{
  "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 MiniMax-M2.7's 57.6 (C), and leads in every scored category.",
    "b": {
      "slug": "minimax-m2-7",
      "name": "MiniMax-M2.7",
      "vendor": "MiniMax",
      "vendorUrl": "https://www.minimax.io",
      "kind": "weights",
      "category": "open-weights",
      "summary": "MiniMax-M2.7 is MiniMax's open-weight mixture-of-experts model for coding, agent harnesses and office work, about 229B parameters in FP8 with thinking always on, published on Hugging Face on 9 April 2026 under a non-commercial licence.",
      "url": "https://www.anchorterminal.com/tools/minimax-m2-7",
      "markdownUrl": "https://www.anchorterminal.com/tools/minimax-m2-7.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minimax-m2-7.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minimax-m2-7.json",
      "dns": "minimax-m2-7.grade.anchorterminal.com",
      "repo": "https://huggingface.co/MiniMaxAI/MiniMax-M2.7",
      "license": "MiniMax M2.7 non-commercial licence (MIT-style terms, commercial use needs MiniMax's written authorisation)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "MiniMaxAI/MiniMax-M2.7"
        }
      ],
      "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 to use for personal, research and non-profit purposes. Since 11 April 2026 the licence prohibits commercial use without MiniMax's prior written authorisation, and commercial users must display \"Built with MiniMax M2.7\". Running it yourself costs the hardware, 230.2 GB of FP8 files. MiniMax's API closed M2.7 to new users on 19 October 2026 and retires it on 18 November 2026 (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/MiniMaxAI/MiniMax-M2.7",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "reasoning",
        "tool-calling",
        "coding",
        "non-commercial",
        "safetensors",
        "fp8",
        "self-hosted"
      ],
      "lastRelease": "2026-04-20",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 57.6,
        "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": 80,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 71,
          "interface": 75,
          "reach": 60,
          "risk": 30,
          "upkeep": 42
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "A 230B mixture-of-experts model for coding and agent harnesses, with parallel tool calls, about 200K of context and parsers in vLLM and SGLang. It needs a 4 GPU server for FP8 or about 166 GB for a 4-bit library build, and it always thinks. The main risk is the licence, switched to non-commercial two days after release.",
        "bestFor": "Researchers and personal projects with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls",
        "strengths": [
          "Runs in released vLLM, SGLang, llama.cpp, Transformers and LM Studio, and in Ollama through its pinned llama.cpp",
          "Tool-call format documented by MiniMax with named vLLM and SGLang parsers and a parsing function",
          "Several tool calls in one block, and about 200K tokens of context",
          "Safetensors only, in the publisher's organisation with a SHA-256 for every shard",
          "No gate or account, and served by three inference providers on Hugging Face"
        ],
        "weaknesses": [
          "Commercial use needs MiniMax's written authorisation since a licence swap on 11 April 2026, made without a note",
          "230 GB in FP8, and about 166 GB for the smallest 4-bit library build, more than a 128 GB machine",
          "Thinking is always on, with no documented way to turn it off or budget it",
          "No safety evaluation, agent guidance or security contact, and every loading route runs repository code",
          "No MiniMax reply in discussions since June 2026, and the card leaves out limitations, architecture, parameters and a cut-off"
        ],
        "agentNotes": [
          "Check the use is personal, research or non-profit before starting it; anything commercial needs MiniMax's written authorisation, and commercial users must show Built with MiniMax M2.7",
          "Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.7 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think",
          "In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code",
          "MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache, with 196K the most per sequence",
          "On one large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.7 lists a 138 GB Q4_K_M GGUF and states 138 GB minimum memory",
          "Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends, and budget output tokens for thinking on every turn",
          "Pin Transformers 4.57.1 as MiniMax's guide does; a report that it fails on Transformers v5 has no answer",
          "Without the hardware, call it through Novita, Featherless AI or DeepInfra; MiniMax's own API retires M2.7 on 18 November 2026"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 1.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 57.6
          }
        ],
        "editorialScores": {
          "capability": 71,
          "interface": 75,
          "reach": 60,
          "risk": 30,
          "upkeep": 42
        },
        "provenanceScore": 60,
        "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",
          "lastMoved": "2026-11-04",
          "oldest": "2026-11-04",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# MiniMax's vLLM guide, 4 GPUs\nuv pip install vllm --torch-backend=auto\nSAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.7 --trust-remote-code \\\n  --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 \\\n  --reasoning-parser minimax_m2_append_think",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"MiniMaxAI/MiniMax-M2.7\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"temperature\": 1.0, \"top_p\": 0.95, \"top_k\": 40}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/minimax-m2-7"
      },
      "sameCompany": [
        "minimax-api",
        "minimax-video",
        "minimax-stt",
        "minimax-speech",
        "minimax-voice-cloning",
        "minimax-code",
        "minimax-m2-5",
        "minimax-m3-1-flash"
      ],
      "weights": {
        "trainedBy": "MiniMax",
        "params": "228.7B",
        "context": 204800,
        "goodFor": "Mixture-of-experts model for coding, agent harnesses and office documents, with parallel tool calls and always-on thinking, text only, for non-commercial use unless MiniMax authorises otherwise.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers",
          "Ollama"
        ],
        "builds": [
          {
            "name": "LM Studio minimax/minimax-m2.7 (Q4_K_M GGUF)",
            "url": "https://huggingface.co/lmstudio-community/MiniMax-M2.7-GGUF",
            "who": "lmstudio",
            "precision": "q4_k_m",
            "sizeGB": 138.3,
            "memoryGB": 192,
            "format": "gguf",
            "runsOn": [
              "LM Studio",
              "llama.cpp"
            ],
            "note": "MiniMax-M2.7-Q4_K_M split GGUF, 138.34 GB summed from the Hub's file list. The catalogue entry also lists Q6_K (187.8 GB) and Q8_0 (243.1 GB). LM Studio states 138 GB minimum memory"
          },
          {
            "name": "MiniMax-M2.7",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.7",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 230.2,
            "memoryGB": 512,
            "format": "safetensors",
            "note": "Native FP8 E4M3 with 128 by 128 blocks; the only build MiniMax publishes. Size summed from the Hub's file list"
          }
        ],
        "run": "SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.7 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think",
        "runBuild": "MiniMax-M2.7",
        "checked": "2026-11-04",
        "minMemoryGB": 192
      },
      "family": {
        "lab": "MiniMax",
        "family": "MiniMax-M2",
        "url": "https://www.anchorterminal.com/open-weights/#minimax",
        "others": [
          {
            "name": "MiniMax-M2",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2",
            "params": "230B total, 10B active"
          },
          {
            "name": "MiniMax-M2.1",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.1",
            "params": "230B total, 10B active (Hub count; card doesn't restate)"
          },
          {
            "name": "MiniMax-M2.5",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.5",
            "params": "230B total, 10B active (Hub count; card doesn't restate)",
            "listing": "https://www.anchorterminal.com/tools/minimax-m2-5"
          }
        ],
        "olderCount": 7,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "MiniMax (named in the licence's copyright line; MiniMax's platform terms name Nanonoble Pte. Ltd., Singapore)",
        "domain": "minimax.io",
        "domainRegistered": "2021-03-27",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The current licence reads Copyright (c) 2026 MiniMax. The platform terms' legal entity and the domain's registration date are as recorded on the minimax-api listing.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.",
          "No changelog. Commit messages are uploads and README or LICENSE updates; the licence change on 11 April 2026 reads only \"Delete LICENSE-MODEL\" and \"Upload LICENSE with huggingface_hub\".",
          "www.minimax.io/.well-known/security.txt redirects to the home page on 21 October 2026, and MiniMax-AI/MiniMax-M2.7 has no SECURITY.md, so there is no security.txt.",
          "MiniMax's site (www.minimax.io/models/text) links to huggingface.co/MiniMaxAI, and the card links the GitHub repository, so the repository is the publisher's own."
        ],
        "score": 60
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/minimax-m2-7.json",
      "live": {
        "slug": "minimax-m2-7",
        "securityTxt": {
          "url": "https://minimax.io/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:08:11.844974518Z"
        },
        "domain": {
          "domain": "minimax.io",
          "checkedAt": "2026-11-08T14:54:44.54388672Z"
        },
        "updatedAt": "2026-11-10T16:08:11.844974518Z"
      }
    },
    "edges": [
      {
        "by": 20,
        "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 earns 15, safetensors only with no pickle files, less 10 because every documented loading route passes trust_remote_code for the repository's Python. Provenance earns the full 15, files in the organisation MiniMax links to with a SHA-256 for each shard. Safety evaluation and agent guidance earn nothing, as MiniMax publishes neither. Licence risk earns nothing (read 21 October 2026). The Prohibited Uses appendix goes beyond the law, MiniMax swapped a commercial licence for a non-commercial one two days after release, and neither that swap nor the amendment three days later carries a dated note. The security programme earns nothing, with no security.txt, disclosure policy, SECURITY.md or advisories."
        },
        "scores": {
          "a": 50,
          "b": 30
        }
      },
      {
        "by": 10,
        "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 25, as MiniMax's own guide documents how tools go in and how calls come back, gives a parsing function, and its deployment guides name the vLLM and SGLang parsers. Trained tool calling earns 20, with several invoke blocks in one call. Structured output earns nothing, since JSON output isn't documented or claimed. Reasoning control earns nothing, because thinking is always on with no documented way to turn it off or budget it. Context earns 10 for 204,800 tokens in the config with no position scaling."
        },
        "scores": {
          "a": 85,
          "b": 75
        }
      },
      {
        "by": 8,
        "category": "upkeep",
        "leads": "exaone-4-5-33b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 20, Uses (the quickstart's question answering, document and agent examples), limitations, the architecture, the parameters, the context, the languages (in the metadata) and a knowledge cut-off of December 2024 are all on the card. Training data 5, A knowledge cut-off and one line on excluding personal, harmful and biased content, with no sources or sizes. Last change 10, the main repository last changed on 6 July 2026 (dependencies moved to official releases), 127 days before this check, and the FP8 repository on 9 August, 93 days. Responsiveness 0, none of the 5 most recent discussions that aren't pull requests (13 April to 4 May 2026) has a reply from an LGAI-EXAONE member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, EXAONE 4.0 and 4.0.1 stay in their own repositories and 4.5 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the 262,144 tokens, and a July commit moved the dependencies to released versions. 20 + 5 + 10 + 0 + 5 + 10 = 50.",
          "b": "The model card earns 2, stating only intended use and leaving out limitations, architecture, parameters, context, natural languages and a cut-off. Training data earns nothing, since only the training process is described. The last change earns 20, a card commit on 26 October 2026 that renames a MiniMax Agent link to MiniMax Code, 9 days before this check; the files, template and code last changed on 20 April 2026. Responsiveness earns 10, with a MiniMax reply in 8 of 36 discussions, none since June. Version policy earns 5, since earlier and later models sit in their own repositories without dated notes. File health earns 5, because a report that it doesn't load in Transformers v5 waits without a reply."
        },
        "scores": {
          "a": 50,
          "b": 42
        }
      },
      {
        "by": 5,
        "category": "reach",
        "leads": "exaone-4-5-33b",
        "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 5. MiniMax only publishes its 230 GB FP8 release, and the smallest build of 4-bit or better from a runtime's own library is LM Studio's Q4_K_M GGUF at 138.3 GB, about 166 GB with the fifth added, so it needs more than our 107.4 GB machine and fits within 256 GB. Runtimes earn the full 25, with model code in released vLLM, SGLang, llama.cpp and Transformers, an LM Studio catalogue entry, and Ollama 0.40.14, whose pinned llama.cpp b11963 has the minimax-m2 architecture (capped at 25). Builds earn 10 for one publisher precision, the FP8 release. Terms earn 5, since the licence today allows only personal, research and non-profit use without MiniMax's written authorisation. Download earns 10, with no gate or account. Hosted earns 5, since Novita, Featherless AI and DeepInfra serve it through Hugging Face, while MiniMax's own API is closed to new users and retires it on 18 November 2026."
        },
        "scores": {
          "a": 65,
          "b": 60
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "LG AI Research",
        "b": "MiniMax",
        "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": "MiniMax M2.7 non-commercial licence (MIT-style terms, commercial use needs MiniMax's written authorisation)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-07-06",
        "b": "2026-04-20",
        "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": "1.8/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "EXAONE 4.5 33B scores 61 (C) on agent readiness against MiniMax-M2.7's 57.6 (C), and leads in every scored category.",
        "question": "Which is better for AI agents, EXAONE 4.5 33B or MiniMax-M2.7?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 65 against 60",
          "Risk, 50 against 30",
          "Agent interface, 85 against 75",
          "Documentation and upkeep, 50 against 42"
        ],
        "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": null,
        "also": null,
        "goodFor": "Researchers and personal projects with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls",
        "slug": "minimax-m2-7",
        "watchFor": "Commercial use needs MiniMax's written authorisation since a licence swap on 11 April 2026, made without a note"
      }
    ],
    "history": {
      "exaone-4-5-33b": null,
      "minimax-m2-7": [
        {
          "causes": [
            "product"
          ],
          "date": "2026-11-04",
          "delta": 1.5,
          "from": 56.1,
          "fromGrade": "C",
          "to": 57.6,
          "toGrade": "C"
        }
      ]
    },
    "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-minimax-m2-7.json",
        "title": "Agents-A1-4B vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-7"
      },
      {
        "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-minimax-m2-7.json",
        "title": "Apertus v1.5 8B vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-exaone-4-5-33b.json",
        "title": "Apodex-1.1-mini vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-exaone-4-5-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-7.json",
        "title": "Apodex-1.1-mini vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-7"
      },
      {
        "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-minimax-m2-7.json",
        "title": "Arete-27B vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-minimax-m2-7"
      },
      {
        "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-minimax-m2-7.json",
        "title": "BigBang-v1 vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-7"
      },
      {
        "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-minimax-m2-7.json",
        "title": "Carbon-X vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-minimax-m2-7"
      },
      {
        "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-minimax-m2-7.json",
        "title": "Cola2 vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-exaone-4-5-33b.json",
        "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-minimax-m2-7.json",
        "title": "Cosmos-Reason2-2B vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minimax-m2-7"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minimax-m2-7.json",
        "title": "Darwin-36B-Opus vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-exaone-4-5-33b.json",
        "title": "DeepSeek-V3.2 vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-exaone-4-5-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minimax-m2-7.json",
        "title": "DeepSeek-V3.2 vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-exaone-4-5-33b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-exaone-4-5-33b"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minimax-m2-7.json",
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-exaone-4-5-33b.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minimax-m2-7.json",
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      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-minimax-m2-7.json",
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        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-minimax-m2-7"
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      {
        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-glm-4-5-air.json",
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      {
        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-glm-4-7-flash.json",
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      {
        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-glm-5-3.json",
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-glm-5-3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-glm-5-3-flash.json",
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-glm-5-3-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-gpt-oss-120b.json",
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-gpt-oss-120b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-gpt-oss-20b.json",
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-gpt-oss-20b"
      },
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        "title": "MiniMax-M2.7 vs Nemotron 3 Super 120B A12B",
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-ultra-550b-a55b.json",
        "title": "MiniMax-M2.7 vs Nemotron 3 Ultra 550B A55B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-ultra-550b-a55b"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-north-mini-code-1-0.json",
        "title": "MiniMax-M2.7 vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-north-mini-code-1-0"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-occamy-1-0.json",
        "title": "MiniMax-M2.7 vs occamy-1.0",
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-olmo-3-7b-think.json",
        "title": "MiniMax-M2.7 vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-olmo-3-7b-think"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-122b.json",
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-122b"
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-35b-a3b"
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        "title": "MiniMax-M2.7 vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-397b"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-9b.json",
        "title": "MiniMax-M2.7 vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-9b"
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        "title": "MiniMax-M2.7 vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-otel-2-0-llm-31b-it"
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "MiniMax-M2.7 vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-30b-a3b-instruct-2507"
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-4b-instruct-2507"
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-0-8b"
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-122b-a10b"
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        "title": "MiniMax-M2.7 vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-2b"
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-397b-a17b"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-4b.json",
        "title": "MiniMax-M2.7 vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-4b"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-9b.json",
        "title": "MiniMax-M2.7 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-9b"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-6-27b.json",
        "title": "MiniMax-M2.7 vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-6-27b"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-6-35b-a3b.json",
        "title": "MiniMax-M2.7 vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-6-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-8-27b.json",
        "title": "MiniMax-M2.7 vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-8-flash-next.json",
        "title": "MiniMax-M2.7 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-8-flash-next"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "MiniMax-M2.7 vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-coder-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-coder-next.json",
        "title": "MiniMax-M2.7 vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-coder-next"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "MiniMax-M2.7 vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-omni-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-2b-instruct.json",
        "title": "MiniMax-M2.7 vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "MiniMax-M2.7 vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-30b-a3b-instruct"
      },
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-32b-instruct.json",
        "title": "MiniMax-M2.7 vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-32b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-4b-instruct.json",
        "title": "MiniMax-M2.7 vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-4b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-8b-instruct.json",
        "title": "MiniMax-M2.7 vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwopus3-8-27b-flash-v2.json",
        "title": "MiniMax-M2.7 vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-recursive-flint.json",
        "title": "MiniMax-M2.7 vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ripple-65b-preview.json",
        "title": "MiniMax-M2.7 vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-sensenova-loop-9b-preview.json",
        "title": "MiniMax-M2.7 vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-smollm3-3b.json",
        "title": "MiniMax-M2.7 vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-spark-x2-6-4b.json",
        "title": "MiniMax-M2.7 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-step-3-7-flash.json",
        "title": "MiniMax-M2.7 vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-27b.json",
        "title": "MiniMax-M2.7 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "MiniMax-M2.7 vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-thinkingcap-qwen3-8-27b.json",
        "title": "MiniMax-M2.7 vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-unlimited-ocr-2.json",
        "title": "MiniMax-M2.7 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-2b.json",
        "title": "MiniMax-M2.7 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-4b.json",
        "title": "MiniMax-M2.7 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-zaya1-8b.json",
        "title": "MiniMax-M2.7 vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 5,
        "edge": "exaone-4-5-33b",
        "exaone-4-5-33b": 65,
        "key": "reach",
        "minimax-m2-7": 60,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 20,
        "edge": "exaone-4-5-33b",
        "exaone-4-5-33b": 50,
        "key": "risk",
        "minimax-m2-7": 30,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 10,
        "edge": "exaone-4-5-33b",
        "exaone-4-5-33b": 85,
        "key": "interface",
        "minimax-m2-7": 75,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 8,
        "edge": "exaone-4-5-33b",
        "exaone-4-5-33b": 50,
        "key": "upkeep",
        "minimax-m2-7": 42,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "EXAONE 4.5 33B scores 61 (C) on agent readiness against MiniMax-M2.7's 57.6 (C), and leads in every scored category. 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.",
      "minimax-m2-7": "A 230B mixture-of-experts model for coding and agent harnesses, with parallel tool calls, about 200K of context and parsers in vLLM and SGLang. It needs a 4 GPU server for FP8 or about 166 GB for a 4-bit library build, and it always thinks. The main risk is the licence, switched to non-commercial two days after release."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minimax-m2-7",
    "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minimax-m2-7.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minimax-m2-7.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minimax-m2-7.min.md"
  },
  "markdown": "EXAONE 4.5 33B scores 61 (C) on agent readiness against MiniMax-M2.7's 57.6 (C), and leads in every scored category. 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- MiniMax-M2.7: grade C, 57.6/100, rank #106 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/minimax-m2-7.md · JSON https://www.anchorterminal.com/api/v1/tools/minimax-m2-7.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- Reach, 65 against 60\n- Risk, 50 against 30\n- Agent interface, 85 against 75\n- Documentation and upkeep, 50 against 42\n\nWatch for: Research and education only. No commercial use of the model or its output\n\n### MiniMax-M2.7 (C)\n\nGood for: Researchers and personal projects with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls\n\nWatch for: Commercial use needs MiniMax's written authorisation since a licence swap on 11 April 2026, made without a note\n\n\n## Why the edge\n\n### Risk: EXAONE 4.5 33B +20\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- MiniMax-M2.7 (30): File safety earns 15, safetensors only with no pickle files, less 10 because every documented loading route passes trust_remote_code for the repository's Python. Provenance earns the full 15, files in the organisation MiniMax links to with a SHA-256 for each shard. Safety evaluation and agent guidance earn nothing, as MiniMax publishes neither. Licence risk earns nothing (read 21 October 2026). The Prohibited Uses appendix goes beyond the law, MiniMax swapped a commercial licence for a non-commercial one two days after release, and neither that swap nor the amendment three days later carries a dated note. The security programme earns nothing, with no security.txt, disclosure policy, SECURITY.md or advisories.\n\n### Agent interface: EXAONE 4.5 33B +10\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- MiniMax-M2.7 (75): The chat template earns 20 for chat_template.jinja in the repository. The tool-calling format earns 25, as MiniMax's own guide documents how tools go in and how calls come back, gives a parsing function, and its deployment guides name the vLLM and SGLang parsers. Trained tool calling earns 20, with several invoke blocks in one call. Structured output earns nothing, since JSON output isn't documented or claimed. Reasoning control earns nothing, because thinking is always on with no documented way to turn it off or budget it. Context earns 10 for 204,800 tokens in the config with no position scaling.\n\n### Documentation and upkeep: EXAONE 4.5 33B +8\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- MiniMax-M2.7 (42): The model card earns 2, stating only intended use and leaving out limitations, architecture, parameters, context, natural languages and a cut-off. Training data earns nothing, since only the training process is described. The last change earns 20, a card commit on 26 October 2026 that renames a MiniMax Agent link to MiniMax Code, 9 days before this check; the files, template and code last changed on 20 April 2026. Responsiveness earns 10, with a MiniMax reply in 8 of 36 discussions, none since June. Version policy earns 5, since earlier and later models sit in their own repositories without dated notes. File health earns 5, because a report that it doesn't load in Transformers v5 waits without a reply.\n\n### Reach: EXAONE 4.5 33B +5\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- MiniMax-M2.7 (60): Memory earns 5. MiniMax only publishes its 230 GB FP8 release, and the smallest build of 4-bit or better from a runtime's own library is LM Studio's Q4_K_M GGUF at 138.3 GB, about 166 GB with the fifth added, so it needs more than our 107.4 GB machine and fits within 256 GB. Runtimes earn the full 25, with model code in released vLLM, SGLang, llama.cpp and Transformers, an LM Studio catalogue entry, and Ollama 0.40.14, whose pinned llama.cpp b11963 has the minimax-m2 architecture (capped at 25). Builds earn 10 for one publisher precision, the FP8 release. Terms earn 5, since the licence today allows only personal, research and non-profit use without MiniMax's written authorisation. Download earns 10, with no gate or account. Hosted earns 5, since Novita, Featherless AI and DeepInfra serve it through Hugging Face, while MiniMax's own API is closed to new users and retires it on 18 November 2026.\n\n## Score by category\n\n| Category | Weight | EXAONE 4.5 33B | MiniMax-M2.7 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 60 | EXAONE 4.5 33B +5 |\n| Risk | 20% (20 this run) | 50 | 30 | EXAONE 4.5 33B +20 |\n| Agent interface | 10% (10 this run) | 85 | 75 | EXAONE 4.5 33B +10 |\n| Documentation and upkeep | 10% (10 this run) | 50 | 42 | EXAONE 4.5 33B +8 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **57.6 · C** | |\n\n## Facts side by side\n\n| Fact | EXAONE 4.5 33B | MiniMax-M2.7 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | LG AI Research | MiniMax |\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) | MiniMax M2.7 non-commercial licence (MIT-style terms, commercial use needs MiniMax's written authorisation) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-06 | 2026-04-20 |\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 | 1.8/5 (4) |\n\n## How the scores have moved\n\n### EXAONE 4.5 33B\n\nNo score change since it was listed.\n\n### MiniMax-M2.7\n\n- +1.5 on 4 November 2026: product\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**MiniMax-M2.7.** A 230B mixture-of-experts model for coding and agent harnesses, with parallel tool calls, about 200K of context and parsers in vLLM and SGLang. It needs a 4 GPU server for FP8 or about 166 GB for a 4-bit library build, and it always thinks. The main risk is the licence, switched to non-commercial two days after release.\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### MiniMax-M2.7\n\n1. Check the use is personal, research or non-profit before starting it; anything commercial needs MiniMax's written authorisation, and commercial users must show Built with MiniMax M2.7\n2. Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.7 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think\n3. In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code\n4. MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache, with 196K the most per sequence\n5. On one large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.7 lists a 138 GB Q4_K_M GGUF and states 138 GB minimum memory\n6. Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends, and budget output tokens for thinking on every turn\n7. Pin Transformers 4.57.1 as MiniMax's guide does; a report that it fails on Transformers v5 has no answer\n8. Without the hardware, call it through Novita, Featherless AI or DeepInfra; MiniMax's own API retires M2.7 on 18 November 2026\n\n## Questions\n\n### Which is better for AI agents, EXAONE 4.5 33B or MiniMax-M2.7?\n\nEXAONE 4.5 33B scores 61 (C) on agent readiness against MiniMax-M2.7's 57.6 (C), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minimax-m2-7.json, and with the fewest tokens: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minimax-m2-7.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"exaone-4-5-33b\", \"b\": \"minimax-m2-7\"}`. 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    "title": "EXAONE 4.5 33B vs MiniMax-M2.7 for AI agents (2026) | Anchor Terminal",
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