", "No reply from LG on the repository's discussions", "Not changed since the summer", "No safety results or training data sources on the card" ], "agentNotes": [ "Don't use it, or anything it writes, commercially. The licence covers the output too", "Serve it with vLLM and `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser hermes`", "Set `enable_thinking` false for a direct answer. Thinking is on by default", "Use temperature 1.0, top_p 0.95 and presence_penalty 1.5 in general, and temperature 0.6 with top_k 20 for documents and Korean", "For llama.cpp, load a projector file from LG's GGUF repository with the weights for image input", "Its knowledge stops at December 2024" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 85, "reach": 65, "risk": 50, "upkeep": 50 }, "provenanceScore": 49, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install \"vllm\u003e=0.33.0\" \"transformers\u003e=5.8.0\"\nvllm serve LGAI-EXAONE/EXAONE-4.5-33B --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser hermes", "http": "# vLLM serves an OpenAI-compatible API at http://localhost:8000/v1. The card recommends temperature 1.0,\n# top_p 0.95 and presence_penalty 1.5, and enable_thinking false for a direct answer" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/exaone-4-5-33b" }, "weights": { "trainedBy": "LG AI Research", "params": "34B", "context": 262144, "goodFor": "Research on document understanding, Korean and agent work with images, where non-commercial terms are acceptable.", "runtimes": [ "llama.cpp", "vLLM", "Transformers" ], "builds": [ { "name": "EXAONE-4.5-33B-GGUF IQ4_XS", "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B-GGUF/blob/main/EXAONE-4.5-33B-IQ4_XS.gguf", "who": "publisher", "precision": "iq4_xs", "sizeGB": 20.6, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "18.03 GB with the 2.57 GB F16 projector" }, { "name": "EXAONE-4.5-33B-GGUF Q4_K_M", "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B-GGUF/blob/main/EXAONE-4.5-33B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 22.62, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "20.05 GB with the 2.57 GB F16 projector" }, { "name": "EXAONE-4.5-33B-AWQ", "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B-AWQ", "who": "publisher", "precision": "awq", "sizeGB": 22.75, "memoryGB": 32, "format": "safetensors", "runsOn": [ "vLLM" ], "note": "LG's 4-bit AWQ build" }, { "name": "EXAONE-4.5-33B-GGUF Q8_0", "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B-GGUF/blob/main/EXAONE-4.5-33B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 37.71, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "35.14 GB with the 2.57 GB F16 projector" }, { "name": "EXAONE-4.5-33B-FP8", "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 41.31, "memoryGB": 64, "format": "safetensors", "runsOn": [ "vLLM" ], "note": "LG's FP8 build" }, { "name": "EXAONE-4.5-33B (BF16 safetensors)", "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B", "who": "publisher", "precision": "bf16", "sizeGB": 68.7, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM", "Transformers" ], "note": "Two shards" } ], "run": "llama-server -hf LGAI-EXAONE/EXAONE-4.5-33B-GGUF:Q4_K_M --jinja", "runBuild": "EXAONE-4.5-33B-GGUF Q4_K_M", "checked": "2026-11-10", "minMemoryGB": 32 }, "family": { "lab": "LG AI Research", "family": "EXAONE 4", "url": "https://www.anchorterminal.com/open-weights/#lg-ai-research", "others": [ { "name": "EXAONE-4.0.1-32B", "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.0.1-32B" }, { "name": "EXAONE-4.0-1.2B", "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-1.2B" } ] }, "area": "models", "provenance": { "legalEntity": "LG Management Development Institute Co., Ltd., the licensor named in the model's licence. The card names LG AI Research", "domain": "lgresearch.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B/blob/main/LICENSE", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The card and the Hub organisation name LG AI Research, and the licence names LG Management Development Institute Co., Ltd. as licensor. How the two relate wasn't read first-hand.", "www.lgresearch.ai's robots.txt allows reading. Its home page gave little text and its about page a 404, so no people or ownership facts were taken from it.", "The domain's registration date wasn't looked up. www.lgresearch.ai has no security.txt (404 on 10 November 2026).", "Nothing is hosted under this listing, so the model licence stands in for terms of service and no privacy policy is scored.", "The changelog is the commit history, 8 commits from 9 April to 6 July 2026." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/exaone-4-5-33b.json" }, "answer": "EXAONE 4.5 33B and Qwen3.5-9B score within a point of each other on agent readiness, 61 (C) and 60.5 (C). Qwen3.5-9B leads on reach and risk.", "b": { "slug": "qwen3-5-9b", "name": "Qwen3.5-9B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3.5-9B is Alibaba's open-weight dense vision-language model of 9B parameters, with thinking on by default and a 262,144-token context, published on Hugging Face in BF16 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-5-9b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-9b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-9b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-5-9b.json", "dns": "qwen3-5-9b.grade.anchorterminal.com", "repo": "https://huggingface.co/Qwen/Qwen3.5-9B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3.5-9B" }, { "registry": "ollama", "name": "qwen3.5:9b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not 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, 19.3 GB for the BF16 files and 6.6 GB for Ollama's default build (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/Qwen/Qwen3.5-9B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "reasoning", "tool-calling", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-03-02", "graded": true, "anchor": { "graded": true, "score": 60.5, "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": 1518, "categoryRank": 69, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 51, "interface": 70, "reach": 85, "risk": 55, "upkeep": 34 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-04" }, "negative": 0, "verdict": "A small dense vision-language model with trained tool calling, thinking that turns off per request and a 262,144-token context. Ollama's 4-bit build is 6.6 GB and the BF16 files 19.3 GB, and every major runtime loads it. The files haven't changed since 2 March 2026, Qwen answered none of the 40 latest discussions, and no safety results are published.", "bestFor": "Agents that want a small multimodal tool-calling model on a laptop or a 16 GB GPU, under a plain licence", "strengths": [ "Runs in vLLM, SGLang, llama.cpp, Ollama, LM Studio and released Transformers", "Ollama's 4-bit build is 6.6 GB and the BF16 files 19.3 GB", "Plain Apache 2.0, no gate, no account", "262,144 tokens native, and thinking that turns off per request", "Safetensors only, with a SHA-256 for every shard and a chat template in the repository", "Four inference providers serve it for agents without the hardware" ], "weaknesses": [ "No quantised build from Qwen itself", "No safety evaluation, agent guidance or security contact", "No reply from Qwen in any of the 40 most recent discussions", "No change to the files or card since 2 March 2026", "The tool-call format and structured output aren't documented beyond the template", "No limitations, knowledge cut-off or training data detail on the card" ], "agentNotes": [ "Smallest start is `ollama run qwen3.5:9b` (6.6 GB, q4_k_m)", "On vLLM run `vllm serve Qwen/Qwen3.5-9B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, or tool calls come back as text", "Tool calls use the XML style `\u003ctool_call\u003e\u003cfunction=name\u003e\u003cparameter=x\u003e`, not Hermes JSON, so use the qwen3_coder parser and not hermes", "Thinking is on by default; pass `chat_template_kwargs` with `enable_thinking` false for short tasks, since `/nothink` isn't supported", "There's no generation_config.json, so pass sampling yourself, temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5 when thinking, or 0.7, 0.8, 20, 1.5 without", "Keep at least 128K of context when thinking, and turn on YaRN only beyond 262,144 tokens", "Validate JSON output yourself or use the runtime's guided decoding" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 60.5 } ], "editorialScores": { "capability": 51, "interface": 70, "reach": 85, "risk": 55, "upkeep": 34 }, "provenanceScore": 54, "checked": { "categories": { "capability": "2026-11-04", "interface": "2026-11-04", "reach": "2026-11-04", "risk": "2026-11-04", "upkeep": "2026-11-04" }, "last": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run qwen3.5:9b\n# or, with the card's vLLM command:\nvllm serve Qwen/Qwen3.5-9B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or, with llama.cpp and LM Studio's GGUF:\nllama-server -hf lmstudio-community/Qwen3.5-9B-GGUF:Q4_K_M --jinja", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"qwen3.5:9b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen3-5-9b" }, "sameCompany": [ "qwen3-embedding-0-6b", "qwen3-embedding-4b", "qwen3-embedding-8b", "qwen3-vl-embedding-2b", "qwen3-vl-embedding-8b", "qwen3-asr-0-6b", "qwen3-asr-1-7b", "qwen3-tts-1-7b-customvoice", "qwen-agentworld-35b-a3b", "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", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "builtOn": "Qwen/Qwen3.5-9B-Base", "params": "9B", "context": 262144, "goodFor": "Dense vision-language model for local agents: text, images and video in, tool calling, thinking on by default.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "ollama qwen3.5:9b", "url": "https://ollama.com/library/qwen3.5:9b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 6.6, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M with a BF16 vision projector (digest 56671c2ab938), also the smallest non-MLX Ollama build. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" }, { "name": "lmstudio-community Qwen3.5-9B-GGUF (Q4_K_M)", "url": "https://huggingface.co/lmstudio-community/Qwen3.5-9B-GGUF", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 6.55, "memoryGB": 8, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "From lmstudio-community, LM Studio's organisation on the Hub. 5.63 GB of Q4_K_M weights plus the 0.92 GB BF16 vision projector, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet" }, { "name": "lmstudio-community Qwen3.5-9B-GGUF (Q6_K)", "url": "https://huggingface.co/lmstudio-community/Qwen3.5-9B-GGUF", "who": "lmstudio", "precision": "q6_k", "sizeGB": 8.28, "memoryGB": 12, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "From lmstudio-community, LM Studio's organisation on the Hub. 7.36 GB of Q6_K weights plus the 0.92 GB BF16 vision projector, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet" }, { "name": "lmstudio-community Qwen3.5-9B-GGUF (Q8_0)", "url": "https://huggingface.co/lmstudio-community/Qwen3.5-9B-GGUF", "who": "lmstudio", "precision": "q8_0", "sizeGB": 10.45, "memoryGB": 16, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "From lmstudio-community, LM Studio's organisation on the Hub. 9.53 GB of Q8_0 weights plus the 0.92 GB BF16 vision projector, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet" }, { "name": "Qwen3.5-9B", "url": "https://huggingface.co/Qwen/Qwen3.5-9B", "who": "publisher", "precision": "bf16", "sizeGB": 19.3, "memoryGB": 24, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve Qwen/Qwen3.5-9B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "Qwen3.5-9B", "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-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": "Qwen3.5-0.8B", "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, "unrestrictedCopies": 5 }, "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 2026 Alibaba Cloud. The registrant (Alibaba Cloud (Singapore) Private Limited) and registration date of qwen.ai are per the RDAP reading of 20 October 2026; rdap.org didn't answer on 21 October.", "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 that don't say what changed.", "qwen.ai redirects /.well-known/security.txt and /robots.txt to its home page, so there is no security.txt." ], "score": 54 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-5-9b.json", "live": { "slug": "qwen3-5-9b", "securityTxt": { "url": "https://qwen.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:09:18.413964975Z" }, "domain": { "domain": "qwen.ai", "registered": "2023-08-27", "source": "https://rdap.identitydigital.services/rdap/domain/qwen.ai", "checkedAt": "2026-11-08T15:07:12.468678256Z" }, "updatedAt": "2026-11-10T16:09:18.413964975Z" } }, "edges": [ { "by": 20, "category": "reach", "leads": "qwen3-5-9b", "name": "Reach", "reasons": { "a": "Memory 20, the smallest build at 4 bits is LG's IQ4_XS GGUF, 18.03 GB with the 2.57 GB projector, and 20.60 GB × 1.2 = 24.7 GB, within 32 GB. Without the projector, for text only, it would fit 24 GB. Runtimes 15, Released vLLM 0.33.0 has exaone4_5.py, Transformers 5.22.2 has the exaone4_5 model, and llama.cpp b12242 has the architecture, with LG's own GGUF. Three at 5 each. Builds 15, LG publishes GGUF at five quantised precisions, an AWQ build and an FP8 build. Terms 5, Research and educational use only. Download 10, the Hub API reports gated false for all four repositories on 10 November 2026, so the files download with no account and no click-through, though the licence says downloading is acceptance. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API that serves it. 20 + 15 + 15 + 5 + 10 + 0 = 65.", "b": "Memory 30, Qwen publishes no quantised 9B, so the smallest 4-bit build from a runtime's own library is Ollama's qwen3.5:9b at 6.6 GB, about 7.9 GB with the fifth added, well under 16 GB. Runtimes 25, the cap, with vLLM listed in its stable docs, Transformers at released tags, llama.cpp, Ollama's library, LM Studio's own catalogue and SGLang all running the architecture. Builds 0, Qwen's FP8 and GPTQ repositories cover only the 27B and larger Qwen3.5 models, so every quantisation of the 9B is someone else's. Terms 15, plain Apache 2.0. Download 10, the repository isn't gated. Hosted 5, Together, Featherless AI, OVHcloud and DeepInfra are live for it on its Hugging Face page. 30 + 25 + 0 + 15 + 10 + 5 = 85." }, "scores": { "a": 65, "b": 85 } }, { "by": 16, "category": "upkeep", "leads": "exaone-4-5-33b", "name": "Documentation and upkeep", "reasons": { "a": "Model card 20, Uses (the quickstart's question answering, document and agent examples), limitations, the architecture, the parameters, the context, the languages (in the metadata) and a knowledge cut-off of December 2024 are all on the card. Training data 5, A knowledge cut-off and one line on excluding personal, harmful and biased content, with no sources or sizes. Last change 10, the main repository last changed on 6 July 2026 (dependencies moved to official releases), 127 days before this check, and the FP8 repository on 9 August, 93 days. Responsiveness 0, none of the 5 most recent discussions that aren't pull requests (13 April to 4 May 2026) has a reply from an LGAI-EXAONE member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, EXAONE 4.0 and 4.0.1 stay in their own repositories and 4.5 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the 262,144 tokens, and a July commit moved the dependencies to released versions. 20 + 5 + 10 + 0 + 5 + 10 = 50.", "b": "Model card 14, it covers intended use, architecture, parameters, context and 201 languages but gives no limitations and no knowledge cut-off. Training data 5, a general line about trillions of multimodal tokens with no cut-off. Last change 5, the files and card last changed on 2 March 2026 (c202236), 247 days before 4 November, within a year. Responsiveness 0, no Qwen member replied in any of the 40 most recent of the 45 discussions that aren't pull requests (2 March to 29 September 2026). Version policy 5, a first release that was never replaced in place, with no dated notes across generations. File health 5, two reports about the files and card (15 and 51) are still open with no reply. 14 + 5 + 5 + 0 + 5 + 5 = 34. The card's highlights mention sparse Mixture-of-Experts for the series while this 9B is dense, but its own overview matches config.json, so it's not counted as a contradiction." }, "scores": { "a": 50, "b": 34 } }, { "by": 15, "category": "interface", "leads": "exaone-4-5-33b", "name": "Agent interface", "reasons": { "a": "Chat template 20, chat_template.jinja in the repository (5,930 characters). Tool-calling format 25, the card's vLLM command names the parser, hermes with auto tool choice, and its agentic example calls tools from MCP servers through the OpenAI-compatible API, so declaration and return follow that API. Trained tool calling 20, the card reports agentic tool use among its language results and shows an agent run with several tool servers, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking is on by default and the card says to set it false for the non-reasoning mode, with an example of each. Context 10, 262,144 tokens per the card's configuration table and config.json, and the card's serve commands run at that length. 20 + 25 + 20 + 0 + 10 + 10 = 85.", "b": "Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card names the qwen3_coder parser for vLLM and SGLang but leaves the declaration and return format to the template, and Qwen's own function-calling guide still describes Qwen3's Hermes format. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, nothing documents asking the weights for JSON. Reasoning control 10, thinking turns off per request with enable_thinking false and the reasoning comes back in think tags that the qwen3 parser splits out. Context 10, 262,144 tokens native." }, "scores": { "a": 85, "b": 70 } }, { "by": 5, "category": "risk", "leads": "qwen3-5-9b", "name": "Risk", "reasons": { "a": "File safety 25, safetensors and GGUF only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the LGAI-EXAONE organisation (LG AI Research on the Hub), which the card's GitHub repository shares a name with and the licence in the repository is LG's (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions: the limitation section lists inappropriate, biased, incorrect and outdated answers and tells users not to try to induce harmful output. Licence risk 5, 15 less 5 for field-of-use limits beyond the law (no commercial use, no building competing models) and 5 for terms the licensor can change or revoke (LG may amend the agreement at any time, and the licence is revocable). Security programme 0, no way to report a flaw on the card beyond a support contact, and www.lgresearch.ai has no security.txt. 25 + 15 + 0 + 5 + 5 + 0 = 50.", "b": "File safety 25, safetensors only and nothing from the repository runs on load or to build prompts. Provenance 15, the Qwen organisation linked from Alibaba's QwenLM README, with a SHA-256 for every shard. Safety evaluation 0, none on the card, and the Qwen3.5 blog couldn't be read, so this is couldn't check scored as absent. Agent guidance 0, the card has no prompt-injection guidance, refusal scope or cautions. Licence risk 15, Apache 2.0 with nothing added and a working licence link. Security programme 0, no SECURITY.md in Qwen's GitHub repositories, no security.txt on qwen.ai or alibabacloud.com, and whether Alibaba's group security centre takes reports about Qwen models couldn't be checked." }, "scores": { "a": 50, "b": 55 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "LG AI Research", "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": "EXAONE AI Model License Agreement 1.2 - NC (research and education only)", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-07-06", "b": "2026-03-02", "name": "Last release" }, { "a": "not read yet", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "none", "b": "2.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "EXAONE 4.5 33B and Qwen3.5-9B score within a point of each other on agent readiness, 61 (C) and 60.5 (C). Qwen3.5-9B leads on reach and risk.", "question": "Which is better for AI agents, EXAONE 4.5 33B or Qwen3.5-9B?" } ], "goodFor": [ { "aheadOn": [ "Agent interface, 85 against 70", "Documentation and upkeep, 50 against 34" ], "also": null, "goodFor": "Researchers and students working on documents, Korean or multimodal agents who don't need commercial rights", "slug": "exaone-4-5-33b", "watchFor": "Research and education only. No commercial use of the model or its output" }, { "aheadOn": [ "Reach, 85 against 65", "Risk, 55 against 50" ], "also": null, "goodFor": "Agents that want a small multimodal tool-calling model on a laptop or a 16 GB GPU, under a plain licence", "slug": "qwen3-5-9b", "watchFor": "No quantised build from Qwen itself" } ], "history": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-exaone-4-5-33b.json", "title": "Agents-A1-4B vs EXAONE 4.5 33B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-exaone-4-5-33b" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-9b.json", "title": "Agents-A1-4B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-exaone-4-5-33b.json", "title": "Apertus v1.5 8B vs EXAONE 4.5 33B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-exaone-4-5-33b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-9b.json", "title": "Apertus v1.5 8B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-exaone-4-5-33b.json", "title": "Apodex-1.1-mini vs EXAONE 4.5 33B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-exaone-4-5-33b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-9b.json", "title": "Apodex-1.1-mini vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-exaone-4-5-33b.json", "title": "Arete-27B vs EXAONE 4.5 33B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-exaone-4-5-33b" }, { "json": 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"title": "Devstral Small 2 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-exaone-4-5-33b.json", "title": "DiffusionGemma 26B A4B IT vs EXAONE 4.5 33B", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-exaone-4-5-33b" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-5-9b.json", "title": "DiffusionGemma 26B A4B IT vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-gemma-4-12b-it.json", "title": "EXAONE 4.5 33B vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-gemma-4-12b-it" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-gemma-4-26b-a4b-it.json", "title": "EXAONE 4.5 33B vs Gemma 4 26B A4B IT", "url": 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"https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-glm-4-7-flash.json", "title": "EXAONE 4.5 33B vs GLM-4.7-Flash", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-glm-4-7-flash" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-glm-5-3.json", "title": "EXAONE 4.5 33B vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-glm-5-3" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-glm-5-3-flash.json", "title": "EXAONE 4.5 33B vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-gpt-oss-120b.json", "title": "EXAONE 4.5 33B vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-gpt-oss-120b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-gpt-oss-20b.json", "title": "EXAONE 4.5 33B vs gpt-oss-20b", "url": 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33B vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-laguna-xs-2-1.json", "title": "EXAONE 4.5 33B vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lbs-27b.json", "title": "EXAONE 4.5 33B vs LBS-27B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm2-5-2-6b.json", "title": "EXAONE 4.5 33B vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm3-1-7b.json", "title": "EXAONE 4.5 33B vs LFM3-1.7B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm3-1-7b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm3-audio-2b.json", "title": "EXAONE 4.5 33B vs LFM3-Audio-2B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm3-audio-2b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm3-omni-2b.json", "title": "EXAONE 4.5 33B vs LFM3-Omni-2B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm3-omni-2b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm3-vl-2b.json", "title": "EXAONE 4.5 33B vs LFM3-VL-2B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm3-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ling-3-0-tiny.json", "title": "EXAONE 4.5 33B vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ling-3-1-flash-1019.json", "title": "EXAONE 4.5 33B vs Ling-3.1-flash-1019", "url": 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Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-5-9b.json", "title": "Qwen3.5-397B-A17B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-5-9b.json", "title": "Qwen3.5-4B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-6-27b.json", "title": "Qwen3.5-9B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-6-35b-a3b.json", "title": "Qwen3.5-9B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-8-27b.json", "title": "Qwen3.5-9B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-8-flash-next.json", "title": "Qwen3.5-9B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Qwen3.5-9B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-coder-next.json", "title": "Qwen3.5-9B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Qwen3.5-9B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-2b-instruct.json", "title": "Qwen3.5-9B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3.5-9B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3.5-9B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.5-9B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3.5-9B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.5-9B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-recursive-flint.json", "title": "Qwen3.5-9B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-ripple-65b-preview.json", "title": "Qwen3.5-9B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-sensenova-loop-9b-preview.json", "title": "Qwen3.5-9B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-smollm3-3b.json", "title": "Qwen3.5-9B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-spark-x2-6-4b.json", "title": "Qwen3.5-9B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-step-3-7-flash.json", "title": "Qwen3.5-9B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3.5-9B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.5-9B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.5-9B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-unlimited-ocr-2.json", "title": "Qwen3.5-9B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-2b.json", "title": "Qwen3.5-9B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-4b.json", "title": "Qwen3.5-9B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-zaya1-8b.json", "title": "Qwen3.5-9B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 20, "edge": "qwen3-5-9b", "exaone-4-5-33b": 65, "key": "reach", "name": "Reach", "qwen3-5-9b": 85, "weight": 25 }, { "by": 5, "edge": "qwen3-5-9b", "exaone-4-5-33b": 50, "key": "risk", "name": "Risk", "qwen3-5-9b": 55, "weight": 20 }, { "by": 15, "edge": "exaone-4-5-33b", "exaone-4-5-33b": 85, "key": "interface", "name": "Agent interface", "qwen3-5-9b": 70, "weight": 10 }, { "by": 16, "edge": "exaone-4-5-33b", "exaone-4-5-33b": 50, "key": "upkeep", "name": "Documentation and upkeep", "qwen3-5-9b": 34, "weight": 10 } ], "summary": "EXAONE 4.5 33B and Qwen3.5-9B score within a point of each other on agent readiness, 61 (C) and 60.5 (C). Qwen3.5-9B leads on reach and risk. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "exaone-4-5-33b": "A well-documented vision-language model with a full set of builds, tool calling, switchable reasoning, a 256K context and a stated knowledge cut-off. The licence decides whether you can use it: research and education only, output included, and LG can change the terms. Nothing answered on the repository and no safety results.", "qwen3-5-9b": "A small dense vision-language model with trained tool calling, thinking that turns off per request and a 262,144-token context. Ollama's 4-bit build is 6.6 GB and the BF16 files 19.3 GB, and every major runtime loads it. The files haven't changed since 2 March 2026, Qwen answered none of the 40 latest discussions, and no safety results are published." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-9b", "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-9b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-9b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-9b.min.md" }, "markdown": "EXAONE 4.5 33B and Qwen3.5-9B score within a point of each other on agent readiness, 61 (C) and 60.5 (C). Qwen3.5-9B leads on reach and risk. Both do open-weight models you run yourself.\n\n- EXAONE 4.5 33B: grade C, 61/100, rank #61 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/exaone-4-5-33b.md · JSON https://www.anchorterminal.com/api/v1/tools/exaone-4-5-33b.json\n- Qwen3.5-9B: grade C, 60.5/100, rank #87 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-9b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-9b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### EXAONE 4.5 33B (C)\n\nGood for: Researchers and students working on documents, Korean or multimodal agents who don't need commercial rights\n\nAhead on:\n- Agent interface, 85 against 70\n- Documentation and upkeep, 50 against 34\n\nWatch for: Research and education only. No commercial use of the model or its output\n\n### Qwen3.5-9B (C)\n\nGood for: Agents that want a small multimodal tool-calling model on a laptop or a 16 GB GPU, under a plain licence\n\nAhead on:\n- Reach, 85 against 65\n- Risk, 55 against 50\n\nWatch for: No quantised build from Qwen itself\n\n\n## Why the edge\n\n### Reach: Qwen3.5-9B +20\n\n- EXAONE 4.5 33B (65): Memory 20, the smallest build at 4 bits is LG's IQ4_XS GGUF, 18.03 GB with the 2.57 GB projector, and 20.60 GB × 1.2 = 24.7 GB, within 32 GB. Without the projector, for text only, it would fit 24 GB. Runtimes 15, Released vLLM 0.33.0 has exaone4_5.py, Transformers 5.22.2 has the exaone4_5 model, and llama.cpp b12242 has the architecture, with LG's own GGUF. Three at 5 each. Builds 15, LG publishes GGUF at five quantised precisions, an AWQ build and an FP8 build. Terms 5, Research and educational use only. Download 10, the Hub API reports gated false for all four repositories on 10 November 2026, so the files download with no account and no click-through, though the licence says downloading is acceptance. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API that serves it. 20 + 15 + 15 + 5 + 10 + 0 = 65.\n- Qwen3.5-9B (85): Memory 30, Qwen publishes no quantised 9B, so the smallest 4-bit build from a runtime's own library is Ollama's qwen3.5:9b at 6.6 GB, about 7.9 GB with the fifth added, well under 16 GB. Runtimes 25, the cap, with vLLM listed in its stable docs, Transformers at released tags, llama.cpp, Ollama's library, LM Studio's own catalogue and SGLang all running the architecture. Builds 0, Qwen's FP8 and GPTQ repositories cover only the 27B and larger Qwen3.5 models, so every quantisation of the 9B is someone else's. Terms 15, plain Apache 2.0. Download 10, the repository isn't gated. Hosted 5, Together, Featherless AI, OVHcloud and DeepInfra are live for it on its Hugging Face page. 30 + 25 + 0 + 15 + 10 + 5 = 85.\n\n### Documentation and upkeep: EXAONE 4.5 33B +16\n\n- EXAONE 4.5 33B (50): Model card 20, Uses (the quickstart's question answering, document and agent examples), limitations, the architecture, the parameters, the context, the languages (in the metadata) and a knowledge cut-off of December 2024 are all on the card. Training data 5, A knowledge cut-off and one line on excluding personal, harmful and biased content, with no sources or sizes. Last change 10, the main repository last changed on 6 July 2026 (dependencies moved to official releases), 127 days before this check, and the FP8 repository on 9 August, 93 days. Responsiveness 0, none of the 5 most recent discussions that aren't pull requests (13 April to 4 May 2026) has a reply from an LGAI-EXAONE member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, EXAONE 4.0 and 4.0.1 stay in their own repositories and 4.5 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the 262,144 tokens, and a July commit moved the dependencies to released versions. 20 + 5 + 10 + 0 + 5 + 10 = 50.\n- Qwen3.5-9B (34): Model card 14, it covers intended use, architecture, parameters, context and 201 languages but gives no limitations and no knowledge cut-off. Training data 5, a general line about trillions of multimodal tokens with no cut-off. Last change 5, the files and card last changed on 2 March 2026 (c202236), 247 days before 4 November, within a year. Responsiveness 0, no Qwen member replied in any of the 40 most recent of the 45 discussions that aren't pull requests (2 March to 29 September 2026). Version policy 5, a first release that was never replaced in place, with no dated notes across generations. File health 5, two reports about the files and card (15 and 51) are still open with no reply. 14 + 5 + 5 + 0 + 5 + 5 = 34. The card's highlights mention sparse Mixture-of-Experts for the series while this 9B is dense, but its own overview matches config.json, so it's not counted as a contradiction.\n\n### Agent interface: EXAONE 4.5 33B +15\n\n- EXAONE 4.5 33B (85): Chat template 20, chat_template.jinja in the repository (5,930 characters). Tool-calling format 25, the card's vLLM command names the parser, hermes with auto tool choice, and its agentic example calls tools from MCP servers through the OpenAI-compatible API, so declaration and return follow that API. Trained tool calling 20, the card reports agentic tool use among its language results and shows an agent run with several tool servers, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking is on by default and the card says to set it false for the non-reasoning mode, with an example of each. Context 10, 262,144 tokens per the card's configuration table and config.json, and the card's serve commands run at that length. 20 + 25 + 20 + 0 + 10 + 10 = 85.\n- Qwen3.5-9B (70): Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card names the qwen3_coder parser for vLLM and SGLang but leaves the declaration and return format to the template, and Qwen's own function-calling guide still describes Qwen3's Hermes format. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, nothing documents asking the weights for JSON. Reasoning control 10, thinking turns off per request with enable_thinking false and the reasoning comes back in think tags that the qwen3 parser splits out. Context 10, 262,144 tokens native.\n\n### Risk: Qwen3.5-9B +5\n\n- EXAONE 4.5 33B (50): File safety 25, safetensors and GGUF only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the LGAI-EXAONE organisation (LG AI Research on the Hub), which the card's GitHub repository shares a name with and the licence in the repository is LG's (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions: the limitation section lists inappropriate, biased, incorrect and outdated answers and tells users not to try to induce harmful output. Licence risk 5, 15 less 5 for field-of-use limits beyond the law (no commercial use, no building competing models) and 5 for terms the licensor can change or revoke (LG may amend the agreement at any time, and the licence is revocable). Security programme 0, no way to report a flaw on the card beyond a support contact, and www.lgresearch.ai has no security.txt. 25 + 15 + 0 + 5 + 5 + 0 = 50.\n- Qwen3.5-9B (55): File safety 25, safetensors only and nothing from the repository runs on load or to build prompts. Provenance 15, the Qwen organisation linked from Alibaba's QwenLM README, with a SHA-256 for every shard. Safety evaluation 0, none on the card, and the Qwen3.5 blog couldn't be read, so this is couldn't check scored as absent. Agent guidance 0, the card has no prompt-injection guidance, refusal scope or cautions. Licence risk 15, Apache 2.0 with nothing added and a working licence link. Security programme 0, no SECURITY.md in Qwen's GitHub repositories, no security.txt on qwen.ai or alibabacloud.com, and whether Alibaba's group security centre takes reports about Qwen models couldn't be checked.\n\n## Score by category\n\n| Category | Weight | EXAONE 4.5 33B | Qwen3.5-9B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 85 | Qwen3.5-9B +20 |\n| Risk | 20% (20 this run) | 50 | 55 | Qwen3.5-9B +5 |\n| Agent interface | 10% (10 this run) | 85 | 70 | EXAONE 4.5 33B +15 |\n| Documentation and upkeep | 10% (10 this run) | 50 | 34 | EXAONE 4.5 33B +16 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **60.5 · C** | |\n\n## Facts side by side\n\n| Fact | EXAONE 4.5 33B | Qwen3.5-9B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | LG AI Research | 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 | EXAONE AI Model License Agreement 1.2 - NC (research and education only) | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-06 | 2026-03-02 |\n| Terms last updated | not read yet | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 2.5/5 (4) |\n\n## Verdicts\n\n**EXAONE 4.5 33B.** A well-documented vision-language model with a full set of builds, tool calling, switchable reasoning, a 256K context and a stated knowledge cut-off. The licence decides whether you can use it: research and education only, output included, and LG can change the terms. Nothing answered on the repository and no safety results.\n\n**Qwen3.5-9B.** A small dense vision-language model with trained tool calling, thinking that turns off per request and a 262,144-token context. Ollama's 4-bit build is 6.6 GB and the BF16 files 19.3 GB, and every major runtime loads it. The files haven't changed since 2 March 2026, Qwen answered none of the 40 latest discussions, and no safety results are published.\n\n## Before you call either\n\n### EXAONE 4.5 33B\n\n1. Don't use it, or anything it writes, commercially. The licence covers the output too\n2. Serve it with vLLM and `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser hermes`\n3. Set `enable_thinking` false for a direct answer. Thinking is on by default\n4. Use temperature 1.0, top_p 0.95 and presence_penalty 1.5 in general, and temperature 0.6 with top_k 20 for documents and Korean\n5. For llama.cpp, load a projector file from LG's GGUF repository with the weights for image input\n6. Its knowledge stops at December 2024\n\n### Qwen3.5-9B\n\n1. Smallest start is `ollama run qwen3.5:9b` (6.6 GB, q4_k_m)\n2. On vLLM run `vllm serve Qwen/Qwen3.5-9B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, or tool calls come back as text\n3. Tool calls use the XML style `\u003ctool_call\u003e\u003cfunction=name\u003e\u003cparameter=x\u003e`, not Hermes JSON, so use the qwen3_coder parser and not hermes\n4. Thinking is on by default; pass `chat_template_kwargs` with `enable_thinking` false for short tasks, since `/nothink` isn't supported\n5. There's no generation_config.json, so pass sampling yourself, temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5 when thinking, or 0.7, 0.8, 20, 1.5 without\n6. Keep at least 128K of context when thinking, and turn on YaRN only beyond 262,144 tokens\n7. Validate JSON output yourself or use the runtime's guided decoding\n\n## Questions\n\n### Which is better for AI agents, EXAONE 4.5 33B or Qwen3.5-9B?\n\nEXAONE 4.5 33B and Qwen3.5-9B score within a point of each other on agent readiness, 61 (C) and 60.5 (C). Qwen3.5-9B leads on reach and risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-9b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-9b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"exaone-4-5-33b\", \"b\": \"qwen3-5-9b\"}`. From a terminal: `anchor compare exaone-4-5-33b qwen3-5-9b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/exaone-4-5-33b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-5-9b.json\n\n## Other comparisons with EXAONE 4.5 33B or Qwen3.5-9B\n\n- [Agents-A1-4B vs EXAONE 4.5 33B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-exaone-4-5-33b.md)\n- [Agents-A1-4B vs Qwen3.5-9B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-9b.md)\n- [Apertus v1.5 8B vs EXAONE 4.5 33B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-exaone-4-5-33b.md)\n- [Apertus v1.5 8B vs Qwen3.5-9B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-9b.md)\n- [Apodex-1.1-mini vs EXAONE 4.5 33B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-exaone-4-5-33b.md)\n- [Apodex-1.1-mini vs Qwen3.5-9B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-9b.md)\n- [Arete-27B vs EXAONE 4.5 33B](https://www.anchorterminal.com/compare/arete-27b-vs-exaone-4-5-33b.md)\n- [Arete-27B vs Qwen3.5-9B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-5-9b.md)\n- [BigBang-v1 vs EXAONE 4.5 33B](https://www.anchorterminal.com/compare/bigbang-v1-vs-exaone-4-5-33b.md)\n- [BigBang-v1 vs Qwen3.5-9B](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-9b.md)\n- [Carbon-X vs EXAONE 4.5 33B](https://www.anchorterminal.com/compare/carbon-x-vs-exaone-4-5-33b.md)\n- [Carbon-X vs Qwen3.5-9B](https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-5-9b.md)\n- [Cola2 vs EXAONE 4.5 33B](https://www.anchorterminal.com/compare/cola2-vs-exaone-4-5-33b.md)\n- [Cola2 vs Qwen3.5-9B](https://www.anchorterminal.com/compare/cola2-vs-qwen3-5-9b.md)\n- [Cosmos-Reason2-2B vs EXAONE 4.5 33B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-exaone-4-5-33b.md)\n- [Cosmos-Reason2-2B vs Qwen3.5-9B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-5-9b.md)\n- [Darwin-36B-Opus vs EXAONE 4.5 33B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-exaone-4-5-33b.md)\n- [Darwin-36B-Opus vs Qwen3.5-9B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-9b.md)\n- [DeepSeek-V3.2 vs EXAONE 4.5 33B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-exaone-4-5-33b.md)\n- [DeepSeek-V3.2 vs Qwen3.5-9B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-5-9b.md)\n- [DeepSeek V4.1 Flash vs EXAONE 4.5 33B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-exaone-4-5-33b.md)\n- [DeepSeek V4.1 Flash vs Qwen3.5-9B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-9b.md)\n- [DeepSeek-V4-Flash-0731 vs EXAONE 4.5 33B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-exaone-4-5-33b.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3.5-9B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-9b.md)\n- [Devstral Small 2 vs EXAONE 4.5 33B](https://www.anchorterminal.com/compare/devstral-small-2-vs-exaone-4-5-33b.md)\n- [Devstral Small 2 vs Qwen3.5-9B](https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-5-9b.md)\n- [DiffusionGemma 26B A4B IT vs EXAONE 4.5 33B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-exaone-4-5-33b.md)\n- [DiffusionGemma 26B A4B IT vs Qwen3.5-9B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-5-9b.md)\n- [EXAONE 4.5 33B vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-gemma-4-12b-it.md)\n- [EXAONE 4.5 33B vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-gemma-4-26b-a4b-it.md)\n- [EXAONE 4.5 33B vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-gemma-4-31b-it.md)\n- [EXAONE 4.5 33B vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-gemma-4-e2b-it.md)\n- [EXAONE 4.5 33B vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-gemma-4-e4b-it.md)\n- [EXAONE 4.5 33B vs GLM-4.5-Air](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-glm-4-5-air.md)\n- [EXAONE 4.5 33B vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-glm-4-7-flash.md)\n- [EXAONE 4.5 33B vs GLM-5.3](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-glm-5-3.md)\n- [EXAONE 4.5 33B vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-glm-5-3-flash.md)\n- [EXAONE 4.5 33B vs gpt-oss-120b](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-gpt-oss-120b.md)\n- [EXAONE 4.5 33B vs gpt-oss-20b](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-gpt-oss-20b.md)\n- [EXAONE 4.5 33B vs Granite 4.2 30B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-granite-4-2-30b.md)\n- [EXAONE 4.5 33B vs Granite 4.2 3B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-granite-4-2-3b.md)\n- [EXAONE 4.5 33B vs Granite 4.2 8B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-granite-4-2-8b.md)\n- [EXAONE 4.5 33B vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-hy-memory-30b-a3b.md)\n- [EXAONE 4.5 33B vs Hy3](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-hy3.md)\n- [EXAONE 4.5 33B vs Inkling](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-inkling.md)\n- [EXAONE 4.5 33B vs Inkling-Small](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-inkling-small.md)\n- [EXAONE 4.5 33B vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [EXAONE 4.5 33B vs Kimi K3](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-kimi-k3.md)\n- [EXAONE 4.5 33B vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-kimi-k3-1-pro.md)\n- [EXAONE 4.5 33B vs Kolibri-1](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-kolibri-1.md)\n- [EXAONE 4.5 33B vs Kronos](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-kronos.md)\n- [EXAONE 4.5 33B vs Laguna M.1](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-laguna-m-1.md)\n- [EXAONE 4.5 33B vs Laguna S 2.1](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-laguna-s-2-1.md)\n- [EXAONE 4.5 33B vs Laguna XS 2.1](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-laguna-xs-2-1.md)\n- [EXAONE 4.5 33B vs LBS-27B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lbs-27b.md)\n- [EXAONE 4.5 33B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm2-5-2-6b.md)\n- [EXAONE 4.5 33B vs LFM3-1.7B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm3-1-7b.md)\n- [EXAONE 4.5 33B vs LFM3-Audio-2B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm3-audio-2b.md)\n- [EXAONE 4.5 33B vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm3-omni-2b.md)\n- [EXAONE 4.5 33B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm3-vl-2b.md)\n- [EXAONE 4.5 33B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ling-3-0-tiny.md)\n- [EXAONE 4.5 33B vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ling-3-1-flash-1019.md)\n- [EXAONE 4.5 33B vs LongCat-2.5](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-longcat-2-5.md)\n- [EXAONE 4.5 33B vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-mimo-v2-6-distill-qwen-9b.md)\n- [EXAONE 4.5 33B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-mimo-v2-6-flash-rl.md)\n- [EXAONE 4.5 33B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minicpm-o-4-5.md)\n- [EXAONE 4.5 33B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minicpm-v-4-7-1b.md)\n- [EXAONE 4.5 33B vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minicpm-v-4-7-35b-a3b.md)\n- [EXAONE 4.5 33B vs MiniCPM5-2B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minicpm5-2b.md)\n- [EXAONE 4.5 33B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minimax-m2-5.md)\n- [EXAONE 4.5 33B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minimax-m2-7.md)\n- [EXAONE 4.5 33B vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minimax-m3-1-flash.md)\n- [EXAONE 4.5 33B vs Mistral Large 4](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-mistral-large-4.md)\n- [EXAONE 4.5 33B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-muse-glimmer-30b.md)\n- [EXAONE 4.5 33B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nanbeige-4-2-3b.md)\n- [EXAONE 4.5 33B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nanbeige-4-3-33b.md)\n- [EXAONE 4.5 33B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [EXAONE 4.5 33B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [EXAONE 4.5 33B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nemotron-3-nano-30b-a3b.md)\n- [EXAONE 4.5 33B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nemotron-3-nano-4b.md)\n- [EXAONE 4.5 33B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [EXAONE 4.5 33B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nemotron-3-super-120b-a12b.md)\n- [EXAONE 4.5 33B vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nemotron-3-ultra-550b-a55b.md)\n- [EXAONE 4.5 33B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-north-mini-code-1-0.md)\n- [EXAONE 4.5 33B vs occamy-1.0](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-occamy-1-0.md)\n- [EXAONE 4.5 33B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-olmo-3-7b-think.md)\n- [EXAONE 4.5 33B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ornith-1-5-122b.md)\n- [EXAONE 4.5 33B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ornith-1-5-35b-a3b.md)\n- [EXAONE 4.5 33B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ornith-1-5-397b.md)\n- [EXAONE 4.5 33B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ornith-1-5-9b.md)\n- [EXAONE 4.5 33B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-otel-2-0-llm-31b-it.md)\n- [EXAONE 4.5 33B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen-agentworld-35b-a3b.md)\n- [EXAONE 4.5 33B vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-30b-a3b-instruct-2507.md)\n- [EXAONE 4.5 33B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-4b-instruct-2507.md)\n- [EXAONE 4.5 33B vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-0-8b.md)\n- [EXAONE 4.5 33B vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-122b-a10b.md)\n- [EXAONE 4.5 33B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-2b.md)\n- [EXAONE 4.5 33B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-397b-a17b.md)\n- [EXAONE 4.5 33B vs Qwen3.5-4B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-4b.md)\n- [EXAONE 4.5 33B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-6-27b.md)\n- [EXAONE 4.5 33B vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-6-35b-a3b.md)\n- [EXAONE 4.5 33B vs Qwen3.8-27B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-8-27b.md)\n- [EXAONE 4.5 33B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-8-flash-next.md)\n- [EXAONE 4.5 33B vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-coder-30b-a3b-instruct.md)\n- [EXAONE 4.5 33B vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-coder-next.md)\n- [EXAONE 4.5 33B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-omni-30b-a3b-instruct.md)\n- [EXAONE 4.5 33B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-vl-2b-instruct.md)\n- [EXAONE 4.5 33B vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-vl-30b-a3b-instruct.md)\n- [EXAONE 4.5 33B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-vl-32b-instruct.md)\n- [EXAONE 4.5 33B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-vl-4b-instruct.md)\n- [EXAONE 4.5 33B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-vl-8b-instruct.md)\n- [EXAONE 4.5 33B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwopus3-8-27b-flash-v2.md)\n- [EXAONE 4.5 33B vs Recursive Flint](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-recursive-flint.md)\n- [EXAONE 4.5 33B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ripple-65b-preview.md)\n- [EXAONE 4.5 33B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-sensenova-loop-9b-preview.md)\n- [EXAONE 4.5 33B vs SmolLM3-3B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-smollm3-3b.md)\n- [EXAONE 4.5 33B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-spark-x2-6-4b.md)\n- [EXAONE 4.5 33B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-step-3-7-flash.md)\n- [EXAONE 4.5 33B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-swift-1-5-qwen3-8-27b.md)\n- [EXAONE 4.5 33B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-swift-1-5-qwen3-8-flash-next.md)\n- [EXAONE 4.5 33B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-thinkingcap-qwen3-8-27b.md)\n- [EXAONE 4.5 33B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-unlimited-ocr-2.md)\n- [EXAONE 4.5 33B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-wevisone-vl-2b.md)\n- [EXAONE 4.5 33B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-wevisone-vl-4b.md)\n- [EXAONE 4.5 33B vs ZAYA1-8B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-zaya1-8b.md)\n- [Gemma 4 12B IT vs Qwen3.5-9B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-9b.md)\n- [Gemma 4 26B A4B IT vs Qwen3.5-9B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-5-9b.md)\n- [Gemma 4 31B IT vs Qwen3.5-9B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-9b.md)\n- [Gemma 4 E2B IT vs Qwen3.5-9B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-5-9b.md)\n- [Gemma 4 E4B IT vs Qwen3.5-9B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-5-9b.md)\n- [GLM-4.5-Air vs Qwen3.5-9B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-5-9b.md)\n- [GLM-4.7-Flash vs 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Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-unlimited-ocr-2.md)\n- [Qwen3.5-9B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-2b.md)\n- [Qwen3.5-9B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-4b.md)\n- [Qwen3.5-9B vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-zaya1-8b.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": "2026-11-11", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "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": "EXAONE 4.5 33B vs Qwen3.5-9B", "url": "" } ], "description": "EXAONE 4.5 33B and Qwen3.5-9B score within a point of each other for open-weight models you run yourself, 61 and 60.5 out of 100. 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