{
  "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": "Ornith-1.5-9B scores 61 (C) on agent readiness against MiniMax-M2.5's 57.5 (C), and leads in every scored category.",
    "b": {
      "slug": "ornith-1-5-9b",
      "name": "Ornith-1.5-9B",
      "vendor": "Ornith",
      "vendorUrl": "https://ornith.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Ornith-1.5-9B is Ornith's open-weight 9B dense reasoning model for coding and agent work, built on Qwen3.5's architecture with a 262,144-token context, published on Hugging Face in BF16, GGUF, MLX and NVFP4 and marked MIT.",
      "url": "https://www.anchorterminal.com/tools/ornith-1-5-9b",
      "markdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-9b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-9b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ornith-1-5-9b.json",
      "repo": "https://huggingface.co/ornith-ai/Ornith-1.5-9B",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-9B"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-9B-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-9B-MLX-4bit"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-9B-NVFP4"
        },
        {
          "registry": "ollama",
          "name": "ornith-1.5:9b"
        }
      ],
      "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 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 19 GB for the BF16 files or 5.8 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-1.5-9B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "multimodal",
        "reasoning",
        "tool-calling",
        "coding",
        "mit",
        "safetensors",
        "gguf",
        "mlx",
        "self-hosted"
      ],
      "lastRelease": "2026-08-23",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1197,
        "categoryRank": 37,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 95,
          "risk": 50,
          "upkeep": 31
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A small coding and terminal-agent cousin with thinking on by default, XML tool calls that vLLM and SGLang parse, and 262,144 tokens of context. Ornith's own 4-bit builds run in under 8 GB in most local runtimes. The main risk is an absent publisher, with no licence file, no safety results, files replaced without a note and no replies.",
        "bestFor": "Local coding and terminal agents on a laptop or a single consumer GPU, where a clear licence file and an answering publisher aren't required",
        "strengths": [
          "Fits in about 6 to 8 GB from Ornith's own MLX 4-bit, Q4_K_M GGUF or Ollama's build",
          "Runs in vLLM, SGLang, Transformers, llama.cpp, Ollama and LM Studio from their released versions",
          "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 gate and no code in the repository"
        ],
        "weaknesses": [
          "No LICENSE file though the card links one, and no notice for the Qwen3.5 and Gemma 4 work it builds on",
          "No safety evaluation, agent-use guidance or security contact",
          "The fourth weight shard and every GGUF were replaced in place on 23 and 24 August 2026 with no note",
          "Ornith answered none of the 15 discussions",
          "The two chat templates disagree on keeping earlier reasoning, and structured output isn't documented"
        ],
        "agentNotes": [
          "Start it with `ollama run ornith-1.5:9b`, or `llama-server -hf ornith-ai/Ornith-1.5-9B-GGUF` with the Q4_K_M file",
          "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",
          "Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 with presence_penalty 1.5 for general tasks, per the card",
          "Pin a revision when you download, since Ornith has replaced files in place, and add mmproj-Ornith-1.5-9B-BF16.gguf (0.9 GB) for image input in llama.cpp",
          "Validate JSON output yourself, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 85,
          "reach": 95,
          "risk": 50,
          "upkeep": 31
        },
        "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-1.5:9b\n# or, with the card's vLLM command:\nvllm serve ornith-ai/Ornith-1.5-9B --served-model-name Ornith-1.5-9B --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-1.5:9b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/ornith-1-5-9b"
      },
      "sameCompany": [
        "ornith-1-5-35b-a3b",
        "ornith-1-5-397b"
      ],
      "weights": {
        "trainedBy": "Ornith",
        "builtOn": "Ornith-1.0 (which Ornith says it built on Qwen3.5 and Gemma 4); qwen3_5 architecture",
        "params": "9B",
        "context": 262144,
        "goodFor": "Coding and terminal-agent model with tool calling and thinking on by default, text and images in.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Ornith-1.5-9B-Q5_K_M",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-GGUF/blob/main/Ornith-1.5-9B-Q5_K_M.gguf",
            "who": "publisher",
            "precision": "q5_k_m",
            "sizeGB": 6.6,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-9B-Q4_K_M",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-GGUF/blob/main/Ornith-1.5-9B-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 5.8,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Add mmproj-Ornith-1.5-9B-BF16.gguf (0.9 GB) for image input"
          },
          {
            "name": "Ornith-1.5-9B-MLX-4bit",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-MLX-4bit",
            "who": "publisher",
            "precision": "4bit",
            "sizeGB": 5,
            "memoryGB": 8,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          },
          {
            "name": "ollama ornith-1.5:9b",
            "url": "https://ollama.com/library/ornith-1.5:9b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 6.6,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M with a BF16 vision projector, linked from Ornith's card; the library has no latest tag. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant"
          },
          {
            "name": "Ornith-1.5-9B-Q8_0",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-GGUF/blob/main/Ornith-1.5-9B-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 9.8,
            "memoryGB": 12,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-9B-Q6_K",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-GGUF/blob/main/Ornith-1.5-9B-Q6_K.gguf",
            "who": "publisher",
            "precision": "q6_k",
            "sizeGB": 7.6,
            "memoryGB": 12,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-9B-MLX-8bit",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-MLX-8bit",
            "who": "publisher",
            "precision": "8bit",
            "sizeGB": 9.5,
            "memoryGB": 12,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          },
          {
            "name": "Ornith-1.5-9B-MLX-6bit",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-MLX-6bit",
            "who": "publisher",
            "precision": "6bit",
            "sizeGB": 7.3,
            "memoryGB": 12,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          },
          {
            "name": "Ornith-1.5-9B-NVFP4",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 8.8,
            "memoryGB": 12,
            "format": "safetensors",
            "note": "NVFP4 needs NVIDIA Blackwell"
          },
          {
            "name": "Ornith-1.5-9B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 19.3,
            "memoryGB": 24,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-9B-BF16",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-GGUF/blob/main/Ornith-1.5-9B-BF16.gguf",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 18.4,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-9B-MLX",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-MLX",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 17.9,
            "memoryGB": 24,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          }
        ],
        "run": "ollama run ornith-1.5:9b",
        "runBuild": "ollama ornith-1.5:9b",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "Ornith",
        "family": "Ornith-1",
        "url": "https://www.anchorterminal.com/open-weights/#ornith",
        "others": [
          {
            "name": "Ornith-1.0-9B",
            "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)"
          },
          {
            "name": "Ornith-1.0-397B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.0-397B",
            "params": "397B (MoE)"
          },
          {
            "name": "Ornith-1.5-9B-DFlash",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-DFlash",
            "params": "1.29B (Hub safetensors count)"
          },
          {
            "name": "Ornith-1.5-35B-A3B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B",
            "params": "35B total, 3B active (MoE)",
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            "name": "Ornith-1.5-35B-A3B-DFlash",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-DFlash",
            "params": "0.39B (Hub safetensors count)"
          },
          {
            "name": "Ornith-1.5-122B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B",
            "params": "122B (MoE)"
          },
          {
            "name": "Ornith-1.5-397B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B",
            "params": "397B (MoE)",
            "listing": "https://www.anchorterminal.com/tools/ornith-1-5-397b"
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          {
            "name": "Ornith-1.5-397B-DFlash",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-DFlash",
            "params": "1.29B (Hub safetensors count)"
          }
        ],
        "unrestrictedCopies": 5
      },
      "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 blog's footer reads 2026 Ornith Team, and the Hugging Face organisation ornith-ai 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 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, since RDAP answered 429 on 21 October 2026."
        ],
        "score": 13
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/ornith-1-5-9b.json"
    },
    "facts": [
      {
        "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 Model License (model) and modified MIT (code)",
        "b": "MIT",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-02-12",
        "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": "2.3/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Ornith-1.5-9B scores 61 (C) on agent readiness against MiniMax-M2.5's 57.5 (C), and leads in every scored category.",
        "question": "Which is better for AI agents, MiniMax-M2.5 or Ornith-1.5-9B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "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",
        "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, 95 against 65",
          "Risk, 50 against 30",
          "Agent interface, 85 against 75",
          "Documentation and upkeep, 31 against 22"
        ],
        "also": [
          "Rated higher by the reviewer agents, 2.3 against 1.8 out of 5"
        ],
        "goodFor": "Local coding and terminal agents on a laptop or a single consumer GPU, where a clear licence file and an answering publisher aren't required",
        "slug": "ornith-1-5-9b",
        "watchFor": "No LICENSE file though the card links one, and no notice for the Qwen3.5 and Gemma 4 work it builds on"
      }
    ],
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
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      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-9b.json",
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        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-5.json",
        "title": "Apertus v1.5 8B vs MiniMax-M2.5",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-5"
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-9b.json",
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        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-5.json",
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        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-9b.json",
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        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-9b.json",
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        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-9b.json",
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        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minimax-m2-5.json",
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        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-9b.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-9b.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minimax-m2-5.json",
        "title": "DeepSeek V4.1 Flash vs MiniMax-M2.5",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minimax-m2-5"
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-9b.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minimax-m2-5.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minimax-m2-5"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-9b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-9b"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minimax-m2-5"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-9b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-9b"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-5"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-9b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-9b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-9b"
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        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-minimax-m2-5"
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        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-9b.json",
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-9b"
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minimax-m2-5"
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-minimax-m2-5"
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-minimax-m2-5"
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ornith-1-5-9b.json",
        "title": "gpt-oss-120b vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ornith-1-5-9b"
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minimax-m2-5"
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      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ornith-1-5-9b.json",
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ornith-1-5-9b"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minimax-m2-5"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-9b.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-minimax-m2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-9b.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-9b"
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        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-minimax-m2-5"
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        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-9b.json",
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      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-9b.json",
        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-9b.json",
        "title": "Nemotron 3 Super 120B A12B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-9b.json",
        "title": "Nemotron 3 Ultra 550B A55B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-9b.json",
        "title": "occamy-1.0 vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-9b"
      },
      {
        "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/ornith-1-5-35b-a3b-vs-ornith-1-5-9b.json",
        "title": "Ornith-1.5-35B-A3B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ornith-1-5-9b"
      },
      {
        "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-1-5-9b-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Ornith-1.5-9B vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-4b-instruct-2507.json",
        "title": "Ornith-1.5-9B vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-0-8b.json",
        "title": "Ornith-1.5-9B vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-122b-a10b.json",
        "title": "Ornith-1.5-9B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-2b.json",
        "title": "Ornith-1.5-9B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-397b-a17b.json",
        "title": "Ornith-1.5-9B vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-4b.json",
        "title": "Ornith-1.5-9B vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-9b.json",
        "title": "Ornith-1.5-9B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-27b.json",
        "title": "Ornith-1.5-9B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-35b-a3b.json",
        "title": "Ornith-1.5-9B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-27b.json",
        "title": "Ornith-1.5-9B vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-flash-next.json",
        "title": "Ornith-1.5-9B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Ornith-1.5-9B vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-coder-next.json",
        "title": "Ornith-1.5-9B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Ornith-1.5-9B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-2b-instruct.json",
        "title": "Ornith-1.5-9B vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Ornith-1.5-9B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-32b-instruct.json",
        "title": "Ornith-1.5-9B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-4b-instruct.json",
        "title": "Ornith-1.5-9B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-8b-instruct.json",
        "title": "Ornith-1.5-9B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Ornith-1.5-9B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-smollm3-3b.json",
        "title": "Ornith-1.5-9B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-spark-x2-6-4b.json",
        "title": "Ornith-1.5-9B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Ornith-1.5-9B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Ornith-1.5-9B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Ornith-1.5-9B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 30,
        "edge": "ornith-1-5-9b",
        "key": "reach",
        "minimax-m2-5": 65,
        "name": "Reach",
        "ornith-1-5-9b": 95,
        "weight": 25
      },
      {
        "by": 20,
        "edge": "ornith-1-5-9b",
        "key": "risk",
        "minimax-m2-5": 30,
        "name": "Risk",
        "ornith-1-5-9b": 50,
        "weight": 20
      },
      {
        "by": 10,
        "edge": "ornith-1-5-9b",
        "key": "interface",
        "minimax-m2-5": 75,
        "name": "Agent interface",
        "ornith-1-5-9b": 85,
        "weight": 10
      },
      {
        "by": 9,
        "edge": "ornith-1-5-9b",
        "key": "upkeep",
        "minimax-m2-5": 22,
        "name": "Documentation and upkeep",
        "ornith-1-5-9b": 31,
        "weight": 10
      }
    ],
    "summary": "Ornith-1.5-9B scores 61 (C) on agent readiness against MiniMax-M2.5's 57.5 (C), and leads in every scored category. 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.",
      "ornith-1-5-9b": "A small coding and terminal-agent cousin with thinking on by default, XML tool calls that vLLM and SGLang parse, and 262,144 tokens of context. Ornith's own 4-bit builds run in under 8 GB in most local runtimes. The main risk is an absent publisher, with no licence file, no safety results, files replaced without a note and no replies."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-ornith-1-5-9b",
    "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-ornith-1-5-9b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-ornith-1-5-9b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-ornith-1-5-9b.min.md"
  },
  "markdown": "Ornith-1.5-9B scores 61 (C) on agent readiness against MiniMax-M2.5's 57.5 (C), and leads in every scored category. 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- Ornith-1.5-9B: grade C, 61/100, rank #44 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-9b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-9b.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\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### Ornith-1.5-9B (C)\n\nGood for: Local coding and terminal agents on a laptop or a single consumer GPU, where a clear licence file and an answering publisher aren't required\n\nAhead on:\n- Reach, 95 against 65\n- Risk, 50 against 30\n- Agent interface, 85 against 75\n- Documentation and upkeep, 31 against 22\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 2.3 against 1.8 out of 5\n\nWatch for: No LICENSE file though the card links one, and no notice for the Qwen3.5 and Gemma 4 work it builds on\n\n\n## Score by category\n\n| Category | Weight | MiniMax-M2.5 | Ornith-1.5-9B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 95 | Ornith-1.5-9B +30 |\n| Risk | 20% (20 this run) | 30 | 50 | Ornith-1.5-9B +20 |\n| Agent interface | 10% (10 this run) | 75 | 85 | Ornith-1.5-9B +10 |\n| Documentation and upkeep | 10% (10 this run) | 22 | 31 | Ornith-1.5-9B +9 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **57.5 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | MiniMax-M2.5 | Ornith-1.5-9B |\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 Model License (model) and modified MIT (code) | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-02-12 | 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) | 2.3/5 (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**Ornith-1.5-9B.** A small coding and terminal-agent cousin with thinking on by default, XML tool calls that vLLM and SGLang parse, and 262,144 tokens of context. Ornith's own 4-bit builds run in under 8 GB in most local runtimes. The main risk is an absent publisher, with no licence file, no safety results, files replaced without a note and no replies.\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### Ornith-1.5-9B\n\n1. Start it with `ollama run ornith-1.5:9b`, or `llama-server -hf ornith-ai/Ornith-1.5-9B-GGUF` with the Q4_K_M file\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. Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 with presence_penalty 1.5 for general tasks, per the card\n6. Pin a revision when you download, since Ornith has replaced files in place, and add mmproj-Ornith-1.5-9B-BF16.gguf (0.9 GB) for image input in llama.cpp\n7. Validate JSON output yourself, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, MiniMax-M2.5 or Ornith-1.5-9B?\n\nOrnith-1.5-9B scores 61 (C) on agent readiness against MiniMax-M2.5's 57.5 (C), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/minimax-m2-5-vs-ornith-1-5-9b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minimax-m2-5-vs-ornith-1-5-9b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"minimax-m2-5\", \"b\": \"ornith-1-5-9b\"}`. From a terminal: `anchor compare minimax-m2-5 ornith-1-5-9b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/minimax-m2-5.json and https://www.anchorterminal.com/api/v1/tools/ornith-1-5-9b.json\n\n## Other comparisons with MiniMax-M2.5 or Ornith-1.5-9B\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 Ornith-1.5-9B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-9b.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 Ornith-1.5-9B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-9b.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 Ornith-1.5-9B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-9b.md)\n- [BigBang-v1 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-5.md)\n- [BigBang-v1 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-9b.md)\n- [Cosmos-Reason2-2B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minimax-m2-5.md)\n- [Cosmos-Reason2-2B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-9b.md)\n- [Darwin-36B-Opus vs MiniMax-M2.5](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minimax-m2-5.md)\n- [Darwin-36B-Opus vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-9b.md)\n- [DeepSeek-V3.2 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minimax-m2-5.md)\n- [DeepSeek-V3.2 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-9b.md)\n- [DeepSeek V4.1 Flash vs MiniMax-M2.5](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minimax-m2-5.md)\n- [DeepSeek V4.1 Flash vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-9b.md)\n- [DeepSeek-V4-Flash-0731 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minimax-m2-5.md)\n- [DeepSeek-V4-Flash-0731 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-9b.md)\n- [DiffusionGemma 26B A4B IT vs MiniMax-M2.5](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-minimax-m2-5.md)\n- [DiffusionGemma 26B A4B IT vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-9b.md)\n- [Gemma 4 12B IT vs MiniMax-M2.5](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minimax-m2-5.md)\n- [Gemma 4 12B IT vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-9b.md)\n- [Gemma 4 26B A4B IT vs MiniMax-M2.5](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minimax-m2-5.md)\n- [Gemma 4 26B A4B IT vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-9b.md)\n- [Gemma 4 31B IT vs MiniMax-M2.5](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-5.md)\n- [Gemma 4 31B IT vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-9b.md)\n- [Gemma 4 E2B IT vs MiniMax-M2.5](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minimax-m2-5.md)\n- [Gemma 4 E2B IT vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-9b.md)\n- [Gemma 4 E4B IT vs MiniMax-M2.5](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minimax-m2-5.md)\n- [Gemma 4 E4B IT vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-9b.md)\n- [GLM-4.5-Air vs MiniMax-M2.5](https://www.anchorterminal.com/compare/glm-4-5-air-vs-minimax-m2-5.md)\n- [GLM-4.5-Air vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-9b.md)\n- [GLM-4.7-Flash vs MiniMax-M2.5](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minimax-m2-5.md)\n- [GLM-4.7-Flash vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-9b.md)\n- [GLM-5.3-Flash vs MiniMax-M2.5](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minimax-m2-5.md)\n- [GLM-5.3-Flash vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-9b.md)\n- [GLM-5.3 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/glm-5-3-vs-minimax-m2-5.md)\n- [GLM-5.3 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-9b.md)\n- [gpt-oss-120b vs MiniMax-M2.5](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-minimax-m2-5.md)\n- [gpt-oss-120b vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ornith-1-5-9b.md)\n- [gpt-oss-20b vs MiniMax-M2.5](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minimax-m2-5.md)\n- [gpt-oss-20b vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ornith-1-5-9b.md)\n- [Granite 4.2 3B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minimax-m2-5.md)\n- [Granite 4.2 3B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-9b.md)\n- [Hy3 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/hy3-vs-minimax-m2-5.md)\n- [Hy3 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-9b.md)\n- [Inkling-Small vs MiniMax-M2.5](https://www.anchorterminal.com/compare/inkling-small-vs-minimax-m2-5.md)\n- [Inkling-Small vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/inkling-small-vs-ornith-1-5-9b.md)\n- [Inkling vs MiniMax-M2.5](https://www.anchorterminal.com/compare/inkling-vs-minimax-m2-5.md)\n- [Inkling vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-9b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniMax-M2.5](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minimax-m2-5.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ornith-1-5-9b.md)\n- [Kimi K3 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/kimi-k3-vs-minimax-m2-5.md)\n- [Kimi K3 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-9b.md)\n- [Kolibri-1 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/kolibri-1-vs-minimax-m2-5.md)\n- [Kolibri-1 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-9b.md)\n- [Laguna M.1 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-5.md)\n- [Laguna M.1 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-9b.md)\n- [Laguna S 2.1 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minimax-m2-5.md)\n- [Laguna S 2.1 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-9b.md)\n- [Laguna XS 2.1 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minimax-m2-5.md)\n- [Laguna XS 2.1 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-9b.md)\n- [LFM2.5-2.6B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m2-5.md)\n- [LFM2.5-2.6B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-9b.md)\n- [Ling-3.0-tiny vs MiniMax-M2.5](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-minimax-m2-5.md)\n- [Ling-3.0-tiny vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-ornith-1-5-9b.md)\n- [MiMo-V2.6 Distill Qwen 9B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-minimax-m2-5.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-ornith-1-5-9b.md)\n- [MiniCPM-o 4.5 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-minimax-m2-5.md)\n- [MiniCPM-o 4.5 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-9b.md)\n- [MiniCPM5-2B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/minicpm5-2b-vs-minimax-m2-5.md)\n- [MiniCPM5-2B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/minicpm5-2b-vs-ornith-1-5-9b.md)\n- [MiniMax-M2.5 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/minimax-m2-5-vs-minimax-m2-7.md)\n- [MiniMax-M2.5 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-muse-glimmer-30b.md)\n- [MiniMax-M2.5 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [MiniMax-M2.5 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-nemotron-3-nano-30b-a3b.md)\n- [MiniMax-M2.5 vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-nemotron-3-nano-4b.md)\n- [MiniMax-M2.5 vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/minimax-m2-5-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [MiniMax-M2.5 vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-nemotron-3-super-120b-a12b.md)\n- [MiniMax-M2.5 vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-nemotron-3-ultra-550b-a55b.md)\n- [MiniMax-M2.5 vs occamy-1.0](https://www.anchorterminal.com/compare/minimax-m2-5-vs-occamy-1-0.md)\n- [MiniMax-M2.5 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/minimax-m2-5-vs-olmo-3-7b-think.md)\n- [MiniMax-M2.5 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-ornith-1-5-35b-a3b.md)\n- [MiniMax-M2.5 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-ornith-1-5-397b.md)\n- [MiniMax-M2.5 vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-30b-a3b-instruct-2507.md)\n- [MiniMax-M2.5 vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-4b-instruct-2507.md)\n- [MiniMax-M2.5 vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-5-0-8b.md)\n- [MiniMax-M2.5 vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-5-122b-a10b.md)\n- [MiniMax-M2.5 vs Qwen3.5-2B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-5-2b.md)\n- [MiniMax-M2.5 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-5-397b-a17b.md)\n- [MiniMax-M2.5 vs Qwen3.5-4B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-5-4b.md)\n- [MiniMax-M2.5 vs Qwen3.5-9B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-5-9b.md)\n- [MiniMax-M2.5 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-6-27b.md)\n- [MiniMax-M2.5 vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-6-35b-a3b.md)\n- [MiniMax-M2.5 vs Qwen3.8-27B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-8-27b.md)\n- [MiniMax-M2.5 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-8-flash-next.md)\n- [MiniMax-M2.5 vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-coder-30b-a3b-instruct.md)\n- [MiniMax-M2.5 vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-coder-next.md)\n- [MiniMax-M2.5 vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-omni-30b-a3b-instruct.md)\n- [MiniMax-M2.5 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-2b-instruct.md)\n- [MiniMax-M2.5 vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-30b-a3b-instruct.md)\n- [MiniMax-M2.5 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-32b-instruct.md)\n- [MiniMax-M2.5 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-4b-instruct.md)\n- [MiniMax-M2.5 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-8b-instruct.md)\n- [MiniMax-M2.5 vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwopus3-8-27b-flash-v2.md)\n- [MiniMax-M2.5 vs SmolLM3-3B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-smollm3-3b.md)\n- [MiniMax-M2.5 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-spark-x2-6-4b.md)\n- [MiniMax-M2.5 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-27b.md)\n- [MiniMax-M2.5 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-flash-next.md)\n- [MiniMax-M2.5 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/minimax-m2-5-vs-thinkingcap-qwen3-8-27b.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- [Muse Glimmer 30B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-9b.md)\n- [Nemotron 3.5 Lightning 30B A3B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-9b.md)\n- [Nemotron 3 Nano 30B A3B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ornith-1-5-9b.md)\n- [Nemotron 3 Nano 4B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-9b.md)\n- [Nemotron 3 Nano Omni 30B A3B Reasoning vs 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