{
  "data": {
    "a": {
      "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",
      "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": 56.1,
        "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": 61,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 71,
          "interface": 75,
          "reach": 60,
          "risk": 30,
          "upkeep": 27
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "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",
          "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": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 56.1
          }
        ],
        "editorialScores": {
          "capability": 71,
          "interface": 75,
          "reach": 60,
          "risk": 30,
          "upkeep": 27
        },
        "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-M2.7 --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-voice-cloning",
        "minimax-code",
        "minimax-m2-5"
      ],
      "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"
        ],
        "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": "2026-10-21",
        "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": 55
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/minimax-m2-7.json"
    },
    "answer": "Ornith-1.5-397B scores 59 (C) on agent readiness against MiniMax-M2.7's 56.1 (C), and leads in every scored category.",
    "b": {
      "slug": "ornith-1-5-397b",
      "name": "Ornith-1.5-397B",
      "vendor": "Ornith",
      "vendorUrl": "https://ornith.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Ornith-1.5-397B is Ornith's flagship open-weight mixture-of-experts reasoning model for coding and agent work, about 397B parameters on Qwen3.5's architecture with a 262,144-token context, published on Hugging Face in BF16, FP8, NVFP4 and GGUF and marked MIT.",
      "url": "https://www.anchorterminal.com/tools/ornith-1-5-397b",
      "markdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-397b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-397b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ornith-1-5-397b.json",
      "repo": "https://huggingface.co/ornith-ai/Ornith-1.5-397B",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-397B"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-397B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-397B-NVFP4"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-397B-GGUF"
        },
        {
          "registry": "ollama",
          "name": "ornith-1.5:397b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and Ornith's FP8, NVFP4 and GGUF builds are not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the MIT licence named in the card's metadata. The repository has no LICENSE file, and the card's licence link returns 404. Running it yourself costs the hardware, about 807 GB for the BF16 files, 418 GB for FP8 or 244 GB for Ornith's Q4_K_M GGUF (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/ornith-ai/Ornith-1.5-397B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "coding",
        "mit",
        "safetensors",
        "gguf",
        "fp8",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-08-23",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 59,
        "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": 53,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 65,
          "risk": 50,
          "upkeep": 36
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "Ornith's flagship coding and terminal-agent cousin, with XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It needs about 290 GB even at 4 bits, so a multi-GPU server, not the 128 GB reference machine. The main risk is an absent publisher, with no licence file, no safety results and files replaced without a note.",
        "bestFor": "A self-hosted frontier-scale coding agent on an 8-GPU server, where a clear licence file and an answering publisher aren't required",
        "strengths": [
          "Runs in vLLM, SGLang, Transformers, llama.cpp and Ollama from their released versions",
          "Ornith publishes FP8, NVFP4 and four GGUF precisions, all ungated",
          "The card names the tool and reasoning parsers for vLLM and SGLang, with a tool-call example",
          "262,144 tokens of native context",
          "Safetensors and GGUF only, no code in the repository"
        ],
        "weaknesses": [
          "Needs about 290 GB of memory at 4 bits and about 970 GB in BF16, so it doesn't fit the reference machine",
          "No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on",
          "No safety evaluation, agent-use guidance or security contact",
          "Every GGUF and the NVFP4 shards were replaced in place in August 2026 with no note, and the NVFP4 build now holds eight shards its index doesn't reference",
          "The two chat templates disagree on keeping earlier reasoning, structured output isn't documented and the card doesn't give the active parameters"
        ],
        "agentNotes": [
          "Start it with the card's vLLM command at `--tensor-parallel-size 8`, or `ollama run ornith-1.5:397b` on a machine with about 300 GB of memory",
          "In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed",
          "Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention",
          "Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it",
          "When downloading the NVFP4 build, fetch only the files its model.safetensors.index.json names (the 25-shard set, 241.7 GB), not the eight extra shards",
          "Pin a revision when you download, since Ornith has replaced files in place",
          "Validate JSON output yourself, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 59
          }
        ],
        "editorialScores": {
          "interface": 85,
          "reach": 65,
          "risk": 50,
          "upkeep": 36
        },
        "provenanceScore": 13,
        "checked": {
          "categories": {
            "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 ornith-1.5:397b\n# or, with the card's vLLM command on eight GPUs:\nvllm serve ornith-ai/Ornith-1.5-397B --served-model-name Ornith-1.5-397B --tensor-parallel-size 8 --max-model-len 262144 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"ornith-1.5:397b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/ornith-1-5-397b"
      },
      "sameCompany": [
        "ornith-1-5-35b-a3b",
        "ornith-1-5-9b"
      ],
      "weights": {
        "trainedBy": "Ornith",
        "builtOn": "Qwen3.5-397B-A17B, per the card's text (Ornith-1.0, which Ornith says it built on Qwen3.5 and Gemma 4, extended in 1.5); not declared in the metadata. Same qwen3_5_moe architecture and an identical parameter count to Qwen3.5-397B-A17B (Apache 2.0)",
        "params": "397B",
        "context": 262144,
        "goodFor": "Flagship coding and terminal-agent mixture-of-experts model with tool calling and thinking on by default, text and images in, for a multi-GPU server.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Ornith-1.5-397B-FP8",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 418.4,
            "memoryGB": 512,
            "format": "safetensors",
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-397B-NVFP4",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 319.5,
            "memoryGB": 512,
            "format": "safetensors",
            "note": "NVFP4 needs NVIDIA Blackwell. The repository's files sum to 319.5 GB, but its index uses only the 25-shard set (241.7 GB); eight further shards uploaded on 20 October 2026 aren't in the index. Either way it needs 512 GB"
          },
          {
            "name": "Ornith-1.5-397B-Q6_K",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q6_K.gguf",
            "who": "publisher",
            "precision": "q6_k",
            "sizeGB": 331,
            "memoryGB": 512,
            "format": "gguf",
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-397B-Q5_K_M",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q5_K_M.gguf",
            "who": "publisher",
            "precision": "q5_k_m",
            "sizeGB": 286.3,
            "memoryGB": 512,
            "format": "gguf",
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-397B-Q4_K_M",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 244.3,
            "memoryGB": 512,
            "format": "gguf",
            "note": "Add mmproj-Ornith-1.5-397B-BF16.gguf (0.9 GB) for image input. Too big for the 128 GB reference machine"
          },
          {
            "name": "ollama ornith-1.5:397b",
            "url": "https://ollama.com/library/ornith-1.5:397b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 242,
            "memoryGB": 512,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M (241 GB) with a BF16 vision projector (922 MB); the library has no latest tag. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB. Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-397B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 806.8,
            "memoryGB": 1024,
            "format": "safetensors",
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-397B-Q8_0",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 428.5,
            "memoryGB": 1024,
            "format": "gguf",
            "note": "Too big for the 128 GB reference machine"
          }
        ],
        "run": "ollama run ornith-1.5:397b",
        "runBuild": "ollama ornith-1.5:397b",
        "checked": "2026-10-21",
        "minMemoryGB": 512
      },
      "family": {
        "lab": "Ornith",
        "family": "Ornith-1",
        "url": "https://www.anchorterminal.com/open-weights/#ornith",
        "others": [
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            "url": "https://huggingface.co/ornith-ai/Ornith-1.0-9B",
            "params": "9B (dense)"
          },
          {
            "name": "Ornith-1.0-35B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.0-35B",
            "params": "35B (MoE)"
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            "name": "Ornith-1.5-9B-DFlash",
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            "params": "35B total, 3B active (MoE)",
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            "name": "Ornith-1.5-35B-A3B-DFlash",
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            "params": "0.39B (Hub safetensors count)"
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            "params": "122B (MoE)"
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            "name": "Ornith-1.5-397B-DFlash",
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            "params": "1.29B (Hub safetensors count)"
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        ],
        "unrestrictedCopies": 4
      },
      "area": "models",
      "provenance": {
        "legalEntity": "",
        "domain": "ornith.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "No legal entity named on the card, the blog or the repository. The citation reads Ornith Team, and the Hugging Face organisation ornith-ai (team plan, 10 members) isn't verified.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored. The licence is MIT in the card's metadata only; the LICENSE file the card links returns 404.",
          "No changelog for the files. Ollama's library page carries Ornith's dated release lines (19 August and 20 October 2026), and the commit messages are uploads, deletions and README updates.",
          "ornith.ai/.well-known/security.txt redirects to the home page, so there is no security.txt.",
          "unchecked: the domain's registration date."
        ],
        "score": 13
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/ornith-1-5-397b.json"
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    "facts": [
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        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "MiniMax",
        "b": "Ornith",
        "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 M2.7 non-commercial licence (MIT-style terms, commercial use needs MiniMax's written authorisation)",
        "b": "MIT",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-04-20",
        "b": "2026-08-23",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "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": "2/5 (4)",
        "name": "Agent reviews"
      }
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    "faq": [
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        "question": "Which is better for AI agents, MiniMax-M2.7 or Ornith-1.5-397B?"
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        "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"
      },
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        "aheadOn": [
          "Reach, 65 against 60",
          "Risk, 50 against 30",
          "Agent interface, 85 against 75",
          "Documentation and upkeep, 36 against 27"
        ],
        "also": null,
        "goodFor": "A self-hosted frontier-scale coding agent on an 8-GPU server, where a clear licence file and an answering publisher aren't required",
        "slug": "ornith-1-5-397b",
        "watchFor": "Needs about 290 GB of memory at 4 bits and about 970 GB in BF16, so it doesn't fit the reference machine"
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      "name": "Open-weight models you run yourself"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-7"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-397b.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-397b"
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-minimax-m2-7"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-397b.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-397b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-397b.json",
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-397b.json",
        "title": "Nemotron 3 Super 120B A12B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-397b.json",
        "title": "Nemotron 3 Ultra 550B A55B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-397b.json",
        "title": "occamy-1.0 vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-397b"
      },
      {
        "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/ornith-1-5-35b-a3b-vs-ornith-1-5-397b.json",
        "title": "Ornith-1.5-35B-A3B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-ornith-1-5-9b.json",
        "title": "Ornith-1.5-397B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Ornith-1.5-397B vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-4b-instruct-2507.json",
        "title": "Ornith-1.5-397B vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-0-8b.json",
        "title": "Ornith-1.5-397B vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-122b-a10b.json",
        "title": "Ornith-1.5-397B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-2b.json",
        "title": "Ornith-1.5-397B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-397b-a17b.json",
        "title": "Ornith-1.5-397B vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-4b.json",
        "title": "Ornith-1.5-397B vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-9b.json",
        "title": "Ornith-1.5-397B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-27b.json",
        "title": "Ornith-1.5-397B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-35b-a3b.json",
        "title": "Ornith-1.5-397B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-27b.json",
        "title": "Ornith-1.5-397B vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-flash-next.json",
        "title": "Ornith-1.5-397B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Ornith-1.5-397B vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-next.json",
        "title": "Ornith-1.5-397B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Ornith-1.5-397B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-2b-instruct.json",
        "title": "Ornith-1.5-397B vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Ornith-1.5-397B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-32b-instruct.json",
        "title": "Ornith-1.5-397B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-4b-instruct.json",
        "title": "Ornith-1.5-397B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-8b-instruct.json",
        "title": "Ornith-1.5-397B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Ornith-1.5-397B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-smollm3-3b.json",
        "title": "Ornith-1.5-397B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-spark-x2-6-4b.json",
        "title": "Ornith-1.5-397B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Ornith-1.5-397B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Ornith-1.5-397B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Ornith-1.5-397B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 5,
        "edge": "ornith-1-5-397b",
        "key": "reach",
        "minimax-m2-7": 60,
        "name": "Reach",
        "ornith-1-5-397b": 65,
        "weight": 25
      },
      {
        "by": 20,
        "edge": "ornith-1-5-397b",
        "key": "risk",
        "minimax-m2-7": 30,
        "name": "Risk",
        "ornith-1-5-397b": 50,
        "weight": 20
      },
      {
        "by": 10,
        "edge": "ornith-1-5-397b",
        "key": "interface",
        "minimax-m2-7": 75,
        "name": "Agent interface",
        "ornith-1-5-397b": 85,
        "weight": 10
      },
      {
        "by": 9,
        "edge": "ornith-1-5-397b",
        "key": "upkeep",
        "minimax-m2-7": 27,
        "name": "Documentation and upkeep",
        "ornith-1-5-397b": 36,
        "weight": 10
      }
    ],
    "summary": "Ornith-1.5-397B scores 59 (C) on agent readiness against MiniMax-M2.7's 56.1 (C), and leads in every scored category. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "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.",
      "ornith-1-5-397b": "Ornith's flagship coding and terminal-agent cousin, with XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It needs about 290 GB even at 4 bits, so a multi-GPU server, not the 128 GB reference machine. The main risk is an absent publisher, with no licence file, no safety results and files replaced without a note."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-397b",
    "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-397b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-397b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-397b.min.md"
  },
  "markdown": "Ornith-1.5-397B scores 59 (C) on agent readiness against MiniMax-M2.7's 56.1 (C), and leads in every scored category. Both do open-weight models you run yourself.\n\n- MiniMax-M2.7: grade C, 56.1/100, rank #76 of 100 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- Ornith-1.5-397B: grade C, 59/100, rank #64 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-397b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-397b.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-M2.7 (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### Ornith-1.5-397B (C)\n\nGood for: A self-hosted frontier-scale coding agent on an 8-GPU server, where a clear licence file and an answering publisher aren't required\n\nAhead on:\n- Reach, 65 against 60\n- Risk, 50 against 30\n- Agent interface, 85 against 75\n- Documentation and upkeep, 36 against 27\n\nWatch for: Needs about 290 GB of memory at 4 bits and about 970 GB in BF16, so it doesn't fit the reference machine\n\n\n## Score by category\n\n| Category | Weight | MiniMax-M2.7 | Ornith-1.5-397B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 60 | 65 | Ornith-1.5-397B +5 |\n| Risk | 20% (20 this run) | 30 | 50 | Ornith-1.5-397B +20 |\n| Agent interface | 10% (10 this run) | 75 | 85 | Ornith-1.5-397B +10 |\n| Documentation and upkeep | 10% (10 this run) | 27 | 36 | Ornith-1.5-397B +9 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **56.1 · C** | **59 · C** | |\n\n## Facts side by side\n\n| Fact | MiniMax-M2.7 | Ornith-1.5-397B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | MiniMax | Ornith |\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 M2.7 non-commercial licence (MIT-style terms, commercial use needs MiniMax's written authorisation) | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-04-20 | 2026-08-23 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | 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) | 2/5 (4) |\n\n## Verdicts\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**Ornith-1.5-397B.** Ornith's flagship coding and terminal-agent cousin, with XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It needs about 290 GB even at 4 bits, so a multi-GPU server, not the 128 GB reference machine. The main risk is an absent publisher, with no licence file, no safety results and files replaced without a note.\n\n## Before you call either\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### Ornith-1.5-397B\n\n1. Start it with the card's vLLM command at `--tensor-parallel-size 8`, or `ollama run ornith-1.5:397b` on a machine with about 300 GB of memory\n2. In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed\n3. Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n4. Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it\n5. When downloading the NVFP4 build, fetch only the files its model.safetensors.index.json names (the 25-shard set, 241.7 GB), not the eight extra shards\n6. Pin a revision when you download, since Ornith has replaced files in place\n7. Validate JSON output yourself, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, MiniMax-M2.7 or Ornith-1.5-397B?\n\nOrnith-1.5-397B scores 59 (C) on agent readiness against MiniMax-M2.7's 56.1 (C), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-397b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-397b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"minimax-m2-7\", \"b\": \"ornith-1-5-397b\"}`. From a terminal: `anchor compare minimax-m2-7 ornith-1-5-397b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/minimax-m2-7.json and https://www.anchorterminal.com/api/v1/tools/ornith-1-5-397b.json\n\n## Other comparisons with MiniMax-M2.7 or Ornith-1.5-397B\n\n- [Agents-A1-4B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-7.md)\n- [Agents-A1-4B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-397b.md)\n- [Apertus v1.5 8B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-7.md)\n- [Apertus v1.5 8B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-397b.md)\n- [Apodex-1.1-mini vs MiniMax-M2.7](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-7.md)\n- [Apodex-1.1-mini vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-397b.md)\n- [BigBang-v1 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-7.md)\n- [BigBang-v1 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-397b.md)\n- [Cosmos-Reason2-2B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minimax-m2-7.md)\n- [Cosmos-Reason2-2B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-397b.md)\n- [Darwin-36B-Opus vs MiniMax-M2.7](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minimax-m2-7.md)\n- [Darwin-36B-Opus vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-397b.md)\n- [DeepSeek-V3.2 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minimax-m2-7.md)\n- [DeepSeek-V3.2 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-397b.md)\n- [DeepSeek V4.1 Flash vs MiniMax-M2.7](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minimax-m2-7.md)\n- [DeepSeek V4.1 Flash vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-397b.md)\n- [DeepSeek-V4-Flash-0731 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minimax-m2-7.md)\n- [DeepSeek-V4-Flash-0731 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-397b.md)\n- [DiffusionGemma 26B A4B IT vs MiniMax-M2.7](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-minimax-m2-7.md)\n- [DiffusionGemma 26B A4B IT vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-397b.md)\n- [Gemma 4 12B IT vs MiniMax-M2.7](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minimax-m2-7.md)\n- [Gemma 4 12B IT vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-397b.md)\n- [Gemma 4 26B A4B IT vs MiniMax-M2.7](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minimax-m2-7.md)\n- [Gemma 4 26B A4B IT vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-397b.md)\n- [Gemma 4 31B IT vs MiniMax-M2.7](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-7.md)\n- [Gemma 4 31B IT vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-397b.md)\n- [Gemma 4 E2B IT vs MiniMax-M2.7](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minimax-m2-7.md)\n- [Gemma 4 E2B IT vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-397b.md)\n- [Gemma 4 E4B IT vs MiniMax-M2.7](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minimax-m2-7.md)\n- [Gemma 4 E4B IT vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-397b.md)\n- [GLM-4.5-Air vs MiniMax-M2.7](https://www.anchorterminal.com/compare/glm-4-5-air-vs-minimax-m2-7.md)\n- [GLM-4.5-Air vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-397b.md)\n- [GLM-4.7-Flash vs MiniMax-M2.7](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minimax-m2-7.md)\n- [GLM-4.7-Flash vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-397b.md)\n- [GLM-5.3-Flash vs MiniMax-M2.7](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minimax-m2-7.md)\n- [GLM-5.3-Flash vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-397b.md)\n- [GLM-5.3 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/glm-5-3-vs-minimax-m2-7.md)\n- [GLM-5.3 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-397b.md)\n- [gpt-oss-120b vs MiniMax-M2.7](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-minimax-m2-7.md)\n- [gpt-oss-120b vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ornith-1-5-397b.md)\n- [gpt-oss-20b vs MiniMax-M2.7](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minimax-m2-7.md)\n- [gpt-oss-20b vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ornith-1-5-397b.md)\n- [Granite 4.2 3B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minimax-m2-7.md)\n- [Granite 4.2 3B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-397b.md)\n- [Hy3 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/hy3-vs-minimax-m2-7.md)\n- [Hy3 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-397b.md)\n- [Inkling-Small vs MiniMax-M2.7](https://www.anchorterminal.com/compare/inkling-small-vs-minimax-m2-7.md)\n- [Inkling-Small vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/inkling-small-vs-ornith-1-5-397b.md)\n- [Inkling vs MiniMax-M2.7](https://www.anchorterminal.com/compare/inkling-vs-minimax-m2-7.md)\n- [Inkling vs 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30B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-muse-glimmer-30b.md)\n- [MiniMax-M2.7 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [MiniMax-M2.7 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-nano-30b-a3b.md)\n- [MiniMax-M2.7 vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-nano-4b.md)\n- [MiniMax-M2.7 vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [MiniMax-M2.7 vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-super-120b-a12b.md)\n- [MiniMax-M2.7 vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-ultra-550b-a55b.md)\n- [MiniMax-M2.7 vs occamy-1.0](https://www.anchorterminal.com/compare/minimax-m2-7-vs-occamy-1-0.md)\n- [MiniMax-M2.7 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/minimax-m2-7-vs-olmo-3-7b-think.md)\n- [MiniMax-M2.7 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-35b-a3b.md)\n- [MiniMax-M2.7 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-9b.md)\n- [MiniMax-M2.7 vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-30b-a3b-instruct-2507.md)\n- [MiniMax-M2.7 vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-4b-instruct-2507.md)\n- [MiniMax-M2.7 vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-0-8b.md)\n- [MiniMax-M2.7 vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-122b-a10b.md)\n- [MiniMax-M2.7 vs Qwen3.5-2B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-2b.md)\n- [MiniMax-M2.7 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-397b-a17b.md)\n- [MiniMax-M2.7 vs Qwen3.5-4B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-4b.md)\n- [MiniMax-M2.7 vs Qwen3.5-9B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-9b.md)\n- [MiniMax-M2.7 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-6-27b.md)\n- [MiniMax-M2.7 vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-6-35b-a3b.md)\n- [MiniMax-M2.7 vs Qwen3.8-27B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-8-27b.md)\n- [MiniMax-M2.7 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-8-flash-next.md)\n- [MiniMax-M2.7 vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-coder-30b-a3b-instruct.md)\n- [MiniMax-M2.7 vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-coder-next.md)\n- [MiniMax-M2.7 vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-omni-30b-a3b-instruct.md)\n- [MiniMax-M2.7 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-2b-instruct.md)\n- [MiniMax-M2.7 vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-30b-a3b-instruct.md)\n- [MiniMax-M2.7 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-32b-instruct.md)\n- [MiniMax-M2.7 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-4b-instruct.md)\n- [MiniMax-M2.7 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-8b-instruct.md)\n- [MiniMax-M2.7 vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwopus3-8-27b-flash-v2.md)\n- [MiniMax-M2.7 vs SmolLM3-3B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-smollm3-3b.md)\n- [MiniMax-M2.7 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-spark-x2-6-4b.md)\n- [MiniMax-M2.7 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-27b.md)\n- [MiniMax-M2.7 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-flash-next.md)\n- [MiniMax-M2.7 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/minimax-m2-7-vs-thinkingcap-qwen3-8-27b.md)\n- [Muse Glimmer 30B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-397b.md)\n- [Nemotron 3.5 Lightning 30B A3B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-397b.md)\n- [Nemotron 3 Nano 30B A3B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ornith-1-5-397b.md)\n- [Nemotron 3 Nano 4B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-397b.md)\n- [Nemotron 3 Nano Omni 30B A3B Reasoning vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-397b.md)\n- [Nemotron 3 Super 120B A12B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-397b.md)\n- [Nemotron 3 Ultra 550B A55B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-397b.md)\n- [occamy-1.0 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-397b.md)\n- [Olmo 3 7B Think vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-397b.md)\n- [Ornith-1.5-35B-A3B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ornith-1-5-397b.md)\n- [Ornith-1.5-397B vs 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        "name": "MiniMax-M2.7 vs Ornith-1.5-397B",
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