{
  "data": {
    "a": {
      "slug": "north-mini-code-1-0",
      "name": "North Mini Code 1.0",
      "vendor": "Cohere",
      "vendorUrl": "https://cohere.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "North Mini Code 1.0 is Cohere's open-weight coding agent model, a mixture of experts of 30B parameters with 3B active, trained for tool use, with interleaved thinking and a 256K context. Apache 2.0. Cohere's 4-bit build is 19 GB.",
      "url": "https://www.anchorterminal.com/tools/north-mini-code-1-0",
      "markdownUrl": "https://www.anchorterminal.com/tools/north-mini-code-1-0.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/north-mini-code-1-0.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/north-mini-code-1-0.json",
      "dns": "north-mini-code-1-0.grade.anchorterminal.com",
      "repo": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "CohereLabs/North-Mini-Code-1.0"
        },
        {
          "registry": "huggingface",
          "name": "CohereLabs/North-Mini-Code-1.0-fp8"
        },
        {
          "registry": "huggingface",
          "name": "CohereLabs/North-Mini-Code-1.0-w4a16"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the repositories is gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under Apache 2.0. The card calls it a research release. Running it yourself takes 19.4 GB at 4 bits (w4a16), 32.1 GB in FP8 or 61.0 GB in BF16 (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "coding",
        "agentic",
        "reasoning",
        "tool-calling",
        "long-context",
        "apache-2.0",
        "safetensors",
        "fp8",
        "4-bit",
        "self-hosted"
      ],
      "lastRelease": "2026-07-29",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 63,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 85,
          "risk": 60,
          "upkeep": 36
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A coding agent model that fits one 24 GB GPU at 4 bits, trained for tool use, with a 256K context, Apache 2.0 and four released runtimes. It's a research release and reads like one: nothing on limitations, safety or data, the card's run instructions are out of date, and Cohere hasn't answered a question on the repository.",
        "bestFor": "Developers who want a coding agent on their own GPU with a permissive licence",
        "strengths": [
          "Trained for tool use in coding agents, with parsers in released vLLM",
          "19 GB at 4 bits from Cohere, and a 256K context",
          "Apache 2.0, ungated",
          "Four released runtimes carry the architecture",
          "Tool use documented with a worked example"
        ],
        "weaknesses": [
          "No limitations, safety results, languages or data description on the card",
          "No reply from Cohere on the repository's discussions",
          "The card's instructions still point at unreleased vLLM and Transformers",
          "No GGUF from Cohere",
          "Text only",
          "No hosted API named"
        ],
        "agentNotes": [
          "Serve it with vLLM 0.33.0 or later and `--tool-call-parser cohere_command4 --reasoning-parser cohere_command4 --enable-auto-tool-choice`, with `cohere_melody` installed",
          "Pass the model's thinking back with its tool calls on every turn. The card says results drop without it",
          "Describe tools in JSON schema and append tool results as a dictionary with the tool role",
          "Use temperature 1.0 and top_p 0.95",
          "For llama.cpp the GGUF files are Unsloth's or bartowski's conversions, not Cohere's",
          "It's text only. Don't send images"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 85,
          "reach": 85,
          "risk": 60,
          "upkeep": 36
        },
        "provenanceScore": 75,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "pip install \"vllm\u003e=0.33.0\" \"cohere_melody\u003e=0.9.0\"\nvllm serve CohereLabs/North-Mini-Code-1.0 -tp 2 --max-model-len 320000 --tool-call-parser cohere_command4 --reasoning-parser cohere_command4 --enable-auto-tool-choice",
        "http": "# vLLM serves an OpenAI-compatible API at http://localhost:8000/v1. Use temperature 1.0 and top_p 0.95,\n# and send the model's earlier thinking back with each turn"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/north-mini-code-1-0"
      },
      "sameCompany": [
        "cohere-chat",
        "cohere-embed",
        "cohere-transcribe-03-2026",
        "cohere-north"
      ],
      "weights": {
        "trainedBy": "Cohere and Cohere Labs",
        "params": "30B",
        "active": "3B",
        "context": 256000,
        "goodFor": "A local coding agent: repository work, terminal tasks and long tool-calling sessions, on one 24 GB GPU at 4 bits.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "North-Mini-Code-1.0-w4a16",
            "url": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0-w4a16",
            "who": "publisher",
            "precision": "w4a16",
            "sizeGB": 19.38,
            "memoryGB": 24,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "Cohere's 4-bit weight build"
          },
          {
            "name": "unsloth North-Mini-Code-1.0-GGUF (UD-Q4_K_M)",
            "url": "https://huggingface.co/unsloth/North-Mini-Code-1.0-GGUF",
            "who": "unsloth",
            "precision": "q4_k_m",
            "sizeGB": 19.2,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From Unsloth, a third party's conversion and not Cohere's. 19.20 GB of UD-Q4_K_M weights, from the Hub's model information on 10 November 2026. Reach doesn't count it"
          },
          {
            "name": "North-Mini-Code-1.0-fp8",
            "url": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0-fp8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 32.06,
            "memoryGB": 48,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ],
            "note": "Cohere's FP8 build"
          },
          {
            "name": "unsloth North-Mini-Code-1.0-GGUF (Q8_0)",
            "url": "https://huggingface.co/unsloth/North-Mini-Code-1.0-GGUF",
            "who": "unsloth",
            "precision": "q8_0",
            "sizeGB": 32.44,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From Unsloth, a third party's conversion and not Cohere's. 32.44 GB of Q8_0 weights, from the Hub's model information on 10 November 2026. Reach doesn't count it"
          },
          {
            "name": "North-Mini-Code-1.0 (BF16 safetensors)",
            "url": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 60.97,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ],
            "note": "49 files"
          }
        ],
        "run": "vllm serve CohereLabs/North-Mini-Code-1.0-w4a16 --tool-call-parser cohere_command4 --reasoning-parser cohere_command4 --enable-auto-tool-choice",
        "runBuild": "North-Mini-Code-1.0-w4a16",
        "checked": "2026-11-10",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "Cohere (Cohere Labs)",
        "family": "North Mini Code",
        "url": "https://www.anchorterminal.com/open-weights/#cohere",
        "olderCount": 29
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Cohere Inc.",
        "domain": "cohere.com",
        "domainRegistered": "2000-03-07",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "The card says Developed by Cohere and Cohere Labs. Legal entity, domain and security facts are carried from our other Cohere listings.",
          "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored.",
          "The changelog is the commit history, 8 commits from 5 to 15 June 2026."
        ],
        "score": 75
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/north-mini-code-1-0.json"
    },
    "answer": "North Mini Code 1.0 scores 61 (C) on agent readiness against Qwen3-VL-4B-Instruct's 52.6 (D), and leads in 3 of 4 scored categories. Qwen3-VL-4B-Instruct leads on reach.",
    "b": {
      "slug": "qwen3-vl-4b-instruct",
      "name": "Qwen3-VL-4B-Instruct",
      "vendor": "Alibaba (Qwen team)",
      "vendorUrl": "https://qwen.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Qwen3-VL-4B-Instruct is Alibaba's open-weight dense vision-language model of about 4B parameters, taking text, images and video with a 262,144-token context, published on Hugging Face in BF16, FP8 and GGUF under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct",
      "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-vl-4b-instruct.json",
      "dns": "qwen3-vl-4b-instruct.grade.anchorterminal.com",
      "repo": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "Qwen/Qwen3-VL-4B-Instruct"
        },
        {
          "registry": "huggingface",
          "name": "Qwen/Qwen3-VL-4B-Instruct-FP8"
        },
        {
          "registry": "huggingface",
          "name": "Qwen/Qwen3-VL-4B-Instruct-GGUF"
        },
        {
          "registry": "ollama",
          "name": "qwen3-vl:4b-instruct"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the three repositories is gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 8.89 GB for the BF16 files, 6.04 GB for Alibaba's FP8 build and 2.5 GB plus a 0.45 GB vision file for its 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/Qwen/Qwen3-VL-4B-Instruct",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "multimodal",
        "vision",
        "video",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "fp8",
        "gguf",
        "self-hosted",
        "edge"
      ],
      "lastRelease": "2025-11-01",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 52.6,
        "grade": "D",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 102,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 25,
          "interface": 60,
          "reach": 95,
          "risk": 55,
          "upkeep": 31
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "Qwen3-VL-4B-Instruct reads screenshots, documents, charts and video, and Alibaba's Q4_K_M GGUF needs about 3.5 GB. Transformers, vLLM, SGLang, llama.cpp and Ollama run it under plain Apache 2.0. It has no thinking mode, documents tool calls only in the template, and Qwen hasn't answered repository questions or published safety work, so add guardrails before it drives a GUI.",
        "bestFor": "An agent with 4 GB or more to spare that needs a local model to read images, documents and video",
        "strengths": [
          "About 3.5 GB of memory for Alibaba's own Q4_K_M GGUF with its vision file, with FP8, Q8_0 and F16 builds as well",
          "Runs in Transformers, vLLM, SGLang, llama.cpp and Ollama in released versions",
          "Apache 2.0 with no use policy, downloadable with no account",
          "262,144 tokens of native context over text, images and video",
          "Safetensors and GGUF only, with a chat template in the repository"
        ],
        "weaknesses": [
          "No safety evaluation, agent-use guidance or security contact",
          "Tool calling isn't documented beyond the template, and Alibaba names no parser",
          "No thinking mode in this edition and no documented JSON output",
          "No reply from Qwen to any of the 14 Hugging Face discussions that aren't pull requests, counted on 4 November 2026",
          "The card leaves out limitations, languages, knowledge cut-off and memory needs",
          "Only Featherless was found serving it for agents without the hardware"
        ],
        "agentNotes": [
          "Start it with `llama-server -hf Qwen/Qwen3-VL-4B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:4b-instruct`",
          "In Ollama the plain `qwen3-vl:4b` tag is the Thinking edition, so name `4b-instruct`",
          "With the GGUF, load the mmproj vision file too, or images won't be read",
          "Parse tool calls as JSON inside `\u003ctool_call\u003e` tags, or use SGLang's `--tool-call-parser qwen`",
          "Use the card's sampling, `temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5` for vision and `temperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0` for text",
          "Validate JSON output yourself, since structured output isn't documented",
          "vLLM needs 0.11.0 or later and Transformers 4.57.0 or later"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "D",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 52.6
          }
        ],
        "editorialScores": {
          "capability": 25,
          "interface": 60,
          "reach": 95,
          "risk": 55,
          "upkeep": 31
        },
        "provenanceScore": 54,
        "checked": {
          "categories": {
            "capability": "2026-11-04",
            "interface": "2026-11-04",
            "reach": "2026-11-04",
            "risk": "2026-11-04",
            "upkeep": "2026-11-04"
          },
          "last": "2026-11-04",
          "oldest": "2026-11-04",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run qwen3-vl:4b-instruct\n# or with vLLM 0.11.0 or later, per Qwen's README:\nvllm serve Qwen/Qwen3-VL-4B-Instruct-FP8\n# or with SGLang, using the tool parser its cookbook names for Qwen3-VL:\npython -m sglang.launch_server --model-path Qwen/Qwen3-VL-4B-Instruct --tool-call-parser qwen",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"qwen3-vl:4b-instruct\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/qwen3-vl-4b-instruct"
      },
      "sameCompany": [
        "qwen3-embedding-0-6b",
        "qwen3-embedding-4b",
        "qwen3-embedding-8b",
        "qwen3-vl-embedding-2b",
        "qwen3-vl-embedding-8b",
        "qwen3-asr-0-6b",
        "qwen3-asr-1-7b",
        "qwen3-tts-1-7b-customvoice",
        "qwen-agentworld-35b-a3b",
        "qwen3-30b-a3b-instruct-2507",
        "qwen3-4b-instruct-2507",
        "qwen3-coder-30b-a3b-instruct",
        "qwen3-coder-next",
        "qwen3-omni-30b-a3b-instruct",
        "qwen3-vl-2b-instruct",
        "qwen3-vl-30b-a3b-instruct",
        "qwen3-vl-32b-instruct",
        "qwen3-vl-8b-instruct",
        "qwen3-5-0-8b",
        "qwen3-5-122b-a10b",
        "qwen3-5-2b",
        "qwen3-5-397b-a17b",
        "qwen3-5-4b",
        "qwen3-5-9b",
        "qwen3-6-27b",
        "qwen3-6-35b-a3b",
        "qwen3-8-27b",
        "qwen3-8-flash-next"
      ],
      "weights": {
        "trainedBy": "Alibaba (Qwen team)",
        "params": "4.44B",
        "context": 262144,
        "goodFor": "Small vision-language model for images, documents and video with text out, fits in 8 GB at 4-bit, no thinking mode in this Instruct edition.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Qwen3-VL-4B-Instruct-FP8",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 6.04,
            "memoryGB": 8,
            "format": "safetensors"
          },
          {
            "name": "Qwen3VL-4B-Instruct-Q8_0",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct-GGUF/blob/main/Qwen3VL-4B-Instruct-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 4.73,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Size includes mmproj-Qwen3VL-4B-Instruct-Q8_0.gguf (0.45 GB), the vision file"
          },
          {
            "name": "Qwen3VL-4B-Instruct-Q4_K_M",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct-GGUF/blob/main/Qwen3VL-4B-Instruct-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 2.95,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "2.5 GB language file plus mmproj-Qwen3VL-4B-Instruct-Q8_0.gguf (0.45 GB) for image input"
          },
          {
            "name": "ollama qwen3-vl:4b-instruct",
            "url": "https://ollama.com/library/qwen3-vl:4b-instruct",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 3.3,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M with its vision projector (digest 84f7ed8042fa, updated the day before our check). qwen3-vl:4b, the library default, is the Thinking edition. Size as Ollama's tags page shows it"
          },
          {
            "name": "Qwen3-VL-4B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 8.89,
            "memoryGB": 12,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          },
          {
            "name": "Qwen3VL-4B-Instruct-F16",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct-GGUF/blob/main/Qwen3VL-4B-Instruct-F16.gguf",
            "who": "publisher",
            "precision": "f16",
            "sizeGB": 8.89,
            "memoryGB": 12,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Size includes mmproj-Qwen3VL-4B-Instruct-F16.gguf (0.84 GB), the vision file"
          }
        ],
        "run": "llama-server -hf Qwen/Qwen3-VL-4B-Instruct-GGUF:Q4_K_M --jinja -c 32768",
        "runBuild": "Qwen3VL-4B-Instruct-Q4_K_M",
        "checked": "2026-11-04",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "Alibaba (Qwen)",
        "family": "Qwen3",
        "url": "https://www.anchorterminal.com/open-weights/#qwen",
        "others": [
          {
            "name": "Qwen3-0.6B",
            "url": "https://huggingface.co/Qwen/Qwen3-0.6B",
            "params": "0.6B"
          },
          {
            "name": "Qwen3-1.7B",
            "url": "https://huggingface.co/Qwen/Qwen3-1.7B",
            "params": "1.7B"
          },
          {
            "name": "Qwen3-14B",
            "url": "https://huggingface.co/Qwen/Qwen3-14B",
            "params": "14B"
          },
          {
            "name": "Qwen3-235B-A22B",
            "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B",
            "params": "235B-A22B"
          },
          {
            "name": "Qwen3-30B-A3B",
            "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B",
            "params": "30B-A3B"
          },
          {
            "name": "Qwen3-32B",
            "url": "https://huggingface.co/Qwen/Qwen3-32B",
            "params": "32B"
          },
          {
            "name": "Qwen3-4B",
            "url": "https://huggingface.co/Qwen/Qwen3-4B",
            "params": "4B"
          },
          {
            "name": "Qwen3-8B",
            "url": "https://huggingface.co/Qwen/Qwen3-8B",
            "params": "8B"
          },
          {
            "name": "Qwen3-0.6B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-0.6B-Base",
            "params": "0.6B"
          },
          {
            "name": "Qwen3-1.7B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-1.7B-Base",
            "params": "1.7B"
          },
          {
            "name": "Qwen3-14B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-14B-Base",
            "params": "14B"
          },
          {
            "name": "Qwen3-30B-A3B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Base",
            "params": "30B-A3B"
          },
          {
            "name": "Qwen3-4B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-4B-Base",
            "params": "4B"
          },
          {
            "name": "Qwen3-8B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-8B-Base",
            "params": "8B"
          },
          {
            "name": "Qwen3-Reranker-0.6B",
            "url": "https://huggingface.co/Qwen/Qwen3-Reranker-0.6B",
            "params": "0.6B"
          },
          {
            "name": "Qwen3-Reranker-8B",
            "url": "https://huggingface.co/Qwen/Qwen3-Reranker-8B",
            "params": "8B"
          },
          {
            "name": "Qwen3-Embedding-0.6B",
            "url": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B",
            "params": "0.6B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-0-6b"
          },
          {
            "name": "Qwen3-Embedding-4B",
            "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B",
            "params": "4B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-4b"
          },
          {
            "name": "Qwen3-Embedding-8B",
            "url": "https://huggingface.co/Qwen/Qwen3-Embedding-8B",
            "params": "8B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-8b"
          },
          {
            "name": "Qwen3-Reranker-4B",
            "url": "https://huggingface.co/Qwen/Qwen3-Reranker-4B",
            "params": "4B"
          },
          {
            "name": "Qwen3-235B-A22B-Instruct-2507",
            "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507",
            "params": "235B-A22B"
          },
          {
            "name": "Qwen3-Coder-480B-A35B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct",
            "params": "480B-A35B"
          },
          {
            "name": "Qwen3-235B-A22B-Thinking-2507",
            "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507",
            "params": "235B-A22B"
          },
          {
            "name": "Qwen3-30B-A3B-Instruct-2507",
            "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507",
            "params": "30B-A3B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-30b-a3b-instruct-2507"
          },
          {
            "name": "Qwen3-30B-A3B-Thinking-2507",
            "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507",
            "params": "30B-A3B"
          },
          {
            "name": "Qwen3-Coder-30B-A3B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct",
            "params": "30B-A3B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct"
          },
          {
            "name": "Qwen3-4B-Instruct-2507",
            "url": "https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507",
            "params": "4B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507"
          },
          {
            "name": "Qwen3-4B-Thinking-2507",
            "url": "https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507",
            "params": "4B"
          },
          {
            "name": "Qwen3-Next-80B-A3B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct",
            "params": "80B-A3B"
          },
          {
            "name": "Qwen3-Next-80B-A3B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking",
            "params": "80B-A3B"
          },
          {
            "name": "Qwen3-Omni-30B-A3B-Captioner",
            "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Captioner",
            "params": "30B-A3B"
          },
          {
            "name": "Qwen3-Omni-30B-A3B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Thinking",
            "params": "30B-A3B"
          },
          {
            "name": "Qwen3-Omni-30B-A3B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct",
            "params": "30B-A3B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-omni-30b-a3b-instruct"
          },
          {
            "name": "Qwen3-VL-235B-A22B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct",
            "params": "235B-A22B"
          },
          {
            "name": "Qwen3-VL-235B-A22B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Thinking",
            "params": "235B-A22B"
          },
          {
            "name": "Qwen3Guard-Gen-0.6B",
            "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-0.6B",
            "params": "0.6B"
          },
          {
            "name": "Qwen3Guard-Gen-4B",
            "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-4B",
            "params": "4B"
          },
          {
            "name": "Qwen3Guard-Gen-8B",
            "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-8B",
            "params": "8B"
          },
          {
            "name": "Qwen3Guard-Stream-0.6B",
            "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-0.6B",
            "params": "0.6B"
          },
          {
            "name": "Qwen3Guard-Stream-4B",
            "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-4B",
            "params": "4B"
          },
          {
            "name": "Qwen3Guard-Stream-8B",
            "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-8B",
            "params": "8B"
          },
          {
            "name": "Qwen3-4B-SafeRL",
            "url": "https://huggingface.co/Qwen/Qwen3-4B-SafeRL",
            "params": "4B"
          },
          {
            "name": "Qwen3-VL-30B-A3B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct",
            "params": "30B-A3B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct"
          },
          {
            "name": "Qwen3-VL-30B-A3B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Thinking",
            "params": "30B-A3B"
          },
          {
            "name": "Qwen3-VL-4B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Thinking",
            "params": "4B"
          },
          {
            "name": "Qwen3-VL-8B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct",
            "params": "8B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct"
          },
          {
            "name": "Qwen3-VL-8B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking",
            "params": "8B"
          },
          {
            "name": "Qwen3-VL-2B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct",
            "params": "2B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-vl-2b-instruct"
          },
          {
            "name": "Qwen3-VL-2B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Thinking",
            "params": "2B"
          },
          {
            "name": "Qwen3-VL-32B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct",
            "params": "32B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct"
          },
          {
            "name": "Qwen3-VL-32B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Thinking",
            "params": "32B"
          },
          {
            "name": "Qwen3-VL-Embedding-2B",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B",
            "params": "2B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-2b"
          },
          {
            "name": "Qwen3-VL-Embedding-8B",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B",
            "params": "8B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-8b"
          },
          {
            "name": "Qwen3-VL-Reranker-2B",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-2B",
            "params": "2B"
          },
          {
            "name": "Qwen3-VL-Reranker-8B",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-8B",
            "params": "8B"
          },
          {
            "name": "Qwen3-TTS-12Hz-0.6B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-Base",
            "params": "0.6B"
          },
          {
            "name": "Qwen3-TTS-12Hz-0.6B-CustomVoice",
            "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice",
            "params": "0.6B"
          },
          {
            "name": "Qwen3-TTS-12Hz-1.7B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-Base",
            "params": "1.7B"
          },
          {
            "name": "Qwen3-TTS-12Hz-1.7B-CustomVoice",
            "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice",
            "params": "1.7B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-tts-1-7b-customvoice"
          },
          {
            "name": "Qwen3-TTS-12Hz-1.7B-VoiceDesign",
            "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign",
            "params": "1.7B"
          },
          {
            "name": "Qwen3-TTS-Tokenizer-12Hz",
            "url": "https://huggingface.co/Qwen/Qwen3-TTS-Tokenizer-12Hz",
            "params": "not in name"
          },
          {
            "name": "Qwen3-ASR-0.6B",
            "url": "https://huggingface.co/Qwen/Qwen3-ASR-0.6B",
            "params": "0.6B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-asr-0-6b"
          },
          {
            "name": "Qwen3-ASR-1.7B",
            "url": "https://huggingface.co/Qwen/Qwen3-ASR-1.7B",
            "params": "1.7B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-asr-1-7b"
          },
          {
            "name": "Qwen3-ForcedAligner-0.6B",
            "url": "https://huggingface.co/Qwen/Qwen3-ForcedAligner-0.6B",
            "params": "0.6B"
          },
          {
            "name": "Qwen3-Coder-Next",
            "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next",
            "params": "not in name; Hub safetensors total 79.7B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-coder-next"
          },
          {
            "name": "Qwen3-Coder-Next-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next-Base",
            "params": "not in name; Hub safetensors total 79.7B"
          },
          {
            "name": "WebWorld-14B",
            "url": "https://huggingface.co/Qwen/WebWorld-14B",
            "params": "14B"
          },
          {
            "name": "WebWorld-32B",
            "url": "https://huggingface.co/Qwen/WebWorld-32B",
            "params": "32B"
          },
          {
            "name": "WebWorld-8B",
            "url": "https://huggingface.co/Qwen/WebWorld-8B",
            "params": "8B"
          },
          {
            "name": "Qwen3.5-397B-A17B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B",
            "params": "397B-A17B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b"
          },
          {
            "name": "Qwen3.5-122B-A10B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B",
            "params": "122B-A10B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b"
          },
          {
            "name": "Qwen3.5-27B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-27B",
            "params": "27B"
          },
          {
            "name": "Qwen3.5-35B-A3B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B",
            "params": "35B-A3B"
          },
          {
            "name": "Qwen3.5-35B-A3B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B-Base",
            "params": "35B-A3B"
          },
          {
            "name": "Qwen3.5-9B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3.5-9B-Base",
            "params": "9B"
          },
          {
            "name": "Qwen3.5-4B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-4B",
            "params": "4B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-5-4b"
          },
          {
            "name": "Qwen3.5-4B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3.5-4B-Base",
            "params": "4B"
          },
          {
            "name": "Qwen3.5-9B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-9B",
            "params": "9B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-5-9b"
          },
          {
            "name": "Qwen3.5-0.8B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B",
            "params": "0.8B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-5-0-8b"
          },
          {
            "name": "Qwen3.5-0.8B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B-Base",
            "params": "0.8B"
          },
          {
            "name": "Qwen3.5-2B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-2B",
            "params": "2B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-5-2b"
          },
          {
            "name": "Qwen3.5-2B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3.5-2B-Base",
            "params": "2B"
          },
          {
            "name": "Qwen3.6-35B-A3B",
            "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B",
            "params": "35B-A3B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b"
          },
          {
            "name": "Qwen3.6-27B",
            "url": "https://huggingface.co/Qwen/Qwen3.6-27B",
            "params": "27B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-6-27b"
          },
          {
            "name": "Qwen-AgentWorld-35B-A3B",
            "url": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B",
            "params": "35B-A3B"
          },
          {
            "name": "Qwen3.8-27B",
            "url": "https://huggingface.co/Qwen/Qwen3.8-27B",
            "params": "27B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-8-27b"
          },
          {
            "name": "Qwen3.8-2.4T-A95B",
            "url": "https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B",
            "params": "2.4T-A95B"
          },
          {
            "name": "Qwen3.8-Flash-Next",
            "url": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next",
            "params": "125B total, 6B active, plus a 51B n-gram embedding table (publisher card); Hub safetensors total 180.0B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-8-flash-next"
          },
          {
            "name": "Qwen-Drive-1.0-4B",
            "url": "https://huggingface.co/Qwen/Qwen-Drive-1.0-4B",
            "params": "4B"
          }
        ],
        "olderCount": 109,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Alibaba Cloud (per the first batch's RDAP check of qwen.ai; this model's files name no copyright holder)",
        "domain": "qwen.ai",
        "domainRegistered": "2023-08-27",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The weights repositories have no LICENSE file and the GitHub repository's Apache 2.0 LICENSE leaves the copyright line as the template. The legal entity and domain date come from the first batch's check of qwen.ai on 20 October 2026; RDAP wasn't queried again.",
          "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "No changelog. The GitHub README has a dated news list of releases, and the Hugging Face commit messages are README updates and uploads that don't say what changed.",
          "qwen.ai/.well-known/security.txt redirects to the home page, so there is no security.txt."
        ],
        "score": 54
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct.json",
      "live": {
        "slug": "qwen3-vl-4b-instruct",
        "securityTxt": {
          "url": "https://qwen.ai/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:09:18.413964975Z"
        },
        "domain": {
          "domain": "qwen.ai",
          "registered": "2023-08-27",
          "source": "https://rdap.identitydigital.services/rdap/domain/qwen.ai",
          "checkedAt": "2026-11-08T15:07:12.468678256Z"
        },
        "updatedAt": "2026-11-10T16:09:18.413964975Z"
      }
    },
    "edges": [
      {
        "by": 25,
        "category": "interface",
        "leads": "north-mini-code-1-0",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, chat_template.jinja in the repository (12,397 characters). Tool-calling format 25, the card documents how tools are declared (JSON schema through the chat template), how the call and its thinking go back into the history and how results are appended, and names the cohere_command4 tool-call parser for vLLM, which released vLLM has. Trained tool calling 20, the card says the model was specifically trained with tool-use capabilities for agentic coding, with reinforcement learning on verifiable tasks, and the template renders every call in a turn with thinking between them. Structured output 0, JSON schema is for describing tools. Reasoning control 10, the template has a reasoning_effort setting, and the card names the cohere_command4 reasoning parser, which returns thinking apart from the answer. Context 10, 256K tokens per the card, with 64K of output, and 500,000 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.",
          "b": "A Jinja chat template in the repository (20). The tool-call format lives only in the template, Hermes-style JSON in tool_call tags, and Alibaba names no parser (10). Trained for function calling with several calls allowed in a turn, per the family's technical report (20). No documented structured output (0). No thinking mode in this edition, so reasoning control scores 0 by the rule. 262,144 tokens of native context (10). 20+10+20+0+0+10 = 60."
        },
        "scores": {
          "a": 85,
          "b": 60
        }
      },
      {
        "by": 10,
        "category": "reach",
        "leads": "qwen3-vl-4b-instruct",
        "name": "Reach",
        "reasons": {
          "a": "Memory 25, the smallest build from Cohere is the w4a16 repository, 19.38 GB, and 19.38 GB × 1.2 = 23.3 GB, within 24 GB. Unsloth's GGUF isn't read for this line. Runtimes 20, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the cohere2_moe model, and llama.cpp b12242 has the cohere2moe architecture. Builds 15, Quantised builds from Cohere at two precisions, FP8 and 4-bit weights (w4a16), each in its own repository. Terms 15, Apache 2.0 in the metadata and on the card, an OSI licence with nothing added. Download 10, the Hub API reports gated false for the model and its builds on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 25 + 20 + 15 + 15 + 10 + 0 = 85.",
          "b": "Alibaba's own Q4_K_M GGUF with its vision file is 2.95 GB, so with a fifth added it needs about 3.5 GB and fits well within 16 GB (30). Transformers 4.57.0, vLLM, SGLang and llama.cpp run it in released versions, and Ollama through its pinned llama.cpp build, which reaches the 25 cap (25). Alibaba publishes FP8 and GGUF builds at F16, Q8_0 and Q4_K_M (15). Apache 2.0 with nothing added (15). No gate and no account (10). Only Featherless is live in the Hub's provider mapping, so it isn't hosted too (0). 30+25+15+15+10+0 = 95."
        },
        "scores": {
          "a": 85,
          "b": 95
        }
      },
      {
        "by": 5,
        "category": "risk",
        "leads": "north-mini-code-1-0",
        "name": "Risk",
        "reasons": {
          "a": "File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in Cohere's verified CohereLabs organisation, which cohere.com links to per our other Cohere listings (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 5, Cohere's trust centre holds a responsible disclosure policy with a bug bounty, as recorded on our Cohere listings (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.",
          "b": "Safetensors and GGUF only and no code in the repositories, so the FP8 card's trust_remote_code flag runs nothing (25). Files in the Qwen organisation with a checksum on every file (15). No safety evaluation at all (0). No guidance for agent use, which matters for a model sold as a GUI agent (0). Apache 2.0 carries none of the licence risks (15). No security contact, disclosure policy or public advisories found for Qwen (0), and Alibaba's group-wide security centre wasn't checked. 25+15+0+0+15+0 = 55."
        },
        "scores": {
          "a": 60,
          "b": 55
        }
      },
      {
        "by": 5,
        "category": "upkeep",
        "leads": "north-mini-code-1-0",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 11, Intended use, the architecture, the parameters and the context are on the card. Training data 0, the card gives the training method and nothing about the data. Last change 10, the main repository last changed on 15 June 2026 (outdated chat templates removed), 148 days before this check, and the draft model's repository on 29 July, 104 days. Responsiveness 0, none of the 11 most recent discussions that aren't pull requests (7 June to 6 November 2026) has a reply from a CohereLabs member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, with each build in its own repository. File health 10, Config, template and card agree on the architecture, and the June commit that removed broken templates says so. 11 + 0 + 10 + 0 + 5 + 10 = 36.",
          "b": "The card misses limitations, languages and a cut-off (11). Training data is described only in the technical report, with no cut-off (5). The last change to code that runs it was the qwen-vl-utils fix of 4 January 2026, and the weights haven't changed since November 2025 (5). Qwen answered none of the 14 Hugging Face discussions that aren't pull requests, counted on 4 November 2026 (0), and GitHub issues weren't counted. Earlier versions kept in their own repositories without notes (5). Two reports wait without a reply (5). 11+5+5+0+5+5 = 31."
        },
        "scores": {
          "a": 36,
          "b": 31
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Cohere",
        "b": "Alibaba (Qwen team)",
        "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": "Apache-2.0",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-07-29",
        "b": "2025-11-01",
        "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": "none",
        "b": "2.8/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "North Mini Code 1.0 scores 61 (C) on agent readiness against Qwen3-VL-4B-Instruct's 52.6 (D), and leads in 3 of 4 scored categories. Qwen3-VL-4B-Instruct leads on reach.",
        "question": "Which is better for AI agents, North Mini Code 1.0 or Qwen3-VL-4B-Instruct?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Risk, 60 against 55",
          "Agent interface, 85 against 60",
          "Documentation and upkeep, 36 against 31"
        ],
        "also": null,
        "goodFor": "Developers who want a coding agent on their own GPU with a permissive licence",
        "slug": "north-mini-code-1-0",
        "watchFor": "No limitations, safety results, languages or data description on the card"
      },
      {
        "aheadOn": [
          "Reach, 95 against 85"
        ],
        "also": null,
        "goodFor": "An agent with 4 GB or more to spare that needs a local model to read images, documents and video",
        "slug": "qwen3-vl-4b-instruct",
        "watchFor": "No safety evaluation, agent-use guidance or security contact"
      }
    ],
    "history": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
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      },
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-north-mini-code-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-north-mini-code-1-0"
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-vl-4b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-north-mini-code-1-0.json",
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-vl-4b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-north-mini-code-1-0.json",
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-north-mini-code-1-0.json",
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        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-north-mini-code-1-0.json",
        "title": "Hy-Memory-30B-A3B vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-north-mini-code-1-0"
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        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-4b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-4b-instruct"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-north-mini-code-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-north-mini-code-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-4b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-north-mini-code-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-north-mini-code-1-0"
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        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-vl-4b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-vl-4b-instruct"
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-north-mini-code-1-0.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-vl-4b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-north-mini-code-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-north-mini-code-1-0"
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        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-vl-4b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-north-mini-code-1-0.json",
        "title": "Kimi K3.1 Pro vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-north-mini-code-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-4b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-4b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-north-mini-code-1-0.json",
        "title": "Kimi K3 vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-north-mini-code-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-4b-instruct.json",
        "title": "Kimi K3 vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-4b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-north-mini-code-1-0.json",
        "title": "Kolibri-1 vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-north-mini-code-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-4b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-4b-instruct"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-north-mini-code-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-north-mini-code-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-vl-4b-instruct.json",
        "title": "Kronos vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-vl-4b-instruct"
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-north-mini-code-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-north-mini-code-1-0"
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-4b-instruct.json",
        "title": "Laguna M.1 vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-4b-instruct"
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-north-mini-code-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-north-mini-code-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-4b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-4b-instruct"
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        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-north-mini-code-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-north-mini-code-1-0"
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        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-4b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-4b-instruct"
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        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-north-mini-code-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-north-mini-code-1-0"
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        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-4b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-north-mini-code-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-north-mini-code-1-0"
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        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-north-mini-code-1-0.json",
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        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-vl-4b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-north-mini-code-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-north-mini-code-1-0"
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        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-vl-4b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-vl-4b-instruct"
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        "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-north-mini-code-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-north-mini-code-1-0"
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-north-mini-code-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-north-mini-code-1-0"
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-4b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-north-mini-code-1-0"
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        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-4b-instruct"
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-north-mini-code-1-0.json",
        "title": "Ling-3.1-flash-1019 vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-north-mini-code-1-0"
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        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-vl-4b-instruct"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-north-mini-code-1-0.json",
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-4b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-4b-instruct"
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        "title": "MiMo-V2.6 Distill Qwen 9B vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-north-mini-code-1-0"
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-north-mini-code-1-0"
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        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-north-mini-code-1-0.json",
        "title": "MiniCPM-o 4.5 vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-north-mini-code-1-0"
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-north-mini-code-1-0.json",
        "title": "MiniCPM-V 4.7 1B vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-north-mini-code-1-0"
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-4b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-4b-instruct"
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-north-mini-code-1-0.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-north-mini-code-1-0"
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-vl-4b-instruct.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-vl-4b-instruct"
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        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-north-mini-code-1-0.json",
        "title": "MiniCPM5-2B vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-north-mini-code-1-0"
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        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-4b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-4b-instruct"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-north-mini-code-1-0.json",
        "title": "MiniMax-M2.5 vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-north-mini-code-1-0"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-4b-instruct.json",
        "title": "MiniMax-M2.5 vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-4b-instruct"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-north-mini-code-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-north-mini-code-1-0"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-4b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-4b-instruct"
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        "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-north-mini-code-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-4b-instruct.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-4b-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-1-5-9b-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/otel-2-0-llm-31b-it-vs-qwen3-vl-4b-instruct.json",
        "title": "OTel-2.0-LLM-31B-IT vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-4b-instruct.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-vl-4b-instruct.json",
        "title": "Qwen3-30B-A3B-Instruct-2507 vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-vl-4b-instruct.json",
        "title": "Qwen3-4B-Instruct-2507 vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-vl-4b-instruct.json",
        "title": "Qwen3.5-0.8B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-4b-instruct.json",
        "title": "Qwen3.5-122B-A10B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-4b-instruct.json",
        "title": "Qwen3.5-2B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-4b-instruct.json",
        "title": "Qwen3.5-397B-A17B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-vl-4b-instruct.json",
        "title": "Qwen3.5-4B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-4b-instruct.json",
        "title": "Qwen3.5-9B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-4b-instruct.json",
        "title": "Qwen3.6-27B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-4b-instruct.json",
        "title": "Qwen3.6-35B-A3B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-4b-instruct.json",
        "title": "Qwen3.8-27B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-4b-instruct.json",
        "title": "Qwen3.8-Flash-Next vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-4b-instruct.json",
        "title": "Qwen3-Coder-30B-A3B-Instruct vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-4b-instruct.json",
        "title": "Qwen3-Coder-Next vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwen3-vl-4b-instruct.json",
        "title": "Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwen3-vl-4b-instruct.json",
        "title": "Qwen3-VL-2B-Instruct vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-qwen3-vl-4b-instruct.json",
        "title": "Qwen3-VL-30B-A3B-Instruct vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-qwen3-vl-4b-instruct.json",
        "title": "Qwen3-VL-32B-Instruct vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-qwen3-vl-8b-instruct.json",
        "title": "Qwen3-VL-4B-Instruct vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen3-VL-4B-Instruct vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-recursive-flint.json",
        "title": "Qwen3-VL-4B-Instruct vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-ripple-65b-preview.json",
        "title": "Qwen3-VL-4B-Instruct vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-sensenova-loop-9b-preview.json",
        "title": "Qwen3-VL-4B-Instruct vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-smollm3-3b.json",
        "title": "Qwen3-VL-4B-Instruct vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-spark-x2-6-4b.json",
        "title": "Qwen3-VL-4B-Instruct vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-step-3-7-flash.json",
        "title": "Qwen3-VL-4B-Instruct vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3-VL-4B-Instruct vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwen3-VL-4B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Qwen3-VL-4B-Instruct vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-unlimited-ocr-2.json",
        "title": "Qwen3-VL-4B-Instruct vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-2b.json",
        "title": "Qwen3-VL-4B-Instruct vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-4b.json",
        "title": "Qwen3-VL-4B-Instruct vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-zaya1-8b.json",
        "title": "Qwen3-VL-4B-Instruct vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 10,
        "edge": "qwen3-vl-4b-instruct",
        "key": "reach",
        "name": "Reach",
        "north-mini-code-1-0": 85,
        "qwen3-vl-4b-instruct": 95,
        "weight": 25
      },
      {
        "by": 5,
        "edge": "north-mini-code-1-0",
        "key": "risk",
        "name": "Risk",
        "north-mini-code-1-0": 60,
        "qwen3-vl-4b-instruct": 55,
        "weight": 20
      },
      {
        "by": 25,
        "edge": "north-mini-code-1-0",
        "key": "interface",
        "name": "Agent interface",
        "north-mini-code-1-0": 85,
        "qwen3-vl-4b-instruct": 60,
        "weight": 10
      },
      {
        "by": 5,
        "edge": "north-mini-code-1-0",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "north-mini-code-1-0": 36,
        "qwen3-vl-4b-instruct": 31,
        "weight": 10
      }
    ],
    "summary": "North Mini Code 1.0 scores 61 (C) on agent readiness against Qwen3-VL-4B-Instruct's 52.6 (D), and leads in 3 of 4 scored categories. Qwen3-VL-4B-Instruct leads on reach. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "north-mini-code-1-0": "A coding agent model that fits one 24 GB GPU at 4 bits, trained for tool use, with a 256K context, Apache 2.0 and four released runtimes. It's a research release and reads like one: nothing on limitations, safety or data, the card's run instructions are out of date, and Cohere hasn't answered a question on the repository.",
      "qwen3-vl-4b-instruct": "Qwen3-VL-4B-Instruct reads screenshots, documents, charts and video, and Alibaba's Q4_K_M GGUF needs about 3.5 GB. Transformers, vLLM, SGLang, llama.cpp and Ollama run it under plain Apache 2.0. It has no thinking mode, documents tool calls only in the template, and Qwen hasn't answered repository questions or published safety work, so add guardrails before it drives a GUI."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-vl-4b-instruct",
    "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-vl-4b-instruct.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-vl-4b-instruct.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-vl-4b-instruct.min.md"
  },
  "markdown": "North Mini Code 1.0 scores 61 (C) on agent readiness against Qwen3-VL-4B-Instruct's 52.6 (D), and leads in 3 of 4 scored categories. Qwen3-VL-4B-Instruct leads on reach. Both do open-weight models you run yourself.\n\n- North Mini Code 1.0: grade C, 61/100, rank #80 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/north-mini-code-1-0.md · JSON https://www.anchorterminal.com/api/v1/tools/north-mini-code-1-0.json\n- Qwen3-VL-4B-Instruct: grade D, 52.6/100, rank #137 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-vl-4b-instruct.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### North Mini Code 1.0 (C)\n\nGood for: Developers who want a coding agent on their own GPU with a permissive licence\n\nAhead on:\n- Risk, 60 against 55\n- Agent interface, 85 against 60\n- Documentation and upkeep, 36 against 31\n\nWatch for: No limitations, safety results, languages or data description on the card\n\n### Qwen3-VL-4B-Instruct (D)\n\nGood for: An agent with 4 GB or more to spare that needs a local model to read images, documents and video\n\nAhead on:\n- Reach, 95 against 85\n\nWatch for: No safety evaluation, agent-use guidance or security contact\n\n\n## Why the edge\n\n### Agent interface: North Mini Code 1.0 +25\n\n- North Mini Code 1.0 (85): Chat template 20, chat_template.jinja in the repository (12,397 characters). Tool-calling format 25, the card documents how tools are declared (JSON schema through the chat template), how the call and its thinking go back into the history and how results are appended, and names the cohere_command4 tool-call parser for vLLM, which released vLLM has. Trained tool calling 20, the card says the model was specifically trained with tool-use capabilities for agentic coding, with reinforcement learning on verifiable tasks, and the template renders every call in a turn with thinking between them. Structured output 0, JSON schema is for describing tools. Reasoning control 10, the template has a reasoning_effort setting, and the card names the cohere_command4 reasoning parser, which returns thinking apart from the answer. Context 10, 256K tokens per the card, with 64K of output, and 500,000 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.\n- Qwen3-VL-4B-Instruct (60): A Jinja chat template in the repository (20). The tool-call format lives only in the template, Hermes-style JSON in tool_call tags, and Alibaba names no parser (10). Trained for function calling with several calls allowed in a turn, per the family's technical report (20). No documented structured output (0). No thinking mode in this edition, so reasoning control scores 0 by the rule. 262,144 tokens of native context (10). 20+10+20+0+0+10 = 60.\n\n### Reach: Qwen3-VL-4B-Instruct +10\n\n- North Mini Code 1.0 (85): Memory 25, the smallest build from Cohere is the w4a16 repository, 19.38 GB, and 19.38 GB × 1.2 = 23.3 GB, within 24 GB. Unsloth's GGUF isn't read for this line. Runtimes 20, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the cohere2_moe model, and llama.cpp b12242 has the cohere2moe architecture. Builds 15, Quantised builds from Cohere at two precisions, FP8 and 4-bit weights (w4a16), each in its own repository. Terms 15, Apache 2.0 in the metadata and on the card, an OSI licence with nothing added. Download 10, the Hub API reports gated false for the model and its builds on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 25 + 20 + 15 + 15 + 10 + 0 = 85.\n- Qwen3-VL-4B-Instruct (95): Alibaba's own Q4_K_M GGUF with its vision file is 2.95 GB, so with a fifth added it needs about 3.5 GB and fits well within 16 GB (30). Transformers 4.57.0, vLLM, SGLang and llama.cpp run it in released versions, and Ollama through its pinned llama.cpp build, which reaches the 25 cap (25). Alibaba publishes FP8 and GGUF builds at F16, Q8_0 and Q4_K_M (15). Apache 2.0 with nothing added (15). No gate and no account (10). Only Featherless is live in the Hub's provider mapping, so it isn't hosted too (0). 30+25+15+15+10+0 = 95.\n\n### Risk: North Mini Code 1.0 +5\n\n- North Mini Code 1.0 (60): File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in Cohere's verified CohereLabs organisation, which cohere.com links to per our other Cohere listings (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 5, Cohere's trust centre holds a responsible disclosure policy with a bug bounty, as recorded on our Cohere listings (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.\n- Qwen3-VL-4B-Instruct (55): Safetensors and GGUF only and no code in the repositories, so the FP8 card's trust_remote_code flag runs nothing (25). Files in the Qwen organisation with a checksum on every file (15). No safety evaluation at all (0). No guidance for agent use, which matters for a model sold as a GUI agent (0). Apache 2.0 carries none of the licence risks (15). No security contact, disclosure policy or public advisories found for Qwen (0), and Alibaba's group-wide security centre wasn't checked. 25+15+0+0+15+0 = 55.\n\n### Documentation and upkeep: North Mini Code 1.0 +5\n\n- North Mini Code 1.0 (36): Model card 11, Intended use, the architecture, the parameters and the context are on the card. Training data 0, the card gives the training method and nothing about the data. Last change 10, the main repository last changed on 15 June 2026 (outdated chat templates removed), 148 days before this check, and the draft model's repository on 29 July, 104 days. Responsiveness 0, none of the 11 most recent discussions that aren't pull requests (7 June to 6 November 2026) has a reply from a CohereLabs member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, with each build in its own repository. File health 10, Config, template and card agree on the architecture, and the June commit that removed broken templates says so. 11 + 0 + 10 + 0 + 5 + 10 = 36.\n- Qwen3-VL-4B-Instruct (31): The card misses limitations, languages and a cut-off (11). Training data is described only in the technical report, with no cut-off (5). The last change to code that runs it was the qwen-vl-utils fix of 4 January 2026, and the weights haven't changed since November 2025 (5). Qwen answered none of the 14 Hugging Face discussions that aren't pull requests, counted on 4 November 2026 (0), and GitHub issues weren't counted. Earlier versions kept in their own repositories without notes (5). Two reports wait without a reply (5). 11+5+5+0+5+5 = 31.\n\n## Score by category\n\n| Category | Weight | North Mini Code 1.0 | Qwen3-VL-4B-Instruct | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 95 | Qwen3-VL-4B-Instruct +10 |\n| Risk | 20% (20 this run) | 60 | 55 | North Mini Code 1.0 +5 |\n| Agent interface | 10% (10 this run) | 85 | 60 | North Mini Code 1.0 +25 |\n| Documentation and upkeep | 10% (10 this run) | 36 | 31 | North Mini Code 1.0 +5 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **52.6 · D** | |\n\n## Facts side by side\n\n| Fact | North Mini Code 1.0 | Qwen3-VL-4B-Instruct |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Cohere | Alibaba (Qwen team) |\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 | Apache-2.0 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-29 | 2025-11-01 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 2.8/5 (4) |\n\n## Verdicts\n\n**North Mini Code 1.0.** A coding agent model that fits one 24 GB GPU at 4 bits, trained for tool use, with a 256K context, Apache 2.0 and four released runtimes. It's a research release and reads like one: nothing on limitations, safety or data, the card's run instructions are out of date, and Cohere hasn't answered a question on the repository.\n\n**Qwen3-VL-4B-Instruct.** Qwen3-VL-4B-Instruct reads screenshots, documents, charts and video, and Alibaba's Q4_K_M GGUF needs about 3.5 GB. Transformers, vLLM, SGLang, llama.cpp and Ollama run it under plain Apache 2.0. It has no thinking mode, documents tool calls only in the template, and Qwen hasn't answered repository questions or published safety work, so add guardrails before it drives a GUI.\n\n## Before you call either\n\n### North Mini Code 1.0\n\n1. Serve it with vLLM 0.33.0 or later and `--tool-call-parser cohere_command4 --reasoning-parser cohere_command4 --enable-auto-tool-choice`, with `cohere_melody` installed\n2. Pass the model's thinking back with its tool calls on every turn. The card says results drop without it\n3. Describe tools in JSON schema and append tool results as a dictionary with the tool role\n4. Use temperature 1.0 and top_p 0.95\n5. For llama.cpp the GGUF files are Unsloth's or bartowski's conversions, not Cohere's\n6. It's text only. Don't send images\n\n### Qwen3-VL-4B-Instruct\n\n1. Start it with `llama-server -hf Qwen/Qwen3-VL-4B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:4b-instruct`\n2. In Ollama the plain `qwen3-vl:4b` tag is the Thinking edition, so name `4b-instruct`\n3. With the GGUF, load the mmproj vision file too, or images won't be read\n4. Parse tool calls as JSON inside `\u003ctool_call\u003e` tags, or use SGLang's `--tool-call-parser qwen`\n5. Use the card's sampling, `temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5` for vision and `temperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0` for text\n6. Validate JSON output yourself, since structured output isn't documented\n7. vLLM needs 0.11.0 or later and Transformers 4.57.0 or later\n\n## Questions\n\n### Which is better for AI agents, North Mini Code 1.0 or Qwen3-VL-4B-Instruct?\n\nNorth Mini Code 1.0 scores 61 (C) on agent readiness against Qwen3-VL-4B-Instruct's 52.6 (D), and leads in 3 of 4 scored categories. Qwen3-VL-4B-Instruct leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-vl-4b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-vl-4b-instruct.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"north-mini-code-1-0\", \"b\": \"qwen3-vl-4b-instruct\"}`. From a terminal: `anchor compare north-mini-code-1-0 qwen3-vl-4b-instruct`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/north-mini-code-1-0.json and https://www.anchorterminal.com/api/v1/tools/qwen3-vl-4b-instruct.json\n\n## Other comparisons with North Mini Code 1.0 or Qwen3-VL-4B-Instruct\n\n- [Agents-A1-4B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/agents-a1-4b-vs-north-mini-code-1-0.md)\n- [Agents-A1-4B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-vl-4b-instruct.md)\n- [Apertus v1.5 8B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-north-mini-code-1-0.md)\n- [Apertus v1.5 8B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-vl-4b-instruct.md)\n- [Apodex-1.1-mini vs North Mini Code 1.0](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-north-mini-code-1-0.md)\n- [Apodex-1.1-mini vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-vl-4b-instruct.md)\n- [Arete-27B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/arete-27b-vs-north-mini-code-1-0.md)\n- [Arete-27B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-4b-instruct.md)\n- [BigBang-v1 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/bigbang-v1-vs-north-mini-code-1-0.md)\n- [BigBang-v1 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-vl-4b-instruct.md)\n- [Carbon-X vs North Mini Code 1.0](https://www.anchorterminal.com/compare/carbon-x-vs-north-mini-code-1-0.md)\n- [Carbon-X vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-vl-4b-instruct.md)\n- [Cola2 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/cola2-vs-north-mini-code-1-0.md)\n- [Cola2 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/cola2-vs-qwen3-vl-4b-instruct.md)\n- [Cosmos-Reason2-2B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-north-mini-code-1-0.md)\n- [Cosmos-Reason2-2B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-vl-4b-instruct.md)\n- [Darwin-36B-Opus vs North Mini Code 1.0](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-north-mini-code-1-0.md)\n- [Darwin-36B-Opus vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-vl-4b-instruct.md)\n- [DeepSeek-V3.2 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-north-mini-code-1-0.md)\n- [DeepSeek-V3.2 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-vl-4b-instruct.md)\n- [DeepSeek V4.1 Flash vs North Mini Code 1.0](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-north-mini-code-1-0.md)\n- 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IT vs North Mini Code 1.0](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-north-mini-code-1-0.md)\n- [Gemma 4 E2B IT vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-vl-4b-instruct.md)\n- [Gemma 4 E4B IT vs North Mini Code 1.0](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-north-mini-code-1-0.md)\n- [Gemma 4 E4B IT vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-vl-4b-instruct.md)\n- [GLM-4.5-Air vs North Mini Code 1.0](https://www.anchorterminal.com/compare/glm-4-5-air-vs-north-mini-code-1-0.md)\n- [GLM-4.5-Air vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-vl-4b-instruct.md)\n- [GLM-4.7-Flash vs North Mini Code 1.0](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-north-mini-code-1-0.md)\n- [GLM-4.7-Flash vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-4b-instruct.md)\n- [GLM-5.3-Flash vs North Mini 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