{ "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 MiniCPM-o 4.5 score within a point of each other on agent readiness, 61 (C) and 61 (C). MiniCPM-o 4.5 leads on reach and documentation and upkeep.", "b": { "slug": "minicpm-o-4-5", "name": "MiniCPM-o 4.5", "vendor": "OpenBMB", "vendorUrl": "https://openbmb.cn", "kind": "weights", "category": "open-weights", "summary": "MiniCPM-o 4.5 is OpenBMB's open-weight omni-modal model of 9.4B parameters that takes images, video, audio and text and answers in text and speech, including full-duplex live conversation and voice cloning from a reference clip, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/minicpm-o-4-5", "markdownUrl": "https://www.anchorterminal.com/tools/minicpm-o-4-5.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minicpm-o-4-5.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minicpm-o-4-5.json", "dns": "minicpm-o-4-5.grade.anchorterminal.com", "repo": "https://huggingface.co/openbmb/MiniCPM-o-4_5", "license": "Apache-2.0; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards", "transports": [], "packages": [ { "registry": "huggingface", "name": "openbmb/MiniCPM-o-4_5" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-o-4_5-gguf" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-o-4_5-awq" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-o-4_5-GPTQ" }, { "registry": "ollama", "name": "openbmb/minicpm-o4.5" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of OpenBMB's four repositories for the model is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. OpenBMB also serves it as MiniCPM-O-4.5-9B on its own OpenAI-compatible API at api.modelbest.cn, with a key from platform.modelbest.cn, which isn't graded here. Running it yourself costs the hardware, 20.0 GB for the BF16 repository and 5.9 GB for the Q4_0 GGUF with its vision file (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": "2026-11-03" }, "docsUrl": "https://github.com/OpenSQZ/MiniCPM-V-CookBook", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "omni", "vision", "speech", "voice-cloning", "full-duplex", "reasoning", "apache-2.0", "safetensors", "gguf", "awq", "gptq", "self-hosted" ], "lastRelease": "2026-10-24", "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": 38, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 35, "reach": 90, "risk": 40, "upkeep": 66 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-03" }, "negative": 0, "verdict": "A small omni cousin that sees, listens and talks back, with full-duplex speech and voice cloning, in about 7 GB at 4-bit through llama.cpp or Ollama for text and images. Speech output needs OpenBMB's remote code or llama.cpp-omni, and 28 GB of GPU for the full demo. The main risks are repository code and pickle files and no safety results.", "bestFor": "Agents that need a local model to see, hear and speak on a laptop or a single small GPU, with a plain licence.", "strengths": [ "OpenBMB's 4-bit GGUF and Ollama builds fit in about 7 GB", "Images, video, audio and text in, text and streaming speech out, with full-duplex live conversation", "Plain Apache 2.0, no gate, and OpenBMB's own API serves it too", "Released vLLM, llama.cpp and Ollama run it, and OpenBMB publishes GGUF, AWQ and GPTQ builds", "Repository changed 5 days ago and earlier versions stay in their own repositories" ], "weaknesses": [ "Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni", "Two pickle files (flow.pt and hift.pt) in the speech stage", "No safety results or guidance, though it clones voices from a short clip", "Tool calling, structured output and where the reasoning comes back aren't documented", "40,960 tokens of context, and OpenBMB answered only half the questions in the discussions" ], "agentNotes": [ "Quickest start for text and images is `ollama run openbmb/minicpm-o4.5`, about 6 GB", "With vLLM 0.16.0 or later pass `--trust-remote-code`, and send `stop_token_ids` [151643, 151645] as OpenBMB's guide does", "vLLM, llama.cpp and Ollama return text only; for speech out use the card's Transformers 4.51.0 code or OpenBMB's llama.cpp-omni", "In llama.cpp pass both the language GGUF and MiniCPM-o-4_5-vision-F16.gguf with `--mmproj`, and use `--jinja --reasoning-budget 0` to turn thinking off", "Keep prompts within 40,960 tokens", "Don't rely on tool calls or JSON output, neither is documented; validate any you get" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "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": 35, "reach": 90, "risk": 40, "upkeep": 66 }, "provenanceScore": 58, "checked": { "categories": { "interface": "2026-11-03", "reach": "2026-11-03", "risk": "2026-11-03", "upkeep": "2026-11-03" }, "last": "2026-11-03", "oldest": "2026-11-03", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run openbmb/minicpm-o4.5\n# or, with vLLM 0.16.0 or later (text out):\nvllm serve openbmb/MiniCPM-o-4_5 --trust-remote-code --dtype auto \\\n --max-model-len 4096 --max-num-batched-tokens 2048", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"openbmb/MiniCPM-o-4_5\", \"messages\": [{\"role\": \"user\", \"content\": [{\"type\": \"image_url\", \"image_url\": {\"url\": \"https://example.com/photo.jpg\"}}, {\"type\": \"text\", \"text\": \"What is in this picture?\"}]}], \"stop_token_ids\": [151643, 151645]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/minicpm-o-4-5" }, "sameCompany": [ "minicpm-v-4-7-1b", "minicpm-v-4-7-35b-a3b", "minicpm5-2b" ], "weights": { "trainedBy": "OpenBMB", "builtOn": "Qwen3-8B with SigLIP2, Whisper-medium and CosyVoice2 (named on the card)", "params": "9.4B", "context": 40960, "goodFor": "Omni-modal assistant for images, video, speech and text in, text and speech out, with live full-duplex conversation and voice cloning, small enough for a laptop at 4-bit.", "runtimes": [ "vLLM", "llama.cpp", "Ollama" ], "builds": [ { "name": "MiniCPM-o-4_5-gguf Q4_K_M", "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 6.12, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "5.03 GB language file plus the 1.10 GB vision file, the pair OpenBMB's llama.cpp guide uses. The audio, TTS and token2wav GGUF files (2.75 GB more) are for OpenBMB's llama.cpp-omni" }, { "name": "MiniCPM-o-4_5-gguf Q4_0", "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-gguf", "who": "publisher", "precision": "q4_0", "sizeGB": 5.87, "memoryGB": 8, "format": "gguf", "note": "4.77 GB language file plus the 1.10 GB vision file, the smallest 4-bit pair" }, { "name": "ollama openbmb/minicpm-o4.5:latest", "url": "https://ollama.com/openbmb/minicpm-o4.5/tags", "who": "publisher", "precision": "q4_k_m", "sizeGB": 6.1, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "In the openbmb namespace on ollama.com, taken as OpenBMB's, text and image input only, 40K context. q4_0 is 5.9 GB. Size as Ollama's tags page shows it" }, { "name": "MiniCPM-o-4_5-gguf Q8_0", "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 9.8, "memoryGB": 12, "format": "gguf", "note": "8.71 GB language file plus the 1.10 GB vision file" }, { "name": "MiniCPM-o-4_5-GPTQ", "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-GPTQ", "who": "publisher", "precision": "gptq-int4", "sizeGB": 9.77, "memoryGB": 12, "format": "safetensors", "note": "4-bit GPTQ, group size 128. Remote code. vLLM's quantisation table marks GPTQ unsupported on AMD GPUs" }, { "name": "MiniCPM-o-4_5-awq", "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-awq", "who": "publisher", "precision": "awq-int4", "sizeGB": 13.07, "memoryGB": 16, "format": "safetensors", "note": "4-bit AWQ language model with the vision, audio and TTS modules left unquantised, plus a bundled CosyVoice2-0.5B folder. Remote code" }, { "name": "MiniCPM-o-4_5-gguf F16", "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-gguf", "who": "publisher", "precision": "f16", "sizeGB": 17.48, "memoryGB": 24, "format": "gguf", "note": "16.38 GB language file plus MiniCPM-o-4_5-vision-F16.gguf (1.10 GB) for image input" }, { "name": "MiniCPM-o-4_5", "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5", "who": "publisher", "precision": "bf16", "sizeGB": 20.05, "memoryGB": 32, "format": "safetensors", "runsOn": [ "vLLM", "Transformers (remote code)" ], "note": "Size is the Hub's file list summed, 18.7 GB of safetensors plus the token2wav stage, example media and code. Speech output needs the remote code with Transformers 4.51.0" } ], "run": "vllm serve openbmb/MiniCPM-o-4_5 --dtype auto --trust-remote-code --max-model-len 4096 --max-num-batched-tokens 2048", "runBuild": "MiniCPM-o-4_5", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "OpenBMB (MiniCPM)", "family": "MiniCPM-o 4", "url": "https://www.anchorterminal.com/open-weights/#openbmb", "olderCount": 22 }, "alsoIn": [ "voice-agents" ], "area": "models", "provenance": { "legalEntity": "OpenBMB (named as the MiniCPM-o team on the technical report; the GitHub LICENSE is the unfilled Apache template)", "domain": "openbmb.cn", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/openbmb/MiniCPM-o-4_5/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The card links OpenBMB's GitHub repository, whose README links back to this Hugging Face repository. rdap.org has no RDAP service for .cn, so the domain's registration date wasn't checked.", "The listing is the weights, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored. OpenBMB's API at api.modelbest.cn has its own terms, not read.", "The changelog is the repository's commit history, whose messages are mostly README updates, with code fixes on 4 March and 6 July 2026. OpenBMB's GitHub README carries dated news entries (release on 3 February 2026, technical report on 7 May, API on 17 May).", "openbmb.cn/.well-known/security.txt and modelbest.cn/.well-known/security.txt answered 404 on 21 October 2026, and OpenBMB/MiniCPM-o has no security policy." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/minicpm-o-4-5.json", "live": { "slug": "minicpm-o-4-5", "securityTxt": { "url": "https://openbmb.cn/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:08:10.170465559Z" }, "domain": { "domain": "openbmb.cn", "checkedAt": "2026-11-08T14:50:03.275690576Z" }, "pages": [ { "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:23:38.685365772Z", "changedAt": "2026-11-10T16:23:38.685365772Z", "fingerprint": "0600a4aee37d" } ], "updatedAt": "2026-11-10T16:23:38.685365772Z" } }, "edges": [ { "by": 50, "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 template in the tokenizer config. Tool-calling format 10 of 25, only the inherited Qwen3 template says how tools work. Trained tool calling 0, OpenBMB never mentions tool use. Structured output 0, not documented. Reasoning control 0, thinking turns on and off per request but nobody says where the reasoning comes back. Context 5, 40,960 tokens. Read as an omni model, it takes images, video and audio and returns text and streaming speech, which the card documents with code." }, "scores": { "a": 85, "b": 35 } }, { "by": 25, "category": "reach", "leads": "minicpm-o-4-5", "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, OpenBMB's 4-bit GGUF with its vision file is 5.9 GB, about 7 GB with a fifth added. Runtimes 15 of 25, released vLLM, llama.cpp and Ollama run it, while Transformers 5.22.1 still needs OpenBMB's remote code and SGLang 0.5.23 supports only the older 2.6. Builds 15, GGUF at several precisions plus AWQ and GPTQ. Terms 15, plain Apache 2.0. Download 10, nothing gated. Hosted 5, OpenBMB's own API serves it." }, "scores": { "a": 65, "b": 90 } }, { "by": 16, "category": "upkeep", "leads": "minicpm-o-4-5", "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 11 of 20, limitations, context length and a knowledge cut-off are missing. Training data 5, described by kind without sources, sizes or a cut-off. Last change 20, the card changed on 24 October 2026. Responsiveness 10, OpenBMB answered half of the 12 questions. Version policy 10, earlier models stay in their own repositories and releases are dated. File health 10, the files agree and the two bug reports have replies." }, "scores": { "a": 50, "b": 66 } }, { "by": 10, "category": "risk", "leads": "exaone-4-5-33b", "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 5 of 25, two pickle files sit in the speech stage and the documented load runs repository code. Provenance 15, the files are in the openbmb organisation OpenBMB's GitHub links, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 5, general cautions only, nothing about voice cloning misuse, prompt injection or refusals. Licence risk 15, plain Apache 2.0. Security programme 0, no security.txt, policy or advisories." }, "scores": { "a": 50, "b": 40 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "LG AI Research", "b": "OpenBMB", "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; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-07-06", "b": "2026-10-24", "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": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "EXAONE 4.5 33B and MiniCPM-o 4.5 score within a point of each other on agent readiness, 61 (C) and 61 (C). MiniCPM-o 4.5 leads on reach and documentation and upkeep.", "question": "Which is better for AI agents, EXAONE 4.5 33B or MiniCPM-o 4.5?" } ], "goodFor": [ { "aheadOn": [ "Risk, 50 against 40", "Agent interface, 85 against 35" ], "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, 90 against 65", "Documentation and upkeep, 66 against 50" ], "also": null, "goodFor": "Agents that need a local model to see, hear and speak on a laptop or a single small GPU, with a plain licence.", "slug": "minicpm-o-4-5", "watchFor": "Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni" } ], "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-minicpm-o-4-5.json", "title": "Agents-A1-4B vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-o-4-5" }, { "json": 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"https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-4b-instruct-2507.json", "title": "MiniCPM-o 4.5 vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-0-8b.json", "title": "MiniCPM-o 4.5 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-122b-a10b.json", "title": "MiniCPM-o 4.5 vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-2b.json", "title": "MiniCPM-o 4.5 vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-397b-a17b.json", "title": "MiniCPM-o 4.5 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-4b.json", "title": "MiniCPM-o 4.5 vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-9b.json", "title": "MiniCPM-o 4.5 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-6-27b.json", "title": "MiniCPM-o 4.5 vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-6-35b-a3b.json", "title": "MiniCPM-o 4.5 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-8-27b.json", "title": "MiniCPM-o 4.5 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-8-flash-next.json", "title": "MiniCPM-o 4.5 vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-coder-30b-a3b-instruct.json", "title": "MiniCPM-o 4.5 vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-coder-next.json", "title": "MiniCPM-o 4.5 vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-omni-30b-a3b-instruct.json", "title": "MiniCPM-o 4.5 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-2b-instruct.json", "title": "MiniCPM-o 4.5 vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-30b-a3b-instruct.json", "title": "MiniCPM-o 4.5 vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-32b-instruct.json", "title": "MiniCPM-o 4.5 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-4b-instruct.json", "title": "MiniCPM-o 4.5 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-8b-instruct.json", "title": "MiniCPM-o 4.5 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwopus3-8-27b-flash-v2.json", "title": "MiniCPM-o 4.5 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-recursive-flint.json", "title": "MiniCPM-o 4.5 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ripple-65b-preview.json", "title": "MiniCPM-o 4.5 vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-sensenova-loop-9b-preview.json", "title": "MiniCPM-o 4.5 vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-smollm3-3b.json", "title": "MiniCPM-o 4.5 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-spark-x2-6-4b.json", "title": "MiniCPM-o 4.5 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-step-3-7-flash.json", "title": "MiniCPM-o 4.5 vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-27b.json", "title": "MiniCPM-o 4.5 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-flash-next.json", "title": "MiniCPM-o 4.5 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-thinkingcap-qwen3-8-27b.json", "title": "MiniCPM-o 4.5 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-unlimited-ocr-2.json", "title": "MiniCPM-o 4.5 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-wevisone-vl-2b.json", "title": "MiniCPM-o 4.5 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-wevisone-vl-4b.json", "title": "MiniCPM-o 4.5 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-zaya1-8b.json", "title": "MiniCPM-o 4.5 vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 25, "edge": "minicpm-o-4-5", "exaone-4-5-33b": 65, "key": "reach", "minicpm-o-4-5": 90, "name": "Reach", "weight": 25 }, { "by": 10, "edge": "exaone-4-5-33b", "exaone-4-5-33b": 50, "key": "risk", "minicpm-o-4-5": 40, "name": "Risk", "weight": 20 }, { "by": 50, "edge": "exaone-4-5-33b", "exaone-4-5-33b": 85, "key": "interface", "minicpm-o-4-5": 35, "name": "Agent interface", "weight": 10 }, { "by": 16, "edge": "minicpm-o-4-5", "exaone-4-5-33b": 50, "key": "upkeep", "minicpm-o-4-5": 66, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "EXAONE 4.5 33B and MiniCPM-o 4.5 score within a point of each other on agent readiness, 61 (C) and 61 (C). MiniCPM-o 4.5 leads on reach and documentation and upkeep. 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.", "minicpm-o-4-5": "A small omni cousin that sees, listens and talks back, with full-duplex speech and voice cloning, in about 7 GB at 4-bit through llama.cpp or Ollama for text and images. Speech output needs OpenBMB's remote code or llama.cpp-omni, and 28 GB of GPU for the full demo. The main risks are repository code and pickle files and no safety results." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minicpm-o-4-5", "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minicpm-o-4-5.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minicpm-o-4-5.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minicpm-o-4-5.min.md" }, "markdown": "EXAONE 4.5 33B and MiniCPM-o 4.5 score within a point of each other on agent readiness, 61 (C) and 61 (C). MiniCPM-o 4.5 leads on reach and documentation and upkeep. 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- MiniCPM-o 4.5: grade C, 61/100, rank #48 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm-o-4-5.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm-o-4-5.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- Best conversational voice-agent APIs: https://www.anchorterminal.com/best/voice-agents/index.md\n- All 581 voice agents comparisons: https://www.anchorterminal.com/compare/voice-agents/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- Risk, 50 against 40\n- Agent interface, 85 against 35\n\nWatch for: Research and education only. No commercial use of the model or its output\n\n### MiniCPM-o 4.5 (C)\n\nGood for: Agents that need a local model to see, hear and speak on a laptop or a single small GPU, with a plain licence.\n\nAhead on:\n- Reach, 90 against 65\n- Documentation and upkeep, 66 against 50\n\nWatch for: Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni\n\n\n## Why the edge\n\n### Agent interface: EXAONE 4.5 33B +50\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- MiniCPM-o 4.5 (35): Chat template 20, a Jinja template in the tokenizer config. Tool-calling format 10 of 25, only the inherited Qwen3 template says how tools work. Trained tool calling 0, OpenBMB never mentions tool use. Structured output 0, not documented. Reasoning control 0, thinking turns on and off per request but nobody says where the reasoning comes back. Context 5, 40,960 tokens. Read as an omni model, it takes images, video and audio and returns text and streaming speech, which the card documents with code.\n\n### Reach: MiniCPM-o 4.5 +25\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- MiniCPM-o 4.5 (90): Memory 30, OpenBMB's 4-bit GGUF with its vision file is 5.9 GB, about 7 GB with a fifth added. Runtimes 15 of 25, released vLLM, llama.cpp and Ollama run it, while Transformers 5.22.1 still needs OpenBMB's remote code and SGLang 0.5.23 supports only the older 2.6. Builds 15, GGUF at several precisions plus AWQ and GPTQ. Terms 15, plain Apache 2.0. Download 10, nothing gated. Hosted 5, OpenBMB's own API serves it.\n\n### Documentation and upkeep: MiniCPM-o 4.5 +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- MiniCPM-o 4.5 (66): Model card 11 of 20, limitations, context length and a knowledge cut-off are missing. Training data 5, described by kind without sources, sizes or a cut-off. Last change 20, the card changed on 24 October 2026. Responsiveness 10, OpenBMB answered half of the 12 questions. Version policy 10, earlier models stay in their own repositories and releases are dated. File health 10, the files agree and the two bug reports have replies.\n\n### Risk: EXAONE 4.5 33B +10\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- MiniCPM-o 4.5 (40): File safety 5 of 25, two pickle files sit in the speech stage and the documented load runs repository code. Provenance 15, the files are in the openbmb organisation OpenBMB's GitHub links, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 5, general cautions only, nothing about voice cloning misuse, prompt injection or refusals. Licence risk 15, plain Apache 2.0. Security programme 0, no security.txt, policy or advisories.\n\n## Score by category\n\n| Category | Weight | EXAONE 4.5 33B | MiniCPM-o 4.5 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 90 | MiniCPM-o 4.5 +25 |\n| Risk | 20% (20 this run) | 50 | 40 | EXAONE 4.5 33B +10 |\n| Agent interface | 10% (10 this run) | 85 | 35 | EXAONE 4.5 33B +50 |\n| Documentation and upkeep | 10% (10 this run) | 50 | 66 | MiniCPM-o 4.5 +16 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | EXAONE 4.5 33B | MiniCPM-o 4.5 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | LG AI Research | OpenBMB |\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; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-06 | 2026-10-24 |\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 | 2.8/5 (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**MiniCPM-o 4.5.** A small omni cousin that sees, listens and talks back, with full-duplex speech and voice cloning, in about 7 GB at 4-bit through llama.cpp or Ollama for text and images. Speech output needs OpenBMB's remote code or llama.cpp-omni, and 28 GB of GPU for the full demo. The main risks are repository code and pickle files and no safety results.\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### MiniCPM-o 4.5\n\n1. Quickest start for text and images is `ollama run openbmb/minicpm-o4.5`, about 6 GB\n2. With vLLM 0.16.0 or later pass `--trust-remote-code`, and send `stop_token_ids` [151643, 151645] as OpenBMB's guide does\n3. vLLM, llama.cpp and Ollama return text only; for speech out use the card's Transformers 4.51.0 code or OpenBMB's llama.cpp-omni\n4. In llama.cpp pass both the language GGUF and MiniCPM-o-4_5-vision-F16.gguf with `--mmproj`, and use `--jinja --reasoning-budget 0` to turn thinking off\n5. Keep prompts within 40,960 tokens\n6. Don't rely on tool calls or JSON output, neither is documented; validate any you get\n\n## Questions\n\n### Which is better for AI agents, EXAONE 4.5 33B or MiniCPM-o 4.5?\n\nEXAONE 4.5 33B and MiniCPM-o 4.5 score within a point of each other on agent readiness, 61 (C) and 61 (C). MiniCPM-o 4.5 leads on reach and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minicpm-o-4-5.json, and with the fewest tokens: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minicpm-o-4-5.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"exaone-4-5-33b\", \"b\": \"minicpm-o-4-5\"}`. From a terminal: `anchor compare exaone-4-5-33b minicpm-o-4-5`\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/minicpm-o-4-5.json\n\n## Other comparisons with EXAONE 4.5 33B or MiniCPM-o 4.5\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 MiniCPM-o 4.5](https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-o-4-5.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 MiniCPM-o 4.5](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-o-4-5.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 MiniCPM-o 4.5](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-o-4-5.md)\n- [Arete-27B vs EXAONE 4.5 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WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-wevisone-vl-2b.md)\n- [MiniCPM-o 4.5 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-wevisone-vl-4b.md)\n- [MiniCPM-o 4.5 vs ZAYA1-8B](https://www.anchorterminal.com/compare/minicpm-o-4-5-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": 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