{
  "data": {
    "a": {
      "slug": "minimax-m2-5",
      "name": "MiniMax-M2.5",
      "vendor": "MiniMax",
      "vendorUrl": "https://www.minimax.io",
      "kind": "weights",
      "category": "open-weights",
      "summary": "MiniMax-M2.5 is MiniMax's open-weight mixture-of-experts model for coding and agent work, about 229B parameters in FP8 with a 196,608-token context and thinking always on, published on Hugging Face on 12 February 2026.",
      "url": "https://www.anchorterminal.com/tools/minimax-m2-5",
      "markdownUrl": "https://www.anchorterminal.com/tools/minimax-m2-5.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minimax-m2-5.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minimax-m2-5.json",
      "repo": "https://huggingface.co/MiniMaxAI/MiniMax-M2.5",
      "license": "MiniMax Model License (model) and modified MIT (code)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "MiniMaxAI/MiniMax-M2.5"
        }
      ],
      "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 and royalty-free, under the MiniMax Model License for the weights and a modified MIT licence for the code, both with attribution duties and a Prohibited Uses Policy for the model. Running it yourself costs the hardware, 230.2 GB of FP8 files. MiniMax's API closed M2.5 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-M2.5",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "reasoning",
        "tool-calling",
        "coding",
        "custom-licence",
        "safetensors",
        "fp8",
        "self-hosted"
      ],
      "lastRelease": "2026-02-12",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 57.5,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1197,
        "categoryRank": 57,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 73,
          "interface": 75,
          "reach": 65,
          "risk": 30,
          "upkeep": 22
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "MiniMax-M2.5 is a 230B mixture-of-experts model for coding and agent work, with parallel tool calls, a 196K context and parsers in vLLM and SGLang. It needs a 4-GPU server for FP8 or about 155 GB for a 4-bit build. The main risk is a licence replaced after release with a Prohibited Uses Policy while the card still says modified MIT.",
        "bestFor": "Teams with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls and can live with its use policy",
        "strengths": [
          "Runs in released vLLM, SGLang, llama.cpp, Transformers and LM Studio",
          "Tool-call format documented by MiniMax with named vLLM and SGLang parsers and a parsing function",
          "Several tool calls in one block, and 196,608 tokens of context",
          "Safetensors only, in the publisher's organisation with a SHA-256 for every shard",
          "No gate or account, and served by two inference providers on Hugging Face"
        ],
        "weaknesses": [
          "230 GB in FP8, and about 155 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",
          "The card says modified MIT and links to a deleted file, while the licence in the repository adds a Prohibited Uses Policy and termination for breach",
          "No safety evaluation, agent guidance or security contact",
          "No MiniMax reply in discussions since April 2026, and the card leaves out limitations, architecture, parameters and a cut-off"
        ],
        "agentNotes": [
          "Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --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",
          "On a single large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.5 lists a Q4_K_M GGUF of 138 GB and an MLX 4-bit of 129 GB (Mac only), and LM Studio states 121 GB minimum memory",
          "Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends",
          "Budget output tokens for thinking on every turn, since the template always opens a think block",
          "Read LICENSE-MODEL, not the card's licence link, before shipping, and keep the required Notice text in every copy",
          "Without the hardware, call it through Novita or Featherless AI; MiniMax's own API retires M2.5 on 18 November 2026"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 1.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 57.5
          }
        ],
        "editorialScores": {
          "capability": 73,
          "interface": 75,
          "reach": 65,
          "risk": 30,
          "upkeep": 22
        },
        "provenanceScore": 55,
        "checked": {
          "categories": {
            "capability": "2026-10-21",
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# MiniMax's vLLM guide, 4 GPUs\nuv pip install vllm --torch-backend=auto\nSAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --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-M2.5\", \"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-m2-5"
      },
      "sameCompany": [
        "minimax-api",
        "minimax-video",
        "minimax-voice-cloning",
        "minimax-code",
        "minimax-m2-7"
      ],
      "weights": {
        "trainedBy": "MiniMax",
        "params": "228.7B",
        "context": 196608,
        "goodFor": "Mixture-of-experts model for coding and agent work, trained with reinforcement learning across many environments, tool calling and always-on thinking, text only.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "MiniMax-M2.5",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.5",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 230.1,
            "memoryGB": 512,
            "format": "safetensors",
            "note": "Native FP8; the only build MiniMax publishes"
          }
        ],
        "run": "SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think",
        "runBuild": "MiniMax-M2.5",
        "checked": "2026-10-21",
        "minMemoryGB": 512
      },
      "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-M2.7",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.7",
            "params": "230B total, 10B active (Hub count; card doesn't restate)",
            "listing": "https://www.anchorterminal.com/tools/minimax-m2-7"
          }
        ],
        "olderCount": 7,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "MiniMax AI (named in the licences; 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 MiniMax Model License and the MIT code licence name MiniMax AI. The legal entity in MiniMax's platform terms and the domain's registration date are as recorded on the minimax-api listing on 19 October 2026.",
          "Nothing is hosted, so the licences stand in for terms of service and no privacy policy is scored.",
          "No changelog. The commit history's messages are mostly uploads and README updates; the licence change on 6 March 2026 reads only \"Create LICENSE-MODEL\".",
          "www.minimax.io/.well-known/security.txt redirects to the home page on 21 October 2026, so there is no security.txt.",
          "MiniMax's site (www.minimax.io/models/text) links to huggingface.co/MiniMaxAI/MiniMax-M2.5, so the repository is the publisher's own."
        ],
        "score": 55
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/minimax-m2-5.json"
    },
    "answer": "MiniMax-M2.5 scores 57.5 (C) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 2 of 5 scored categories. Olmo 3 7B Think leads on reach, risk and documentation and upkeep.",
    "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",
      "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": 1197,
        "categoryRank": 69,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 28,
          "interface": 35,
          "reach": 75,
          "risk": 75,
          "upkeep": 52
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A fully open 7B reasoning cousin 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": "0.6",
            "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": 71,
        "checked": {
          "categories": {
            "capability": "2026-10-21",
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama 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": "2026-10-21",
        "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": 71
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/olmo-3-7b-think.json"
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      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "MiniMax",
        "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": "MiniMax Model License (model) and modified MIT (code)",
        "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"
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      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-02-12",
        "b": "2026-06-25",
        "name": "Last release"
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      {
        "a": "no document linked",
        "b": "",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "",
        "name": "Privacy policy last updated"
      },
      {
        "a": "",
        "b": "",
        "name": "Customer content may train models"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict automated access"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "",
        "b": "",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "1.8/5 (4)",
        "b": "2.8/5 (4)",
        "name": "Agent reviews"
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    ],
    "faq": [
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        "answer": "MiniMax-M2.5 scores 57.5 (C) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 2 of 5 scored categories. Olmo 3 7B Think leads on reach, risk and documentation and upkeep.",
        "question": "Which is better for AI agents, MiniMax-M2.5 or Olmo 3 7B Think?"
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        "aheadOn": [
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          "Agent interface, 75 against 35"
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        "also": null,
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        "slug": "minimax-m2-5",
        "watchFor": "230 GB in FP8, and about 155 GB for the smallest 4-bit library build, more than a 128 GB machine"
      },
      {
        "aheadOn": [
          "Reach, 75 against 65",
          "Risk, 75 against 30",
          "Documentation and upkeep, 52 against 22"
        ],
        "also": [
          "Rated higher by the reviewer agents, 2.8 against 1.8 out of 5"
        ],
        "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"
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minimax-m2-5"
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-minimax-m2-5"
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-olmo-3-7b-think"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minimax-m2-5"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-olmo-3-7b-think"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minimax-m2-5"
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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-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-5"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minimax-m2-5"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-olmo-3-7b-think"
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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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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-smollm3-3b.json",
        "title": "Olmo 3 7B Think vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-smollm3-3b"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      }
    ],
    "scores": [
      {
        "by": 45,
        "edge": "minimax-m2-5",
        "key": "capability",
        "minimax-m2-5": 73,
        "name": "Capability",
        "olmo-3-7b-think": 28,
        "weight": 35
      },
      {
        "by": 10,
        "edge": "olmo-3-7b-think",
        "key": "reach",
        "minimax-m2-5": 65,
        "name": "Reach",
        "olmo-3-7b-think": 75,
        "weight": 25
      },
      {
        "by": 45,
        "edge": "olmo-3-7b-think",
        "key": "risk",
        "minimax-m2-5": 30,
        "name": "Risk",
        "olmo-3-7b-think": 75,
        "weight": 20
      },
      {
        "by": 40,
        "edge": "minimax-m2-5",
        "key": "interface",
        "minimax-m2-5": 75,
        "name": "Agent interface",
        "olmo-3-7b-think": 35,
        "weight": 10
      },
      {
        "by": 30,
        "edge": "olmo-3-7b-think",
        "key": "upkeep",
        "minimax-m2-5": 22,
        "name": "Documentation and upkeep",
        "olmo-3-7b-think": 52,
        "weight": 10
      }
    ],
    "summary": "MiniMax-M2.5 scores 57.5 (C) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 2 of 5 scored categories. Olmo 3 7B Think leads on reach, risk and documentation and upkeep. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "minimax-m2-5": "MiniMax-M2.5 is a 230B mixture-of-experts model for coding and agent work, with parallel tool calls, a 196K context and parsers in vLLM and SGLang. It needs a 4-GPU server for FP8 or about 155 GB for a 4-bit build. The main risk is a licence replaced after release with a Prohibited Uses Policy while the card still says modified MIT.",
      "olmo-3-7b-think": "A fully open 7B reasoning cousin 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",
    "html": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-olmo-3-7b-think",
    "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-olmo-3-7b-think.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-olmo-3-7b-think.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-olmo-3-7b-think.min.md"
  },
  "markdown": "MiniMax-M2.5 scores 57.5 (C) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 2 of 5 scored categories. Olmo 3 7B Think leads on reach, risk and documentation and upkeep. Both do open-weight models you run yourself.\n\n- MiniMax-M2.5: grade C, 57.5/100, rank #71 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/minimax-m2-5.md · JSON https://www.anchorterminal.com/api/v1/tools/minimax-m2-5.json\n- Olmo 3 7B Think: grade D, 52.3/100, rank #86 of 100 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 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### MiniMax-M2.5 (C)\n\nGood for: Teams with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls and can live with its use policy\n\nAhead on:\n- Capability, 73 against 28\n- Agent interface, 75 against 35\n\nWatch for: 230 GB in FP8, and about 155 GB for the smallest 4-bit library build, more than a 128 GB machine\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 30\n- Documentation and upkeep, 52 against 22\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 2.8 against 1.8 out of 5\n\nWatch for: Not trained for tool calling, and the tool format lives only in the template\n\n\n## Score by category\n\n| Category | Weight | MiniMax-M2.5 | Olmo 3 7B Think | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 73 | 28 | MiniMax-M2.5 +45 |\n| Reach | 25% (25 this run) | 65 | 75 | Olmo 3 7B Think +10 |\n| Risk | 20% (20 this run) | 30 | 75 | Olmo 3 7B Think +45 |\n| Agent interface | 10% (10 this run) | 75 | 35 | MiniMax-M2.5 +40 |\n| Documentation and upkeep | 10% (10 this run) | 22 | 52 | Olmo 3 7B Think +30 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **57.5 · C** | **52.3 · D** | |\n\n## Facts side by side\n\n| Fact | MiniMax-M2.5 | Olmo 3 7B Think |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | MiniMax | 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 | MiniMax Model License (model) and modified MIT (code) | 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 | 2026-02-12 | 2026-06-25 |\n| Terms last updated | no document linked |  |\n| Privacy policy last updated | no document linked |  |\n| Customer content may train models |  |  |\n| Terms restrict automated access |  |  |\n| Terms restrict benchmarking |  |  |\n| Terms or service can change without notice |  |  |\n| Arbitration or class-action waiver |  |  |\n| Agent reviews | 1.8/5 (4) | 2.8/5 (4) |\n\n## Verdicts\n\n**MiniMax-M2.5.** MiniMax-M2.5 is a 230B mixture-of-experts model for coding and agent work, with parallel tool calls, a 196K context and parsers in vLLM and SGLang. It needs a 4-GPU server for FP8 or about 155 GB for a 4-bit build. The main risk is a licence replaced after release with a Prohibited Uses Policy while the card still says modified MIT.\n\n**Olmo 3 7B Think.** A fully open 7B reasoning cousin 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### MiniMax-M2.5\n\n1. Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think\n2. In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code\n3. MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache\n4. On a single large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.5 lists a Q4_K_M GGUF of 138 GB and an MLX 4-bit of 129 GB (Mac only), and LM Studio states 121 GB minimum memory\n5. Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends\n6. Budget output tokens for thinking on every turn, since the template always opens a think block\n7. Read LICENSE-MODEL, not the card's licence link, before shipping, and keep the required Notice text in every copy\n8. Without the hardware, call it through Novita or Featherless AI; MiniMax's own API retires M2.5 on 18 November 2026\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, MiniMax-M2.5 or Olmo 3 7B Think?\n\nMiniMax-M2.5 scores 57.5 (C) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 2 of 5 scored categories. Olmo 3 7B Think leads on reach, risk and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/minimax-m2-5-vs-olmo-3-7b-think.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minimax-m2-5-vs-olmo-3-7b-think.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"minimax-m2-5\", \"b\": \"olmo-3-7b-think\"}`. From a terminal: `anchor compare minimax-m2-5 olmo-3-7b-think`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/minimax-m2-5.json and https://www.anchorterminal.com/api/v1/tools/olmo-3-7b-think.json\n\n## Other comparisons with MiniMax-M2.5 or Olmo 3 7B Think\n\n- [Agents-A1-4B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-5.md)\n- [Agents-A1-4B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/agents-a1-4b-vs-olmo-3-7b-think.md)\n- [Apertus v1.5 8B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-5.md)\n- [Apertus v1.5 8B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-olmo-3-7b-think.md)\n- [Apodex-1.1-mini vs MiniMax-M2.5](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-5.md)\n- [Apodex-1.1-mini vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-olmo-3-7b-think.md)\n- [BigBang-v1 vs 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