{
  "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 scores 61 (C) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 1 of 4 scored categories. Olmo 3 7B Think leads on reach and risk.",
    "b": {
      "slug": "olmo-3-7b-think",
      "name": "Olmo 3 7B Think",
      "vendor": "Ai2 (Allen Institute for AI)",
      "vendorUrl": "https://allenai.org",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Olmo 3 7B Think is Ai2's fully open 7B reasoning model for maths, code and step-by-step answers, with training data, code and checkpoints published, a 65,536-token context and thinking always on, released in November 2025 under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/olmo-3-7b-think",
      "markdownUrl": "https://www.anchorterminal.com/tools/olmo-3-7b-think.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/olmo-3-7b-think.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/olmo-3-7b-think.json",
      "dns": "olmo-3-7b-think.grade.anchorterminal.com",
      "repo": "https://huggingface.co/allenai/Olmo-3-7B-Think",
      "license": "Apache-2.0, with Ai2's Responsible Use Guidelines named on the card",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "allenai/Olmo-3-7B-Think"
        },
        {
          "registry": "ollama",
          "name": "olmo-3:7b-think"
        }
      ],
      "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. The card says it is intended for research and educational use in line with Ai2's Responsible Use Guidelines, which list disallowed uses outside research. Running it yourself costs the hardware, 14.6 GB in BF16 and 4.5 GB for Ollama's 4-bit 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/allenai/Olmo-3-7B-Think",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "reasoning",
        "fully-open",
        "open-data",
        "apache-2.0",
        "safetensors",
        "self-hosted"
      ],
      "lastRelease": "2026-06-25",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 52.3,
        "grade": "D",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 103,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 28,
          "interface": 35,
          "reach": 75,
          "risk": 75,
          "upkeep": 52
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "A fully open 7B reasoning model for maths, code and step-by-step answers, with every dataset and checkpoint published and its safety results rerunnable. Ollama's 4-bit build needs about 5.4 GB. It always thinks, wasn't trained for tool calls, has 65K of context, and Ai2 hasn't answered its discussions.",
        "bestFor": "Researchers and agents that want a small fully open reasoning model for maths and code, with its training data on hand",
        "strengths": [
          "Training data, code, checkpoints and evaluation harness all published, the data with sources and sizes",
          "Safety results published with a harness to rerun them",
          "Ollama's 4-bit build fits in about 5.4 GB, and released llama.cpp, Ollama, vLLM, LM Studio and Transformers run it",
          "Safetensors only with no repository code, a checksum on every file and no gate",
          "Apache 2.0 with no revenue threshold"
        ],
        "weaknesses": [
          "Not trained for tool calling, and the tool format lives only in the template",
          "Thinking is always on with no way to turn it off, and 65,536 tokens of context",
          "Ai2 publishes no quantised build and no provider hosts it",
          "The card binds use to Ai2's Responsible Use Guidelines, which Ai2 can change",
          "No Ai2 reply in the discussions, and the template's default prompt contradicts the card"
        ],
        "agentNotes": [
          "Quickest start is `ollama run olmo-3:7b-think`, about 4.5 GB",
          "With vLLM, `vllm serve allenai/Olmo-3-7B-Think --max-model-len 65536`; vLLM runs it through its Transformers backend",
          "Sample at temperature 0.6 and top_p 0.95 with up to 32,768 output tokens, as the card recommends, since thinking traces are long",
          "Every reply starts with a think block; strip it yourself or use a reasoning parser that reads think tags",
          "Pass your own system prompt; the template's default says the cut-off is November 2024 and that no functions are available",
          "For tool calls use Olmo 3 7B Instruct instead, which Ai2 trained for function calling",
          "A discussion reports endless reasoning loops when served with vLLM, unanswered, so cap max_tokens",
          "Check the use against Ai2's Responsible Use Guidelines, which bar automated social-media posting and undisclosed automated content outside research"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "D",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 52.3
          }
        ],
        "editorialScores": {
          "capability": 28,
          "interface": 35,
          "reach": 75,
          "risk": 75,
          "upkeep": 52
        },
        "provenanceScore": 70,
        "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 olmo-3:7b-think\n# or\nvllm serve allenai/Olmo-3-7B-Think --max-model-len 65536",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"olmo-3:7b-think\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"options\": {\"temperature\": 0.6, \"top_p\": 0.95}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/olmo-3-7b-think"
      },
      "weights": {
        "trainedBy": "Ai2 (Allen Institute for AI)",
        "builtOn": "allenai/Olmo-3-7B-Think-DPO",
        "params": "7.3B",
        "context": 65536,
        "goodFor": "Fully open 7B reasoning model for maths, code and step-by-step answers in English, with published training data and checkpoints for research, always thinking, not trained for tool calls.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "ollama olmo-3:7b-think-q4_K_M",
            "url": "https://ollama.com/library/olmo-3:7b-think-q4_K_M",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 4.5,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Digest b8d4c92ac9c1, also the 7b-think and latest tags. q8_0 is 7.8 GB and fp16 15 GB. Sizes as Ollama's tags page shows them"
          },
          {
            "name": "LM Studio allenai/olmo-3-7b-think (Q4_K_M GGUF)",
            "url": "https://huggingface.co/lmstudio-community/Olmo-3-7B-Think-GGUF",
            "who": "lmstudio",
            "precision": "q4_k_m",
            "sizeGB": 4.5,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "LM Studio",
              "llama.cpp"
            ],
            "note": "Olmo-3-7B-Think-Q4_K_M.gguf, 4.47 GB from the Hub's file list. The catalogue entry also lists Q6_K, Q8_0 and MLX 4, 6 and 8-bit builds; the MLX 4-bit is 4.12 GB (Mac only)"
          },
          {
            "name": "Olmo-3-7B-Think",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-Think",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 14.6,
            "memoryGB": 24,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "Transformers"
            ]
          }
        ],
        "run": "vllm serve allenai/Olmo-3-7B-Think --max-model-len 65536",
        "runBuild": "Olmo-3-7B-Think",
        "checked": "2026-11-04",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "Ai2 (Allen Institute for AI)",
        "family": "Olmo 3 (3, 3.1)",
        "url": "https://www.anchorterminal.com/open-weights/#ai2",
        "others": [
          {
            "name": "Olmo-3.1-7B-RL-Zero-Math",
            "url": "https://huggingface.co/allenai/Olmo-3.1-7B-RL-Zero-Math",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3.1-7B-RL-Zero-Code",
            "url": "https://huggingface.co/allenai/Olmo-3.1-7B-RL-Zero-Code",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3.1-32B-Think",
            "url": "https://huggingface.co/allenai/Olmo-3.1-32B-Think",
            "params": "32B (Hub safetensors count 32.23B)"
          },
          {
            "name": "Olmo-3.1-32B-Instruct",
            "url": "https://huggingface.co/allenai/Olmo-3.1-32B-Instruct",
            "params": "32B (Hub safetensors count 32.23B)"
          },
          {
            "name": "Olmo-3-7B-RL-Zero-Mix",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-RL-Zero-Mix",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3-7B-Instruct-DPO",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-Instruct-DPO",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3-7B-Instruct",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-Instruct",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3-32B-Think",
            "url": "https://huggingface.co/allenai/Olmo-3-32B-Think",
            "params": "32B (Hub safetensors count 32.23B)"
          },
          {
            "name": "Olmo-3.1-32B-Instruct-SFT",
            "url": "https://huggingface.co/allenai/Olmo-3.1-32B-Instruct-SFT",
            "params": "32B (Hub safetensors count 32.23B)"
          },
          {
            "name": "Olmo-3.1-32B-Instruct-DPO",
            "url": "https://huggingface.co/allenai/Olmo-3.1-32B-Instruct-DPO",
            "params": "32B (Hub safetensors count 32.23B)"
          },
          {
            "name": "Olmo-3-7B-Think-DPO",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-Think-DPO",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3-7B-RL-Zero-Math",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-RL-Zero-Math",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3-7B-RL-Zero-IF",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-RL-Zero-IF",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3-7B-RL-Zero-General",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-RL-Zero-General",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3-7B-RL-Zero-Code",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-RL-Zero-Code",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3-7B-Instruct-SFT",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-Instruct-SFT",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3-32B-Think-SFT",
            "url": "https://huggingface.co/allenai/Olmo-3-32B-Think-SFT",
            "params": "32B (Hub safetensors count 32.23B)"
          },
          {
            "name": "Olmo-3-32B-Think-DPO",
            "url": "https://huggingface.co/allenai/Olmo-3-32B-Think-DPO",
            "params": "32B (Hub safetensors count 32.23B)"
          },
          {
            "name": "Olmo-3-1125-32B",
            "url": "https://huggingface.co/allenai/Olmo-3-1125-32B",
            "params": "32B (Hub safetensors count 32.23B)"
          },
          {
            "name": "Olmo-3-7B-Think-SFT",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-Think-SFT",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3-1025-7B",
            "url": "https://huggingface.co/allenai/Olmo-3-1025-7B",
            "params": "7B (Hub safetensors count 7.30B)"
          }
        ],
        "olderCount": 95,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "The Allen Institute for Artificial Intelligence (Ai2), a 501(c)(3) nonprofit, per its Responsible Use Guidelines",
        "domain": "allenai.org",
        "domainRegistered": "2010-12-03",
        "endpointOnVendorDomain": null,
        "terms": "https://allenai.org/terms",
        "privacy": "https://allenai.org/privacy-policy",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "expired",
        "checked": "2026-10-21",
        "notes": [
          "RDAP for allenai.org gives a registration date of 3 December 2010.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored. Ai2's guidelines say its website terms also govern use of its tools.",
          "allenai.org serves a security.txt with a contact and an Expires date of 31 July 2026, which has passed. The Responsible Use Guidelines name a channel for bugs and security concerns with Ai2's models.",
          "No changelog. The commits are card edits, two configuration fixes from pull requests in November and December 2025, and a parameter-count fix on 25 June 2026.",
          "allenai.org links to huggingface.co/allenai, so the repository is the publisher's own."
        ],
        "score": 70
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/olmo-3-7b-think.json",
      "live": {
        "slug": "olmo-3-7b-think",
        "securityTxt": {
          "url": "https://allenai.org/.well-known/security.txt",
          "state": "expired",
          "expires": "2026-07-31T05:00:00.000Z",
          "checkedAt": "2026-11-10T16:07:23.74126383Z"
        },
        "domain": {
          "domain": "allenai.org",
          "registered": "2010-12-03",
          "source": "https://rdap.publicinterestregistry.org/rdap/domain/allenai.org",
          "checkedAt": "2026-11-08T15:03:50.561358192Z"
        },
        "pages": [
          {
            "url": "https://allenai.org/privacy-policy",
            "kind": "privacy",
            "status": 200,
            "checkedAt": "2026-11-10T16:09:31.69478417Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "6575e6dd3713"
          },
          {
            "url": "https://allenai.org/terms",
            "kind": "terms",
            "status": 200,
            "checkedAt": "2026-11-10T16:09:35.047986684Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "fde9cf039435"
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        ],
        "updatedAt": "2026-11-10T16:09:35.047986684Z"
      }
    },
    "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": "The chat template earns 20 for chat_template.jinja in the repository. The tool-calling format earns 10, since only the template and vLLM's own parser describe it. Trained tool calling earns nothing, because Ai2's report says tool calling was trained into the Instruct model, not Think. Structured output earns nothing. Reasoning control earns nothing, since it always thinks. Context earns 5 for 65,536 tokens trained. 20 + 10 + 0 + 0 + 0 + 5 = 35."
        },
        "scores": {
          "a": 85,
          "b": 35
        }
      },
      {
        "by": 25,
        "category": "risk",
        "leads": "olmo-3-7b-think",
        "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 earns 25, safetensors only and nothing from the repository runs. Provenance earns 15, files in the allenai organisation Ai2 links to, with a checksum for every file. Safety evaluation earns 20, as Ai2 publishes its safety results and the OLMES harness to rerun them. Agent guidance earns 5 for the card's general cautions. Licence risk earns 5, losing 5 for disallowed uses beyond the law and 5 because Ai2 can update the guidelines and end access at its discretion. The security programme earns 5 for a reporting route, a security.txt now past its expiry and a channel for security concerns in the guidelines, with no public advisories. 25 + 15 + 20 + 5 + 5 + 5 = 75."
        },
        "scores": {
          "a": 50,
          "b": 75
        }
      },
      {
        "by": 10,
        "category": "reach",
        "leads": "olmo-3-7b-think",
        "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 earns the full 30, since Ollama's 4-bit build is 4.5 GB and fits 8 GB (per the 21 October check of Ollama's library). Runtimes earn 25, with released llama.cpp (b11963), Ollama (v0.40.14, pinning b11963), vLLM, LM Studio and Transformers all running it. Builds earn nothing, because Ai2 publishes no quantised build of its own. Terms earn 10, since the card binds Apache 2.0 to Ai2's Responsible Use Guidelines, a use policy. Download earns 10, with no gate. Hosted earns nothing, as the Hub's provider mapping is empty and Ai2 has no API for it. 30 + 25 + 0 + 10 + 10 + 0 = 75."
        },
        "scores": {
          "a": 65,
          "b": 75
        }
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    "facts": [
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        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "LG AI Research",
        "b": "Ai2 (Allen Institute for AI)",
        "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, with Ai2's Responsible Use Guidelines named on the card",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-07-06",
        "b": "2026-06-25",
        "name": "Last release"
      },
      {
        "a": "not read yet",
        "b": "2024-09-25",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "2025-02-19",
        "name": "Privacy policy last updated"
      },
      {
        "a": "not read yet",
        "b": "yes",
        "name": "Customer content may train models"
      },
      {
        "a": "not read yet",
        "b": "not found in the text",
        "name": "Terms restrict automated access"
      },
      {
        "a": "not read yet",
        "b": "not found in the text",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "not read yet",
        "b": "yes",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "not read yet",
        "b": "not found in the text",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "none",
        "b": "2.8/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "EXAONE 4.5 33B scores 61 (C) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 1 of 4 scored categories. Olmo 3 7B Think leads on reach and risk.",
        "question": "Which is better for AI agents, EXAONE 4.5 33B or Olmo 3 7B Think?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "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, 75 against 65",
          "Risk, 75 against 50"
        ],
        "also": null,
        "goodFor": "Researchers and agents that want a small fully open reasoning model for maths and code, with its training data on hand",
        "slug": "olmo-3-7b-think",
        "watchFor": "Not trained for tool calling, and the tool format lives only in the template"
      }
    ],
    "history": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
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        "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-olmo-3-7b-think.json",
        "title": "Agents-A1-4B vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-olmo-3-7b-think"
      },
      {
        "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-olmo-3-7b-think.json",
        "title": "Apertus v1.5 8B vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-olmo-3-7b-think"
      },
      {
        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-olmo-3-7b-think.json",
        "title": "Apodex-1.1-mini vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-olmo-3-7b-think"
      },
      {
        "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": "https://www.anchorterminal.com/compare/arete-27b-vs-olmo-3-7b-think.json",
        "title": "Arete-27B vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-olmo-3-7b-think"
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      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-exaone-4-5-33b.json",
        "title": "BigBang-v1 vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-exaone-4-5-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-olmo-3-7b-think.json",
        "title": "BigBang-v1 vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-olmo-3-7b-think"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-exaone-4-5-33b.json",
        "title": "Carbon-X vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-exaone-4-5-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-olmo-3-7b-think.json",
        "title": "Carbon-X vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-olmo-3-7b-think"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-exaone-4-5-33b.json",
        "title": "Cola2 vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-exaone-4-5-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-olmo-3-7b-think.json",
        "title": "Cola2 vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-olmo-3-7b-think"
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      {
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        "title": "Cosmos-Reason2-2B vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-exaone-4-5-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-olmo-3-7b-think.json",
        "title": "Cosmos-Reason2-2B vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-olmo-3-7b-think"
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      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-exaone-4-5-33b.json",
        "title": "Darwin-36B-Opus vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-exaone-4-5-33b"
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        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-olmo-3-7b-think.json",
        "title": "Darwin-36B-Opus vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-olmo-3-7b-think"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-exaone-4-5-33b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-exaone-4-5-33b"
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-olmo-3-7b-think"
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-6-27b"
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        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-6-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-6-35b-a3b"
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-8-flash-next.json",
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-8-flash-next"
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        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-coder-30b-a3b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-coder-30b-a3b-instruct"
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-coder-next"
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-omni-30b-a3b-instruct"
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-vl-32b-instruct"
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        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-vl-4b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-vl-4b-instruct"
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        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-vl-8b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-vl-8b-instruct"
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        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwopus3-8-27b-flash-v2.json",
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwopus3-8-27b-flash-v2"
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        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-recursive-flint.json",
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-recursive-flint"
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        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ripple-65b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ripple-65b-preview"
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-sensenova-loop-9b-preview"
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-smollm3-3b"
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        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-spark-x2-6-4b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-olmo-3-7b-think.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-olmo-3-7b-think"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-olmo-3-7b-think.json",
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        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-olmo-3-7b-think"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-olmo-3-7b-think.json",
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-olmo-3-7b-think"
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        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-olmo-3-7b-think"
      },
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        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-olmo-3-7b-think.json",
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        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-olmo-3-7b-think"
      },
      {
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        "title": "Nanbeige4.2-3B vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-olmo-3-7b-think"
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        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-olmo-3-7b-think"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-olmo-3-7b-think.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-olmo-3-7b-think"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-olmo-3-7b-think.json",
        "title": "Nemotron 3.5 Lightning 30B A3B vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-olmo-3-7b-think"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-olmo-3-7b-think.json",
        "title": "Nemotron 3 Nano 30B A3B vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-olmo-3-7b-think"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-olmo-3-7b-think.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-olmo-3-7b-think"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-olmo-3-7b-think.json",
        "title": "Nemotron 3 Super 120B A12B vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-olmo-3-7b-think"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-olmo-3-7b-think.json",
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        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-olmo-3-7b-think"
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        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-olmo-3-7b-think"
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      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-122b.json",
        "title": "Olmo 3 7B Think vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-122b"
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        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-35b-a3b.json",
        "title": "Olmo 3 7B Think vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-397b.json",
        "title": "Olmo 3 7B Think vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-397b"
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      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-9b.json",
        "title": "Olmo 3 7B Think vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-9b"
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      {
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        "title": "Olmo 3 7B Think vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-otel-2-0-llm-31b-it"
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        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen-agentworld-35b-a3b.json",
        "title": "Olmo 3 7B Think vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen-agentworld-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Olmo 3 7B Think vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-30b-a3b-instruct-2507"
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      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-4b-instruct-2507.json",
        "title": "Olmo 3 7B Think vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-4b-instruct-2507"
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      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-0-8b.json",
        "title": "Olmo 3 7B Think vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-0-8b"
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      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-122b-a10b.json",
        "title": "Olmo 3 7B Think vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-122b-a10b"
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      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-2b.json",
        "title": "Olmo 3 7B Think vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-397b-a17b.json",
        "title": "Olmo 3 7B Think vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-397b-a17b"
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      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-4b.json",
        "title": "Olmo 3 7B Think vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-9b.json",
        "title": "Olmo 3 7B Think vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-6-27b.json",
        "title": "Olmo 3 7B Think vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-6-35b-a3b.json",
        "title": "Olmo 3 7B Think vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-8-27b.json",
        "title": "Olmo 3 7B Think vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-8-flash-next.json",
        "title": "Olmo 3 7B Think vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Olmo 3 7B Think vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-coder-30b-a3b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-coder-next.json",
        "title": "Olmo 3 7B Think vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Olmo 3 7B Think vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-omni-30b-a3b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-2b-instruct.json",
        "title": "Olmo 3 7B Think vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Olmo 3 7B Think vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-32b-instruct.json",
        "title": "Olmo 3 7B Think vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-4b-instruct.json",
        "title": "Olmo 3 7B Think vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-8b-instruct.json",
        "title": "Olmo 3 7B Think vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Olmo 3 7B Think vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-recursive-flint.json",
        "title": "Olmo 3 7B Think vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ripple-65b-preview.json",
        "title": "Olmo 3 7B Think vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-sensenova-loop-9b-preview.json",
        "title": "Olmo 3 7B Think vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-smollm3-3b.json",
        "title": "Olmo 3 7B Think vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-smollm3-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-spark-x2-6-4b.json",
        "title": "Olmo 3 7B Think vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-spark-x2-6-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-step-3-7-flash.json",
        "title": "Olmo 3 7B Think vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Olmo 3 7B Think vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Olmo 3 7B Think vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Olmo 3 7B Think vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-unlimited-ocr-2.json",
        "title": "Olmo 3 7B Think vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-wevisone-vl-2b.json",
        "title": "Olmo 3 7B Think vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-wevisone-vl-4b.json",
        "title": "Olmo 3 7B Think vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-zaya1-8b.json",
        "title": "Olmo 3 7B Think vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 10,
        "edge": "olmo-3-7b-think",
        "exaone-4-5-33b": 65,
        "key": "reach",
        "name": "Reach",
        "olmo-3-7b-think": 75,
        "weight": 25
      },
      {
        "by": 25,
        "edge": "olmo-3-7b-think",
        "exaone-4-5-33b": 50,
        "key": "risk",
        "name": "Risk",
        "olmo-3-7b-think": 75,
        "weight": 20
      },
      {
        "by": 50,
        "edge": "exaone-4-5-33b",
        "exaone-4-5-33b": 85,
        "key": "interface",
        "name": "Agent interface",
        "olmo-3-7b-think": 35,
        "weight": 10
      },
      {
        "by": 2,
        "edge": "olmo-3-7b-think",
        "exaone-4-5-33b": 50,
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "olmo-3-7b-think": 52,
        "weight": 10
      }
    ],
    "summary": "EXAONE 4.5 33B scores 61 (C) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 1 of 4 scored categories. Olmo 3 7B Think 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.",
      "olmo-3-7b-think": "A fully open 7B reasoning model for maths, code and step-by-step answers, with every dataset and checkpoint published and its safety results rerunnable. Ollama's 4-bit build needs about 5.4 GB. It always thinks, wasn't trained for tool calls, has 65K of context, and Ai2 hasn't answered its discussions."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
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    "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-olmo-3-7b-think.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-olmo-3-7b-think.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-olmo-3-7b-think.min.md"
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  "markdown": "EXAONE 4.5 33B scores 61 (C) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 1 of 4 scored categories. Olmo 3 7B Think 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- Olmo 3 7B Think: grade D, 52.3/100, rank #139 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/olmo-3-7b-think.md · JSON https://www.anchorterminal.com/api/v1/tools/olmo-3-7b-think.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 35\n\nWatch for: Research and education only. No commercial use of the model or its output\n\n### Olmo 3 7B Think (D)\n\nGood for: Researchers and agents that want a small fully open reasoning model for maths and code, with its training data on hand\n\nAhead on:\n- Reach, 75 against 65\n- Risk, 75 against 50\n\nWatch for: Not trained for tool calling, and the tool format lives only in the template\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- Olmo 3 7B Think (35): The chat template earns 20 for chat_template.jinja in the repository. The tool-calling format earns 10, since only the template and vLLM's own parser describe it. Trained tool calling earns nothing, because Ai2's report says tool calling was trained into the Instruct model, not Think. Structured output earns nothing. Reasoning control earns nothing, since it always thinks. Context earns 5 for 65,536 tokens trained. 20 + 10 + 0 + 0 + 0 + 5 = 35.\n\n### Risk: Olmo 3 7B Think +25\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- Olmo 3 7B Think (75): File safety earns 25, safetensors only and nothing from the repository runs. Provenance earns 15, files in the allenai organisation Ai2 links to, with a checksum for every file. Safety evaluation earns 20, as Ai2 publishes its safety results and the OLMES harness to rerun them. Agent guidance earns 5 for the card's general cautions. Licence risk earns 5, losing 5 for disallowed uses beyond the law and 5 because Ai2 can update the guidelines and end access at its discretion. The security programme earns 5 for a reporting route, a security.txt now past its expiry and a channel for security concerns in the guidelines, with no public advisories. 25 + 15 + 20 + 5 + 5 + 5 = 75.\n\n### Reach: Olmo 3 7B Think +10\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- Olmo 3 7B Think (75): Memory earns the full 30, since Ollama's 4-bit build is 4.5 GB and fits 8 GB (per the 21 October check of Ollama's library). Runtimes earn 25, with released llama.cpp (b11963), Ollama (v0.40.14, pinning b11963), vLLM, LM Studio and Transformers all running it. Builds earn nothing, because Ai2 publishes no quantised build of its own. Terms earn 10, since the card binds Apache 2.0 to Ai2's Responsible Use Guidelines, a use policy. Download earns 10, with no gate. Hosted earns nothing, as the Hub's provider mapping is empty and Ai2 has no API for it. 30 + 25 + 0 + 10 + 10 + 0 = 75.\n\n## Score by category\n\n| Category | Weight | EXAONE 4.5 33B | Olmo 3 7B Think | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 75 | Olmo 3 7B Think +10 |\n| Risk | 20% (20 this run) | 50 | 75 | Olmo 3 7B Think +25 |\n| Agent interface | 10% (10 this run) | 85 | 35 | EXAONE 4.5 33B +50 |\n| Documentation and upkeep | 10% (10 this run) | 50 | 52 | Olmo 3 7B Think +2 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **52.3 · D** | |\n\n## Facts side by side\n\n| Fact | EXAONE 4.5 33B | Olmo 3 7B Think |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | LG AI Research | Ai2 (Allen Institute for AI) |\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, with Ai2's Responsible Use Guidelines named on the card |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-06 | 2026-06-25 |\n| Terms last updated | not read yet | 2024-09-25 |\n| Privacy policy last updated | no document linked | 2025-02-19 |\n| Customer content may train models | not read yet | yes |\n| Terms restrict automated access | not read yet | not found in the text |\n| Terms restrict benchmarking | not read yet | not found in the text |\n| Terms or service can change without notice | not read yet | yes |\n| Arbitration or class-action waiver | not read yet | not found in the text |\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**Olmo 3 7B Think.** A fully open 7B reasoning model for maths, code and step-by-step answers, with every dataset and checkpoint published and its safety results rerunnable. Ollama's 4-bit build needs about 5.4 GB. It always thinks, wasn't trained for tool calls, has 65K of context, and Ai2 hasn't answered its discussions.\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### Olmo 3 7B Think\n\n1. Quickest start is `ollama run olmo-3:7b-think`, about 4.5 GB\n2. With vLLM, `vllm serve allenai/Olmo-3-7B-Think --max-model-len 65536`; vLLM runs it through its Transformers backend\n3. Sample at temperature 0.6 and top_p 0.95 with up to 32,768 output tokens, as the card recommends, since thinking traces are long\n4. Every reply starts with a think block; strip it yourself or use a reasoning parser that reads think tags\n5. Pass your own system prompt; the template's default says the cut-off is November 2024 and that no functions are available\n6. For tool calls use Olmo 3 7B Instruct instead, which Ai2 trained for function calling\n7. A discussion reports endless reasoning loops when served with vLLM, unanswered, so cap max_tokens\n8. Check the use against Ai2's Responsible Use Guidelines, which bar automated social-media posting and undisclosed automated content outside research\n\n## Questions\n\n### Which is better for AI agents, EXAONE 4.5 33B or Olmo 3 7B Think?\n\nEXAONE 4.5 33B scores 61 (C) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 1 of 4 scored categories. Olmo 3 7B Think 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-olmo-3-7b-think.json, and with the fewest tokens: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-olmo-3-7b-think.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"exaone-4-5-33b\", \"b\": \"olmo-3-7b-think\"}`. 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    "title": "EXAONE 4.5 33B vs Olmo 3 7B Think for AI agents (2026)",
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