{
  "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 SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 3 of 4 scored categories. SenseNova-Loop-9B-Preview leads on documentation and upkeep.",
    "b": {
      "slug": "sensenova-loop-9b-preview",
      "name": "SenseNova-Loop-9B-Preview",
      "vendor": "SenseTime (SenseNova)",
      "vendorUrl": "https://www.sensetime.com/en",
      "kind": "weights",
      "category": "open-weights",
      "summary": "SenseNova-Loop-9B-Preview is SenseTime's open-weight multimodal model of about 9B parameters. It runs 24 of its 32 decoder layers twice, with text and images in, tool calling, a thinking switch and a 256K context. Apache 2.0. No released runtime loads it.",
      "url": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview",
      "markdownUrl": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/sensenova-loop-9b-preview.json",
      "dns": "sensenova-loop-9b-preview.grade.anchorterminal.com",
      "repo": "https://huggingface.co/sensenova/SenseNova-Loop-9B-Preview",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "sensenova/SenseNova-Loop-9B-Preview"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under Apache 2.0. Running it yourself costs the hardware: the files are 38.1 GB because most weights are stored in 32-bit floats, and the card's example loads them as 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://github.com/OpenSenseNova/SenseNova-Loop",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "multimodal",
        "vision",
        "looped-transformer",
        "tool-calling",
        "reasoning",
        "long-context",
        "preview",
        "apache-2.0",
        "safetensors",
        "custom-code",
        "self-hosted"
      ],
      "lastRelease": "2026-11-02",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 46.8,
        "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": 110,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 60,
          "reach": 40,
          "risk": 50,
          "upkeep": 44
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "An unusual architecture under Apache 2.0, with a 256K training context, a thinking switch and tool calling in the template. It's a preview in practice too: no released runtime runs it, loading it runs the repository's code, the files are twice the size the card implies, and nobody from SenseNova has answered a question yet.",
        "bestFor": "Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today",
        "strengths": [
          "Apache 2.0, ungated",
          "A 256K training context, stated on the card",
          "Thinking can be switched per request",
          "The architecture and parameter counts are documented",
          "Changed in the last ten days"
        ],
        "weaknesses": [
          "No released runtime runs it. vLLM and SGLang need unmerged pull requests",
          "Loading it runs Python from the repository",
          "The files are 38.1 GB, mostly F32, though the card says BF16",
          "No quantised build",
          "No tool-calling documentation beyond the template",
          "No limitations, safety results or data sources, and the technical report isn't out",
          "None of 5 questions answered"
        ],
        "agentNotes": [
          "Read modeling_sensenova_loop.py before you load it. trust_remote_code runs it",
          "Pin transformers 5.18.0, the version the card checked",
          "Load with dtype=torch.bfloat16 as the card does, or the 38 GB of F32 weights stay at full size",
          "Pass enable_thinking=False to apply_chat_template for a direct answer",
          "Tool calls come back as tool_call blocks with function and parameter tags. Parse them yourself, since no runtime ships a parser",
          "Wait for the vLLM or SGLang pull request to land in a release before putting it behind an API"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "D",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 46.8
          }
        ],
        "editorialScores": {
          "interface": 60,
          "reach": 40,
          "risk": 50,
          "upkeep": 44
        },
        "provenanceScore": 51,
        "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 \"transformers==5.18.0\" accelerate safetensors\n# then AutoModelForCausalLM.from_pretrained(\"sensenova/SenseNova-Loop-9B-Preview\", trust_remote_code=True, dtype=torch.bfloat16)\n# trust_remote_code runs modeling_sensenova_loop.py from the repository. Read it first",
        "http": "# No released server runs it. The card's vLLM and SGLang commands build from open pull requests,\n# and its LightLLM example serves an OpenAI-compatible API at http://localhost:8000/v1 on four GPUs"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/sensenova-loop-9b-preview"
      },
      "weights": {
        "trainedBy": "SenseTime (SenseNova)",
        "params": "9B",
        "context": 262144,
        "goodFor": "Trying a looped transformer for long-context, tool-using work, if you can run the repository's own code on a large GPU.",
        "runtimes": [],
        "builds": [
          {
            "name": "SenseNova-Loop-9B-Preview (safetensors, mostly F32)",
            "url": "https://huggingface.co/sensenova/SenseNova-Loop-9B-Preview/tree/main",
            "who": "publisher",
            "precision": "f32",
            "sizeGB": 38.14,
            "memoryGB": 48,
            "format": "safetensors",
            "note": "Eight shards and the MTP head. Stored mostly as 32-bit floats; the card's example loads it as BF16, about half the size in memory"
          }
        ],
        "run": "python -c \"from transformers import AutoModelForCausalLM; AutoModelForCausalLM.from_pretrained('sensenova/SenseNova-Loop-9B-Preview', trust_remote_code=True)\"",
        "runBuild": "SenseNova-Loop-9B-Preview (safetensors, mostly F32)",
        "checked": "2026-11-10",
        "minMemoryGB": 48
      },
      "family": {
        "lab": "SenseTime (SenseNova)",
        "family": "SenseNova-Loop",
        "url": "https://www.anchorterminal.com/open-weights/#sensenova"
      },
      "area": "models",
      "provenance": {
        "legalEntity": "SenseTime, listed on the Stock Exchange of Hong Kong (00020) per its investor page. SenseNova is its foundation model series, per its site",
        "domain": "sensetime.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "https://github.com/OpenSenseNova/SenseNova-Loop/blob/main/LICENSE",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/sensenova/SenseNova-Loop-9B-Preview/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "The card names SenseNova only. SenseTime's site presents SenseNova as its foundation model series, and that is the tie between the two; the site wasn't read for a link to this repository.",
          "The domain's registration date wasn't looked up.",
          "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored.",
          "www.sensetime.com has no security.txt (404 on 10 November 2026). Its about page gives a security contact.",
          "The changelog is the commit history, 39 commits from 28 October to 2 November 2026."
        ],
        "score": 51
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.json"
    },
    "edges": [
      {
        "by": 45,
        "category": "reach",
        "leads": "north-mini-code-1-0",
        "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": "Memory 15, the only build is the safetensors set, 38.14 GB with its MTP head, since the Hub counts 9.41B of its parameters in F32. Runtimes 0, none of the six runs it in a released version. Builds 0, SenseNova publishes one set of weights and no quantised build. Terms 15, Apache 2.0 in the card's metadata and on the card, which links the LICENSE in the GitHub repository, and nothing added. Download 10, the Hub API reports gated false 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, and the card names no API that serves it. 15 + 0 + 0 + 15 + 10 + 0 = 40."
        },
        "scores": {
          "a": 85,
          "b": 40
        }
      },
      {
        "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": "Chat template 20, chat_template.jinja in the repository (7,756 characters). Tool-calling format 10, only the template says it: a tools block, and calls written as tool_call blocks with function and parameter tags. Trained tool calling 10, the card says the training recipes include tool trajectories and that the model is for agentic workflows. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the card documents enable_thinking in the chat template call, on or off per request, and the template puts the reasoning in think tags ahead of the answer. Context 10, 262,144 tokens in training, per the card's specification table, and config.json gives the language model 262,144 positions. 20 + 10 + 10 + 0 + 10 + 10 = 60."
        },
        "scores": {
          "a": 85,
          "b": 60
        }
      },
      {
        "by": 10,
        "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": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the sensenova organisation, which the GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on instructions inside tool results or images. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, SenseTime's about page gives a security contact (5). 15 + 15 + 0 + 0 + 15 + 5 = 50."
        },
        "scores": {
          "a": 60,
          "b": 50
        }
      },
      {
        "by": 8,
        "category": "upkeep",
        "leads": "sensenova-loop-9b-preview",
        "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": "Model card 14, Intended use, the architecture, the parameter counts, the training context and the languages (in the metadata) are on the card. Training data 5, one general sentence on the recipes, with no sources, sizes or cut-off. Last change 20, the last commit, on 2 November 2026, reworked the speculative decoding section of the card, 8 days before this check. Responsiveness 0, none of the 5 discussions that aren't pull requests (30 October to 9 November 2026) has a reply from a sensenova member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, labelled a preview, with dated news on the card. File health 0, the files contradict the card on precision. 14 + 5 + 20 + 0 + 5 + 0 = 44."
        },
        "scores": {
          "a": 36,
          "b": 44
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Cohere",
        "b": "SenseTime (SenseNova)",
        "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": "2026-11-02",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "not read yet",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      }
    ],
    "faq": [
      {
        "answer": "North Mini Code 1.0 scores 61 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 3 of 4 scored categories. SenseNova-Loop-9B-Preview leads on documentation and upkeep.",
        "question": "Which is better for AI agents, North Mini Code 1.0 or SenseNova-Loop-9B-Preview?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 85 against 40",
          "Risk, 60 against 50",
          "Agent interface, 85 against 60"
        ],
        "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": [
          "Documentation and upkeep, 44 against 36"
        ],
        "also": null,
        "goodFor": "Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today",
        "slug": "sensenova-loop-9b-preview",
        "watchFor": "No released runtime runs it. vLLM and SGLang need unmerged pull requests"
      }
    ],
    "history": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
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        "title": "Qwopus3.8-27B-Flash-V2 vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-sensenova-loop-9b-preview"
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        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-sensenova-loop-9b-preview.json",
        "title": "Recursive Flint vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-sensenova-loop-9b-preview"
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      {
        "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-sensenova-loop-9b-preview.json",
        "title": "Ripple-65B Preview vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-sensenova-loop-9b-preview"
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      {
        "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-smollm3-3b.json",
        "title": "SenseNova-Loop-9B-Preview vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-smollm3-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-spark-x2-6-4b.json",
        "title": "SenseNova-Loop-9B-Preview vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-spark-x2-6-4b"
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        "title": "SenseNova-Loop-9B-Preview vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-swift-1-5-qwen3-8-27b.json",
        "title": "SenseNova-Loop-9B-Preview vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "SenseNova-Loop-9B-Preview vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-swift-1-5-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-thinkingcap-qwen3-8-27b.json",
        "title": "SenseNova-Loop-9B-Preview vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-unlimited-ocr-2.json",
        "title": "SenseNova-Loop-9B-Preview vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b.json",
        "title": "SenseNova-Loop-9B-Preview vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-4b.json",
        "title": "SenseNova-Loop-9B-Preview vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-zaya1-8b.json",
        "title": "SenseNova-Loop-9B-Preview vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-zaya1-8b"
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    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 45,
        "edge": "north-mini-code-1-0",
        "key": "reach",
        "name": "Reach",
        "north-mini-code-1-0": 85,
        "sensenova-loop-9b-preview": 40,
        "weight": 25
      },
      {
        "by": 10,
        "edge": "north-mini-code-1-0",
        "key": "risk",
        "name": "Risk",
        "north-mini-code-1-0": 60,
        "sensenova-loop-9b-preview": 50,
        "weight": 20
      },
      {
        "by": 25,
        "edge": "north-mini-code-1-0",
        "key": "interface",
        "name": "Agent interface",
        "north-mini-code-1-0": 85,
        "sensenova-loop-9b-preview": 60,
        "weight": 10
      },
      {
        "by": 8,
        "edge": "sensenova-loop-9b-preview",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "north-mini-code-1-0": 36,
        "sensenova-loop-9b-preview": 44,
        "weight": 10
      }
    ],
    "summary": "North Mini Code 1.0 scores 61 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 3 of 4 scored categories. SenseNova-Loop-9B-Preview leads on documentation and upkeep. 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.",
      "sensenova-loop-9b-preview": "An unusual architecture under Apache 2.0, with a 256K training context, a thinking switch and tool calling in the template. It's a preview in practice too: no released runtime runs it, loading it runs the repository's code, the files are twice the size the card implies, and nobody from SenseNova has answered a question yet."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
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    "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-sensenova-loop-9b-preview.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-sensenova-loop-9b-preview.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-sensenova-loop-9b-preview.min.md"
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  "markdown": "North Mini Code 1.0 scores 61 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 3 of 4 scored categories. SenseNova-Loop-9B-Preview leads on documentation and upkeep. 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- SenseNova-Loop-9B-Preview: grade D, 46.8/100, rank #155 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.md · JSON https://www.anchorterminal.com/api/v1/tools/sensenova-loop-9b-preview.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- Reach, 85 against 40\n- Risk, 60 against 50\n- Agent interface, 85 against 60\n\nWatch for: No limitations, safety results, languages or data description on the card\n\n### SenseNova-Loop-9B-Preview (D)\n\nGood for: Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today\n\nAhead on:\n- Documentation and upkeep, 44 against 36\n\nWatch for: No released runtime runs it. vLLM and SGLang need unmerged pull requests\n\n\n## Why the edge\n\n### Reach: North Mini Code 1.0 +45\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- SenseNova-Loop-9B-Preview (40): Memory 15, the only build is the safetensors set, 38.14 GB with its MTP head, since the Hub counts 9.41B of its parameters in F32. Runtimes 0, none of the six runs it in a released version. Builds 0, SenseNova publishes one set of weights and no quantised build. Terms 15, Apache 2.0 in the card's metadata and on the card, which links the LICENSE in the GitHub repository, and nothing added. Download 10, the Hub API reports gated false 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, and the card names no API that serves it. 15 + 0 + 0 + 15 + 10 + 0 = 40.\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- SenseNova-Loop-9B-Preview (60): Chat template 20, chat_template.jinja in the repository (7,756 characters). Tool-calling format 10, only the template says it: a tools block, and calls written as tool_call blocks with function and parameter tags. Trained tool calling 10, the card says the training recipes include tool trajectories and that the model is for agentic workflows. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the card documents enable_thinking in the chat template call, on or off per request, and the template puts the reasoning in think tags ahead of the answer. Context 10, 262,144 tokens in training, per the card's specification table, and config.json gives the language model 262,144 positions. 20 + 10 + 10 + 0 + 10 + 10 = 60.\n\n### Risk: North Mini Code 1.0 +10\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- SenseNova-Loop-9B-Preview (50): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the sensenova organisation, which the GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on instructions inside tool results or images. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, SenseTime's about page gives a security contact (5). 15 + 15 + 0 + 0 + 15 + 5 = 50.\n\n### Documentation and upkeep: SenseNova-Loop-9B-Preview +8\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- SenseNova-Loop-9B-Preview (44): Model card 14, Intended use, the architecture, the parameter counts, the training context and the languages (in the metadata) are on the card. Training data 5, one general sentence on the recipes, with no sources, sizes or cut-off. Last change 20, the last commit, on 2 November 2026, reworked the speculative decoding section of the card, 8 days before this check. Responsiveness 0, none of the 5 discussions that aren't pull requests (30 October to 9 November 2026) has a reply from a sensenova member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, labelled a preview, with dated news on the card. File health 0, the files contradict the card on precision. 14 + 5 + 20 + 0 + 5 + 0 = 44.\n\n## Score by category\n\n| Category | Weight | North Mini Code 1.0 | SenseNova-Loop-9B-Preview | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 40 | North Mini Code 1.0 +45 |\n| Risk | 20% (20 this run) | 60 | 50 | North Mini Code 1.0 +10 |\n| Agent interface | 10% (10 this run) | 85 | 60 | North Mini Code 1.0 +25 |\n| Documentation and upkeep | 10% (10 this run) | 36 | 44 | SenseNova-Loop-9B-Preview +8 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **46.8 · D** | |\n\n## Facts side by side\n\n| Fact | North Mini Code 1.0 | SenseNova-Loop-9B-Preview |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Cohere | SenseTime (SenseNova) |\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 | 2026-11-02 |\n| Terms last updated | no document linked | not read yet |\n| Privacy policy last updated | no document linked | no document linked |\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**SenseNova-Loop-9B-Preview.** An unusual architecture under Apache 2.0, with a 256K training context, a thinking switch and tool calling in the template. It's a preview in practice too: no released runtime runs it, loading it runs the repository's code, the files are twice the size the card implies, and nobody from SenseNova has answered a question yet.\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### SenseNova-Loop-9B-Preview\n\n1. Read modeling_sensenova_loop.py before you load it. trust_remote_code runs it\n2. Pin transformers 5.18.0, the version the card checked\n3. Load with dtype=torch.bfloat16 as the card does, or the 38 GB of F32 weights stay at full size\n4. Pass enable_thinking=False to apply_chat_template for a direct answer\n5. Tool calls come back as tool_call blocks with function and parameter tags. Parse them yourself, since no runtime ships a parser\n6. Wait for the vLLM or SGLang pull request to land in a release before putting it behind an API\n\n## Questions\n\n### Which is better for AI agents, North Mini Code 1.0 or SenseNova-Loop-9B-Preview?\n\nNorth Mini Code 1.0 scores 61 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 3 of 4 scored categories. SenseNova-Loop-9B-Preview leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-sensenova-loop-9b-preview.json, and with the fewest tokens: https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-sensenova-loop-9b-preview.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"north-mini-code-1-0\", \"b\": \"sensenova-loop-9b-preview\"}`. From a terminal: `anchor compare north-mini-code-1-0 sensenova-loop-9b-preview`\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/sensenova-loop-9b-preview.json\n\n## Other comparisons with North Mini Code 1.0 or SenseNova-Loop-9B-Preview\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 SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/agents-a1-4b-vs-sensenova-loop-9b-preview.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 SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-sensenova-loop-9b-preview.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 SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-sensenova-loop-9b-preview.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 SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/arete-27b-vs-sensenova-loop-9b-preview.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 SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/bigbang-v1-vs-sensenova-loop-9b-preview.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 SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/carbon-x-vs-sensenova-loop-9b-preview.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 SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/cola2-vs-sensenova-loop-9b-preview.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 SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-sensenova-loop-9b-preview.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 SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-sensenova-loop-9b-preview.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 SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-sensenova-loop-9b-preview.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- [DeepSeek V4.1 Flash vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-sensenova-loop-9b-preview.md)\n- [DeepSeek-V4-Flash-0731 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-north-mini-code-1-0.md)\n- [DeepSeek-V4-Flash-0731 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-sensenova-loop-9b-preview.md)\n- [Devstral Small 2 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/devstral-small-2-vs-north-mini-code-1-0.md)\n- [Devstral Small 2 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/devstral-small-2-vs-sensenova-loop-9b-preview.md)\n- [DiffusionGemma 26B A4B IT vs North Mini Code 1.0](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-north-mini-code-1-0.md)\n- [DiffusionGemma 26B A4B IT vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-sensenova-loop-9b-preview.md)\n- [EXAONE 4.5 33B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-north-mini-code-1-0.md)\n- [EXAONE 4.5 33B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-sensenova-loop-9b-preview.md)\n- [Gemma 4 12B IT vs North Mini Code 1.0](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-north-mini-code-1-0.md)\n- [Gemma 4 12B IT vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-sensenova-loop-9b-preview.md)\n- [Gemma 4 26B A4B IT vs North Mini Code 1.0](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-north-mini-code-1-0.md)\n- [Gemma 4 26B A4B IT vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-sensenova-loop-9b-preview.md)\n- [Gemma 4 31B IT vs North Mini Code 1.0](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-north-mini-code-1-0.md)\n- [Gemma 4 31B IT vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-sensenova-loop-9b-preview.md)\n- [Gemma 4 E2B 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 SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-sensenova-loop-9b-preview.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 SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-sensenova-loop-9b-preview.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 SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/glm-4-5-air-vs-sensenova-loop-9b-preview.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 SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-sensenova-loop-9b-preview.md)\n- [GLM-5.3-Flash vs North Mini Code 1.0](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-north-mini-code-1-0.md)\n- [GLM-5.3-Flash vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-sensenova-loop-9b-preview.md)\n- [GLM-5.3 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/glm-5-3-vs-north-mini-code-1-0.md)\n- [GLM-5.3 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/glm-5-3-vs-sensenova-loop-9b-preview.md)\n- [gpt-oss-120b vs North Mini Code 1.0](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-north-mini-code-1-0.md)\n- [gpt-oss-120b vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-sensenova-loop-9b-preview.md)\n- [gpt-oss-20b vs North Mini Code 1.0](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-north-mini-code-1-0.md)\n- [gpt-oss-20b vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-sensenova-loop-9b-preview.md)\n- [Granite 4.2 30B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-north-mini-code-1-0.md)\n- [Granite 4.2 30B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-sensenova-loop-9b-preview.md)\n- [Granite 4.2 3B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-north-mini-code-1-0.md)\n- [Granite 4.2 3B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-sensenova-loop-9b-preview.md)\n- [Granite 4.2 8B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-north-mini-code-1-0.md)\n- [Granite 4.2 8B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-sensenova-loop-9b-preview.md)\n- [Hy-Memory-30B-A3B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-north-mini-code-1-0.md)\n- [Hy-Memory-30B-A3B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-sensenova-loop-9b-preview.md)\n- [Hy3 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/hy3-vs-north-mini-code-1-0.md)\n- [Hy3 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/hy3-vs-sensenova-loop-9b-preview.md)\n- [Inkling-Small vs North Mini Code 1.0](https://www.anchorterminal.com/compare/inkling-small-vs-north-mini-code-1-0.md)\n- [Inkling-Small vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/inkling-small-vs-sensenova-loop-9b-preview.md)\n- [Inkling vs North Mini Code 1.0](https://www.anchorterminal.com/compare/inkling-vs-north-mini-code-1-0.md)\n- [Inkling vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/inkling-vs-sensenova-loop-9b-preview.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs North Mini Code 1.0](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-north-mini-code-1-0.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-sensenova-loop-9b-preview.md)\n- [Kimi K3.1 Pro vs North Mini Code 1.0](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-north-mini-code-1-0.md)\n- [Kimi K3.1 Pro vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-sensenova-loop-9b-preview.md)\n- [Kimi K3 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/kimi-k3-vs-north-mini-code-1-0.md)\n- [Kimi K3 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/kimi-k3-vs-sensenova-loop-9b-preview.md)\n- [Kolibri-1 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/kolibri-1-vs-north-mini-code-1-0.md)\n- [Kolibri-1 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/kolibri-1-vs-sensenova-loop-9b-preview.md)\n- [Kronos vs North Mini Code 1.0](https://www.anchorterminal.com/compare/kronos-vs-north-mini-code-1-0.md)\n- [Kronos vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/kronos-vs-sensenova-loop-9b-preview.md)\n- [Laguna M.1 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/laguna-m-1-vs-north-mini-code-1-0.md)\n- [Laguna M.1 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/laguna-m-1-vs-sensenova-loop-9b-preview.md)\n- [Laguna S 2.1 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-north-mini-code-1-0.md)\n- [Laguna S 2.1 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-sensenova-loop-9b-preview.md)\n- [Laguna XS 2.1 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-north-mini-code-1-0.md)\n- [Laguna XS 2.1 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-sensenova-loop-9b-preview.md)\n- [LBS-27B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/lbs-27b-vs-north-mini-code-1-0.md)\n- [LBS-27B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/lbs-27b-vs-sensenova-loop-9b-preview.md)\n- [LFM2.5-2.6B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-north-mini-code-1-0.md)\n- [LFM2.5-2.6B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-sensenova-loop-9b-preview.md)\n- [LFM3-1.7B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-north-mini-code-1-0.md)\n- [LFM3-1.7B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-sensenova-loop-9b-preview.md)\n- [LFM3-Audio-2B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-north-mini-code-1-0.md)\n- [LFM3-Audio-2B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-sensenova-loop-9b-preview.md)\n- [LFM3-Omni-2B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-north-mini-code-1-0.md)\n- [LFM3-Omni-2B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-sensenova-loop-9b-preview.md)\n- [LFM3-VL-2B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-north-mini-code-1-0.md)\n- [LFM3-VL-2B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-sensenova-loop-9b-preview.md)\n- [Ling-3.0-tiny vs North Mini Code 1.0](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-north-mini-code-1-0.md)\n- [Ling-3.0-tiny vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-sensenova-loop-9b-preview.md)\n- [Ling-3.1-flash-1019 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-north-mini-code-1-0.md)\n- [Ling-3.1-flash-1019 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-sensenova-loop-9b-preview.md)\n- [LongCat-2.5 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/longcat-2-5-vs-north-mini-code-1-0.md)\n- [LongCat-2.5 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/longcat-2-5-vs-sensenova-loop-9b-preview.md)\n- [MiMo-V2.6 Distill Qwen 9B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-north-mini-code-1-0.md)\n- [MiMo-V2.6 Distill Qwen 9B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-sensenova-loop-9b-preview.md)\n- [MiMo-V2.6-Flash-RL vs North Mini Code 1.0](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-north-mini-code-1-0.md)\n- [MiMo-V2.6-Flash-RL vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-sensenova-loop-9b-preview.md)\n- [MiniCPM-o 4.5 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-north-mini-code-1-0.md)\n- [MiniCPM-o 4.5 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-sensenova-loop-9b-preview.md)\n- [MiniCPM-V 4.7 1B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-north-mini-code-1-0.md)\n- [MiniCPM-V 4.7 1B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-sensenova-loop-9b-preview.md)\n- [MiniCPM-V 4.7 35B-A3B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-north-mini-code-1-0.md)\n- [MiniCPM-V 4.7 35B-A3B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-sensenova-loop-9b-preview.md)\n- [MiniCPM5-2B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/minicpm5-2b-vs-north-mini-code-1-0.md)\n- [MiniCPM5-2B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minicpm5-2b-vs-sensenova-loop-9b-preview.md)\n- [MiniMax-M2.5 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/minimax-m2-5-vs-north-mini-code-1-0.md)\n- [MiniMax-M2.5 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minimax-m2-5-vs-sensenova-loop-9b-preview.md)\n- [MiniMax-M2.7 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/minimax-m2-7-vs-north-mini-code-1-0.md)\n- [MiniMax-M2.7 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minimax-m2-7-vs-sensenova-loop-9b-preview.md)\n- [MiniMax-M3.1-Flash vs North Mini Code 1.0](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-north-mini-code-1-0.md)\n- [MiniMax-M3.1-Flash vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-sensenova-loop-9b-preview.md)\n- 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[SenseNova-Loop-9B-Preview vs ZAYA1-8B](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-zaya1-8b.md)\n",
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    "title": "North Mini Code 1.0 vs SenseNova-Loop-9B-Preview for AI agents (2026)",
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