{
  "data": {
    "a": {
      "slug": "apertus-v1-5-8b",
      "name": "Apertus v1.5 8B",
      "vendor": "Swiss AI Initiative (EPFL and ETH Zurich)",
      "vendorUrl": "https://www.swiss-ai.org",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Apertus v1.5 8B is the Swiss AI Initiative's open-data, open-weight multilingual model of 8.9B parameters that takes text, images and audio and answers in text, with optional thinking and a 262,144-token context. The download is gated behind a click-through.",
      "url": "https://www.anchorterminal.com/tools/apertus-v1-5-8b",
      "markdownUrl": "https://www.anchorterminal.com/tools/apertus-v1-5-8b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/apertus-v1-5-8b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/apertus-v1-5-8b.json",
      "dns": "apertus-v1-5-8b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B",
      "license": "Apache-2.0, with the Apertus Acceptable Use Policy (v1.5, 14 July 2026) accepted at the download gate",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "swiss-ai/Apertus-v1.5-8B"
        }
      ],
      "auth": "none",
      "authNotes": "The repository is gated (gated: auto). Downloading needs a Hugging Face account, accepting Apache 2.0, the Apertus Acceptable Use Policy and a privacy policy, and sharing your contact details, company and institutional email with the authors; Hugging Face approves the request automatically. The card, the file list, the chat template and the gate's text are public. We didn't accept the terms (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 plus the Acceptable Use Policy, which adds an indemnity for ETH Zurich and EPFL and requires anyone who redistributes the model to pass the policy on with an express act of assent. Running it costs the hardware, 18.4 GB of files in BF16 with the image and audio tokenizers in F32 (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/swiss-ai/Apertus-v1.5-8B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "multimodal",
        "multilingual",
        "open-data",
        "audio-input",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "gated",
        "self-hosted"
      ],
      "lastRelease": "2026-10-14",
      "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": 30,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 40,
          "risk": 75,
          "upkeep": 84
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-03"
        },
        "negative": 0,
        "verdict": "A fully open 8B cousin, training data included, for multilingual chat and tool calls with images and audio in. It needs about 22 GB in BF16 and Swiss AI's own vLLM or Transformers fork, since no released runtime supports it. The main risks are a gated download with a policy to pass on and tools that fail with thinking on.",
        "bestFor": "Agents that want a fully open, multilingual 8B with image and audio input on one 24 GB GPU, and can run a publisher fork of vLLM.",
        "strengths": [
          "Open training data, with every dataset listed by version and licence and the post-training data published",
          "Safety benchmarks reported for the released 8B, plus outside evaluators given early access",
          "Tool format documented by the publisher, with a parser named and several calls a turn",
          "Thinking that turns on per request, and 262,144 tokens of context",
          "Safetensors only, with a checksum on every file",
          "Swiss AI answered every question in the repository's discussions"
        ],
        "weaknesses": [
          "No released runtime supports it, only Swiss AI's forks of vLLM and Transformers",
          "No quantised build from Swiss AI, and the report warns 4-bit NVFP4 in attention gives NaN logits",
          "Gated download that collects contact details, under a use policy you must pass on when you redistribute",
          "Tool calling and thinking don't combine, and parallel calls are weaker on the 8B",
          "No structured output documentation and no knowledge cut-off"
        ],
        "agentNotes": [
          "Accept the terms on the card and set HF_TOKEN before downloading",
          "Use Swiss AI's Docker image ghcr.io/swiss-ai/vllm_apertus_1.5_release, since stock vLLM and Transformers don't load Apertus1p5ForConditionalGeneration yet",
          "Serve with `--chat-template-content-format string --enable-auto-tool-choice --tool-call-parser apertus` for tools, and leave thinking off when you need tools",
          "For thinking, set `--reasoning-parser apertus` and `--default-chat-template-kwargs.enable_thinking true`, and budget max tokens generously",
          "Audio is resampled to 24 kHz and costs 40 tokens a second, and video must be passed as frames",
          "Don't cast the loaded model with .half(), the image and audio tokenizers must stay in F32",
          "If you redistribute the weights, the policy says you must pass it on and get the recipient's express assent"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.8,
        "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": 40,
          "risk": 75,
          "upkeep": 84
        },
        "provenanceScore": 45,
        "checked": {
          "categories": {
            "interface": "2026-11-03",
            "reach": "2026-11-03",
            "risk": "2026-11-03",
            "upkeep": "2026-11-03"
          },
          "last": "2026-11-03",
          "oldest": "2026-11-03",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# accept the terms on the Hugging Face card first, then set HF_TOKEN\ndocker pull ghcr.io/swiss-ai/vllm_apertus_1.5_release:latest-amd64\nvllm serve swiss-ai/Apertus-v1.5-8B --chat-template-content-format string \\\n  --gpu-memory-utilization 0.6 --max-model-len 262144 \\\n  --enable-auto-tool-choice --tool-call-parser apertus",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"swiss-ai/Apertus-v1.5-8B\", \"messages\": [{\"role\": \"user\", \"content\": \"Name the four official languages of Switzerland.\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/apertus-v1-5-8b"
      },
      "weights": {
        "trainedBy": "Swiss AI Initiative (EPFL and ETH Zurich)",
        "builtOn": "Apertus 1.0 8B (continued pretraining of Swiss AI's own model, per the card)",
        "params": "8.9B",
        "context": 262144,
        "goodFor": "Fully open multilingual model for text, images and audio in with text out, instruction following, tool calls and optional thinking, for teams that want open data and a European publisher.",
        "runtimes": [],
        "builds": [
          {
            "name": "Apertus-v1.5-8B",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 18.4,
            "memoryGB": 24,
            "format": "safetensors",
            "runsOn": [
              "vLLM (Swiss AI fork)",
              "Transformers (Swiss AI fork)"
            ],
            "note": "Gated (click-through with contact details). Size is the Hub's file list summed, 17.2 GB of BF16 language-model shards plus 1.2 GB of F32 vision and audio tokenizers. No quantised build from Swiss AI"
          }
        ],
        "run": "docker pull ghcr.io/swiss-ai/vllm_apertus_1.5_release:latest-amd64 \u0026\u0026 vllm serve swiss-ai/Apertus-v1.5-8B --chat-template-content-format string --gpu-memory-utilization 0.6 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser apertus",
        "runBuild": "Apertus-v1.5-8B",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "Swiss AI Initiative (EPFL, ETH Zurich, CSCS)",
        "family": "Apertus (v1 2509, v1.1, v1.5)",
        "url": "https://www.anchorterminal.com/open-weights/#swiss-ai",
        "others": [
          {
            "name": "Apertus-v1.5-8B-base",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B-base",
            "params": "8B (Hub safetensors count 8.84B)"
          },
          {
            "name": "Apertus-v1.5-70B-base",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-70B-base",
            "params": "70B (Hub safetensors count 71.94B)"
          },
          {
            "name": "Apertus-v1.5-8B-SFT",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B-SFT",
            "params": "8B (Hub safetensors count 8.90B)"
          },
          {
            "name": "Apertus-v1.5-8B-RL",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B-RL",
            "params": "8B (Hub safetensors count 8.90B)"
          },
          {
            "name": "Apertus-v1.5-8B-DPO",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B-DPO",
            "params": "8B (Hub safetensors count 8.90B)"
          },
          {
            "name": "Apertus-v1.5-70B-SFT",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-70B-SFT",
            "params": "70B (Hub safetensors count 72.01B)"
          },
          {
            "name": "Apertus-v1.5-70B-RL",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-70B-RL",
            "params": "70B (Hub safetensors count 72.01B)"
          },
          {
            "name": "Apertus-v1.5-70B",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-70B",
            "params": "70B (Hub safetensors count 72.01B)"
          },
          {
            "name": "Apertus-v1.1-1.5B-Instruct",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-1.5B-Instruct",
            "params": "1.5B (Hub safetensors count 1.51B)"
          },
          {
            "name": "Apertus-v1.1-4B-Instruct",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-4B-Instruct",
            "params": "4B (Hub safetensors count 3.83B)"
          },
          {
            "name": "Apertus-v1.1-0.5B-Instruct",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-0.5B-Instruct",
            "params": "0.5B (Hub safetensors count 573M)"
          },
          {
            "name": "Apertus-v1.1-4B",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-4B",
            "params": "4B (Hub safetensors count 3.83B)"
          },
          {
            "name": "Apertus-v1.1-0.5B",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-0.5B",
            "params": "0.5B (Hub safetensors count 438M)"
          },
          {
            "name": "Apertus-v1.1-1.5B",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-1.5B",
            "params": "1.5B (Hub safetensors count 1.51B)"
          },
          {
            "name": "Apertus-8B-2509",
            "url": "https://huggingface.co/swiss-ai/Apertus-8B-2509",
            "params": "8B (Hub safetensors count 8.05B)"
          },
          {
            "name": "Apertus-70B-2509",
            "url": "https://huggingface.co/swiss-ai/Apertus-70B-2509",
            "params": "70B (Hub safetensors count 70.60B)"
          },
          {
            "name": "Apertus-70B-Instruct-2509",
            "url": "https://huggingface.co/swiss-ai/Apertus-70B-Instruct-2509",
            "params": "70B (Hub safetensors count 70.60B)"
          },
          {
            "name": "Apertus-8B-Instruct-2509",
            "url": "https://huggingface.co/swiss-ai/Apertus-8B-Instruct-2509",
            "params": "8B (Hub safetensors count 8.05B)"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "ETH Zurich and EPFL (the Swiss National AI Institute, named in the Acceptable Use Policy)",
        "domain": "swiss-ai.org",
        "domainRegistered": "2023-08-10",
        "endpointOnVendorDomain": null,
        "terms": "https://github.com/swiss-ai/apertus-legal/blob/main/apertus_1.5/USAGE_POLICY.pdf",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The Acceptable Use Policy names the Swiss National AI Institute, a partnership of ETH Zurich and EPFL. RDAP gives swiss-ai.org a registration date of 10 August 2023 and apertus-ai.org 5 August 2025.",
          "Nothing is hosted for this listing, so the licence and the Acceptable Use Policy stand in for terms of service and no privacy policy is scored. A separate privacy policy covers the contact details the gate collects.",
          "The Hugging Face commit history is behind the gate. The repository was last modified on 14 October 2026 per the Hub.",
          "www.swiss-ai.org answers 400 for /.well-known/security.txt and apertus-ai.org didn't answer through our proxy on 21 October 2026. ETH Zurich's security.txt and disclosure policy cover ETH's IT infrastructure."
        ],
        "score": 45
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/apertus-v1-5-8b.json",
      "live": {
        "slug": "apertus-v1-5-8b",
        "securityTxt": {
          "url": "https://swiss-ai.org/.well-known/security.txt",
          "state": "unknown",
          "checkedAt": "2026-11-10T16:09:00.762955628Z"
        },
        "domain": {
          "domain": "swiss-ai.org",
          "registered": "2023-08-10",
          "source": "https://rdap.publicinterestregistry.org/rdap/domain/swiss-ai.org",
          "checkedAt": "2026-11-08T14:53:29.32312593Z"
        },
        "updatedAt": "2026-11-10T16:09:00.762955628Z"
      }
    },
    "answer": "Apertus v1.5 8B and North Mini Code 1.0 score within a point of each other on agent readiness, 61 (C) and 61 (C). North Mini Code 1.0 leads on reach.",
    "b": {
      "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"
    },
    "edges": [
      {
        "by": 48,
        "category": "upkeep",
        "leads": "apertus-v1-5-8b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 14 of 20, languages and a knowledge cut-off are missing. Training data 20, every dataset is public and listed with versions, licences and counts. Last change 20, the card changed on 14 October 2026, 20 days before 3 November, and Swiss AI added its own evaluation-results files on 26 October and 3 November. Responsiveness 20, Swiss AI answered all three questions, unchanged on 3 November 2026. Version policy 5, earlier models stay in their own repositories without a dated note. File health 5, the gated config couldn't be checked against the card, and the card and the policy disagree on the output filter.",
          "b": "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."
        },
        "scores": {
          "a": 84,
          "b": 36
        }
      },
      {
        "by": 45,
        "category": "reach",
        "leads": "north-mini-code-1-0",
        "name": "Reach",
        "reasons": {
          "a": "Memory 25 of 30, the BF16 files are 18.4 GB, or 22.1 GB with a fifth added, which fits 24 GB. Runtimes 0, no released vLLM (to 0.33.0), SGLang (to 0.5.23), Transformers (to 5.22.1), llama.cpp, Ollama or LM Studio runs version 1.5 yet, only Swiss AI's forks. Builds 0, Swiss AI publishes no quantised build. Terms 10 of 15, Apache 2.0 with an acceptable use policy added at the gate. Download 5, a click-through that needs an account and your contact details. Hosted 0, no inference provider or publisher API was found, and Swiss AI's documentation page names none on 3 November 2026.",
          "b": "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."
        },
        "scores": {
          "a": 40,
          "b": 85
        }
      },
      {
        "by": 15,
        "category": "risk",
        "leads": "apertus-v1-5-8b",
        "name": "Risk",
        "reasons": {
          "a": "File safety 25, safetensors only and no code in the repository. Provenance 15, the files are in the swiss-ai organisation the report links, with a checksum on every file. Safety evaluation 10 of 20, published safety benchmarks run with Swiss AI's open harnesses and early access for three outside evaluators, but the manual evaluation is Swiss AI's own and can't be rerun. Agent guidance 10, the report says what the refusals cover, how far jailbreaks get and that capability limits are the main defence, with a personal-data filter to apply, but nothing on prompt injection for deployers. Licence risk 10, 5 off for the use policy. Security programme 5, ETH Zurich's disclosure policy, with no advisories found.",
          "b": "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."
        },
        "scores": {
          "a": 75,
          "b": 60
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Swiss AI Initiative (EPFL and ETH Zurich)",
        "b": "Cohere",
        "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, with the Apertus Acceptable Use Policy (v1.5, 14 July 2026) accepted at the download gate",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-14",
        "b": "2026-07-29",
        "name": "Last release"
      },
      {
        "a": "not read yet",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "2.8/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Apertus v1.5 8B and North Mini Code 1.0 score within a point of each other on agent readiness, 61 (C) and 61 (C). North Mini Code 1.0 leads on reach.",
        "question": "Which is better for AI agents, Apertus v1.5 8B or North Mini Code 1.0?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Risk, 75 against 60",
          "Documentation and upkeep, 84 against 36"
        ],
        "also": null,
        "goodFor": "Agents that want a fully open, multilingual 8B with image and audio input on one 24 GB GPU, and can run a publisher fork of vLLM.",
        "slug": "apertus-v1-5-8b",
        "watchFor": "No released runtime supports it, only Swiss AI's forks of vLLM and Transformers"
      },
      {
        "aheadOn": [
          "Reach, 85 against 40"
        ],
        "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"
      }
    ],
    "history": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apertus-v1-5-8b.json",
        "title": "Agents-A1-4B vs Apertus v1.5 8B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apertus-v1-5-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-north-mini-code-1-0.json",
        "title": "Agents-A1-4B vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-north-mini-code-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-apodex-1-1-mini.json",
        "title": "Apertus v1.5 8B vs Apodex-1.1-mini",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-apodex-1-1-mini"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-arete-27b.json",
        "title": "Apertus v1.5 8B vs Arete-27B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-arete-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-bigbang-v1.json",
        "title": "Apertus v1.5 8B vs BigBang-v1",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-bigbang-v1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-carbon-x.json",
        "title": "Apertus v1.5 8B vs Carbon-X",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-carbon-x"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cola2.json",
        "title": "Apertus v1.5 8B vs Cola2",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cola2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cosmos-reason2-2b.json",
        "title": "Apertus v1.5 8B vs Cosmos-Reason2-2B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cosmos-reason2-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-darwin-36b-opus.json",
        "title": "Apertus v1.5 8B vs Darwin-36B-Opus",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-darwin-36b-opus"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v3-2.json",
        "title": "Apertus v1.5 8B vs DeepSeek-V3.2",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v3-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-1-flash.json",
        "title": "Apertus v1.5 8B vs DeepSeek V4.1 Flash",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-1-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-flash-0731.json",
        "title": "Apertus v1.5 8B vs DeepSeek-V4-Flash-0731",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-flash-0731"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-devstral-small-2.json",
        "title": "Apertus v1.5 8B vs Devstral Small 2",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-devstral-small-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-diffusiongemma-26b-a4b-it.json",
        "title": "Apertus v1.5 8B vs DiffusionGemma 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-diffusiongemma-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-exaone-4-5-33b.json",
        "title": "Apertus v1.5 8B vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-exaone-4-5-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-12b-it.json",
        "title": "Apertus v1.5 8B vs Gemma 4 12B IT",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-12b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-26b-a4b-it.json",
        "title": "Apertus v1.5 8B vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-31b-it.json",
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        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e2b-it.json",
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        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e2b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e4b-it.json",
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        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-5-air.json",
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        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-5-air"
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-7-flash"
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        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3.json",
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        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3"
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3-flash.json",
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        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gpt-oss-120b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gpt-oss-20b.json",
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        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gpt-oss-20b"
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-30b.json",
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        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-30b"
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-3b.json",
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        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-8b.json",
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        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-8b"
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy-memory-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy-memory-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy3.json",
        "title": "Apertus v1.5 8B vs Hy3",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling.json",
        "title": "Apertus v1.5 8B vs Inkling",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling-small.json",
        "title": "Apertus v1.5 8B vs Inkling-Small",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling-small"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-internvl3-5-gpt-oss-20b-a4b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-internvl3-5-gpt-oss-20b-a4b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3.json",
        "title": "Apertus v1.5 8B vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3-1-pro.json",
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        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3-1-pro"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kolibri-1.json",
        "title": "Apertus v1.5 8B vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kolibri-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kronos.json",
        "title": "Apertus v1.5 8B vs Kronos",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kronos"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-m-1.json",
        "title": "Apertus v1.5 8B vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-m-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-s-2-1.json",
        "title": "Apertus v1.5 8B vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-s-2-1"
      },
      {
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-north-mini-code-1-0.json",
        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs North Mini Code 1.0",
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        "title": "Nemotron 3 Super 120B A12B vs North Mini Code 1.0",
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-north-mini-code-1-0.json",
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        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-occamy-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-occamy-1-0"
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        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-olmo-3-7b-think.json",
        "title": "North Mini Code 1.0 vs Olmo 3 7B Think",
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        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ornith-1-5-122b.json",
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        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ornith-1-5-122b"
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        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ornith-1-5-397b"
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        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-30b-a3b-instruct-2507.json",
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        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-30b-a3b-instruct-2507"
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        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-4b-instruct-2507"
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        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-5-4b"
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        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-5-9b.json",
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        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-8-flash-next.json",
        "title": "North Mini Code 1.0 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-8-flash-next"
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        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-coder-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-coder-next.json",
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        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-coder-next"
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        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "North Mini Code 1.0 vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-omni-30b-a3b-instruct"
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        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-vl-2b-instruct"
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        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "North Mini Code 1.0 vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-vl-30b-a3b-instruct"
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        "title": "North Mini Code 1.0 vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-vl-32b-instruct"
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        "title": "North Mini Code 1.0 vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-vl-4b-instruct"
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        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-vl-8b-instruct.json",
        "title": "North Mini Code 1.0 vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-vl-8b-instruct"
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        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwopus3-8-27b-flash-v2.json",
        "title": "North Mini Code 1.0 vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwopus3-8-27b-flash-v2"
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      {
        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-recursive-flint.json",
        "title": "North Mini Code 1.0 vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-recursive-flint"
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      {
        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ripple-65b-preview.json",
        "title": "North Mini Code 1.0 vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-sensenova-loop-9b-preview.json",
        "title": "North Mini Code 1.0 vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-sensenova-loop-9b-preview"
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      {
        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-smollm3-3b.json",
        "title": "North Mini Code 1.0 vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-smollm3-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-spark-x2-6-4b.json",
        "title": "North Mini Code 1.0 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-spark-x2-6-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-step-3-7-flash.json",
        "title": "North Mini Code 1.0 vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-swift-1-5-qwen3-8-27b.json",
        "title": "North Mini Code 1.0 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-swift-1-5-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "North Mini Code 1.0 vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-swift-1-5-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-thinkingcap-qwen3-8-27b.json",
        "title": "North Mini Code 1.0 vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-unlimited-ocr-2.json",
        "title": "North Mini Code 1.0 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-wevisone-vl-2b.json",
        "title": "North Mini Code 1.0 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-wevisone-vl-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-wevisone-vl-4b.json",
        "title": "North Mini Code 1.0 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-zaya1-8b.json",
        "title": "North Mini Code 1.0 vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-zaya1-8b"
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    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "apertus-v1-5-8b": 40,
        "by": 45,
        "edge": "north-mini-code-1-0",
        "key": "reach",
        "name": "Reach",
        "north-mini-code-1-0": 85,
        "weight": 25
      },
      {
        "apertus-v1-5-8b": 75,
        "by": 15,
        "edge": "apertus-v1-5-8b",
        "key": "risk",
        "name": "Risk",
        "north-mini-code-1-0": 60,
        "weight": 20
      },
      {
        "apertus-v1-5-8b": 85,
        "by": 0,
        "edge": "",
        "key": "interface",
        "name": "Agent interface",
        "north-mini-code-1-0": 85,
        "weight": 10
      },
      {
        "apertus-v1-5-8b": 84,
        "by": 48,
        "edge": "apertus-v1-5-8b",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "north-mini-code-1-0": 36,
        "weight": 10
      }
    ],
    "summary": "Apertus v1.5 8B and North Mini Code 1.0 score within a point of each other on agent readiness, 61 (C) and 61 (C). North Mini Code 1.0 leads on reach. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "apertus-v1-5-8b": "A fully open 8B cousin, training data included, for multilingual chat and tool calls with images and audio in. It needs about 22 GB in BF16 and Swiss AI's own vLLM or Transformers fork, since no released runtime supports it. The main risks are a gated download with a policy to pass on and tools that fail with thinking on.",
      "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."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-north-mini-code-1-0",
    "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-north-mini-code-1-0.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-north-mini-code-1-0.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-north-mini-code-1-0.min.md"
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  "markdown": "Apertus v1.5 8B and North Mini Code 1.0 score within a point of each other on agent readiness, 61 (C) and 61 (C). North Mini Code 1.0 leads on reach. Both do open-weight models you run yourself.\n\n- Apertus v1.5 8B: grade C, 61/100, rank #40 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/apertus-v1-5-8b.md · JSON https://www.anchorterminal.com/api/v1/tools/apertus-v1-5-8b.json\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- 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### Apertus v1.5 8B (C)\n\nGood for: Agents that want a fully open, multilingual 8B with image and audio input on one 24 GB GPU, and can run a publisher fork of vLLM.\n\nAhead on:\n- Risk, 75 against 60\n- Documentation and upkeep, 84 against 36\n\nWatch for: No released runtime supports it, only Swiss AI's forks of vLLM and Transformers\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- Reach, 85 against 40\n\nWatch for: No limitations, safety results, languages or data description on the card\n\n\n## Why the edge\n\n### Documentation and upkeep: Apertus v1.5 8B +48\n\n- Apertus v1.5 8B (84): Model card 14 of 20, languages and a knowledge cut-off are missing. Training data 20, every dataset is public and listed with versions, licences and counts. Last change 20, the card changed on 14 October 2026, 20 days before 3 November, and Swiss AI added its own evaluation-results files on 26 October and 3 November. Responsiveness 20, Swiss AI answered all three questions, unchanged on 3 November 2026. Version policy 5, earlier models stay in their own repositories without a dated note. File health 5, the gated config couldn't be checked against the card, and the card and the policy disagree on the output filter.\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\n### Reach: North Mini Code 1.0 +45\n\n- Apertus v1.5 8B (40): Memory 25 of 30, the BF16 files are 18.4 GB, or 22.1 GB with a fifth added, which fits 24 GB. Runtimes 0, no released vLLM (to 0.33.0), SGLang (to 0.5.23), Transformers (to 5.22.1), llama.cpp, Ollama or LM Studio runs version 1.5 yet, only Swiss AI's forks. Builds 0, Swiss AI publishes no quantised build. Terms 10 of 15, Apache 2.0 with an acceptable use policy added at the gate. Download 5, a click-through that needs an account and your contact details. Hosted 0, no inference provider or publisher API was found, and Swiss AI's documentation page names none on 3 November 2026.\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\n### Risk: Apertus v1.5 8B +15\n\n- Apertus v1.5 8B (75): File safety 25, safetensors only and no code in the repository. Provenance 15, the files are in the swiss-ai organisation the report links, with a checksum on every file. Safety evaluation 10 of 20, published safety benchmarks run with Swiss AI's open harnesses and early access for three outside evaluators, but the manual evaluation is Swiss AI's own and can't be rerun. Agent guidance 10, the report says what the refusals cover, how far jailbreaks get and that capability limits are the main defence, with a personal-data filter to apply, but nothing on prompt injection for deployers. Licence risk 10, 5 off for the use policy. Security programme 5, ETH Zurich's disclosure policy, with no advisories found.\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\n## Score by category\n\n| Category | Weight | Apertus v1.5 8B | North Mini Code 1.0 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 40 | 85 | North Mini Code 1.0 +45 |\n| Risk | 20% (20 this run) | 75 | 60 | Apertus v1.5 8B +15 |\n| Agent interface | 10% (10 this run) | 85 | 85 | even |\n| Documentation and upkeep | 10% (10 this run) | 84 | 36 | Apertus v1.5 8B +48 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Apertus v1.5 8B | North Mini Code 1.0 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Swiss AI Initiative (EPFL and ETH Zurich) | Cohere |\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, with the Apertus Acceptable Use Policy (v1.5, 14 July 2026) accepted at the download gate | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-14 | 2026-07-29 |\n| Terms last updated | not read yet | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 2.8/5 (4) | none |\n\n## Verdicts\n\n**Apertus v1.5 8B.** A fully open 8B cousin, training data included, for multilingual chat and tool calls with images and audio in. It needs about 22 GB in BF16 and Swiss AI's own vLLM or Transformers fork, since no released runtime supports it. The main risks are a gated download with a policy to pass on and tools that fail with thinking on.\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## Before you call either\n\n### Apertus v1.5 8B\n\n1. Accept the terms on the card and set HF_TOKEN before downloading\n2. Use Swiss AI's Docker image ghcr.io/swiss-ai/vllm_apertus_1.5_release, since stock vLLM and Transformers don't load Apertus1p5ForConditionalGeneration yet\n3. Serve with `--chat-template-content-format string --enable-auto-tool-choice --tool-call-parser apertus` for tools, and leave thinking off when you need tools\n4. For thinking, set `--reasoning-parser apertus` and `--default-chat-template-kwargs.enable_thinking true`, and budget max tokens generously\n5. Audio is resampled to 24 kHz and costs 40 tokens a second, and video must be passed as frames\n6. Don't cast the loaded model with .half(), the image and audio tokenizers must stay in F32\n7. If you redistribute the weights, the policy says you must pass it on and get the recipient's express assent\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## Questions\n\n### Which is better for AI agents, Apertus v1.5 8B or North Mini Code 1.0?\n\nApertus v1.5 8B and North Mini Code 1.0 score within a point of each other on agent readiness, 61 (C) and 61 (C). North Mini Code 1.0 leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-north-mini-code-1-0.json, and with the fewest tokens: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-north-mini-code-1-0.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"apertus-v1-5-8b\", \"b\": \"north-mini-code-1-0\"}`. From a terminal: `anchor compare apertus-v1-5-8b north-mini-code-1-0`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/apertus-v1-5-8b.json and https://www.anchorterminal.com/api/v1/tools/north-mini-code-1-0.json\n\n## Other comparisons with Apertus v1.5 8B or North Mini Code 1.0\n\n- [Agents-A1-4B vs Apertus v1.5 8B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-apertus-v1-5-8b.md)\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- [Apertus v1.5 8B vs Apodex-1.1-mini](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-apodex-1-1-mini.md)\n- [Apertus v1.5 8B vs Arete-27B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-arete-27b.md)\n- [Apertus v1.5 8B vs BigBang-v1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-bigbang-v1.md)\n- [Apertus v1.5 8B vs Carbon-X](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-carbon-x.md)\n- [Apertus v1.5 8B vs 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      {
        "name": "Apertus v1.5 8B vs North Mini Code 1.0",
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      }
    ],
    "description": "Apertus v1.5 8B and North Mini Code 1.0 score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side.",
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    "h1": "Apertus v1.5 8B vs North Mini Code 1.0",
    "image": "https://www.anchorterminal.com/assets/og/default.png",
    "path": "/compare/apertus-v1-5-8b-vs-north-mini-code-1-0",
    "published": "2026-10-01",
    "section": "tools",
    "title": "Apertus v1.5 8B vs North Mini Code 1.0 for AI agents (2026)",
    "toc": null,
    "updated": "2026-11-10",
    "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-north-mini-code-1-0"
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