{ "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 and WeVisOne-VL-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-4B leads on documentation and upkeep.", "b": { "slug": "wevisone-vl-4b", "name": "WeVisOne-VL-4B", "vendor": "Tencent (WeChat Vision)", "vendorUrl": "https://www.tencent.com", "kind": "weights", "category": "open-weights", "summary": "WeVisOne-VL-4B is an open-weight perception model of 4.8B parameters from Tencent's WeChat Vision team, built on Qwen3-VL. Name something in an image and it returns boxes, points, counts, text, masks or alpha mattes. Apache 2.0.", "url": "https://www.anchorterminal.com/tools/wevisone-vl-4b", "markdownUrl": "https://www.anchorterminal.com/tools/wevisone-vl-4b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/wevisone-vl-4b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/wevisone-vl-4b.json", "dns": "wevisone-vl-4b.grade.anchorterminal.com", "repo": "https://huggingface.co/tencent/WeVisOne-VL-4B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "tencent/WeVisOne-VL-4B" } ], "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, 9.66 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://github.com/Tencent/WeVisOne-VL", "capabilities": [ "model.open-weights", "docs.ocr" ], "tags": [ "open-weights", "model", "vision", "grounding", "object-detection", "segmentation", "matting", "ocr", "counting", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-10-27", "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": 66, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 70, "reach": 70, "risk": 55, "upkeep": 48 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A small Apache 2.0 perception model that finds, counts, reads and cuts out what you name in an image, and says None when it isn't there. Boxes and points run in vLLM, SGLang or Transformers. Masks and mattes need Tencent's package and a pickle file. Two weeks old, with no questions answered yet and no safety results.", "bestFor": "Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence", "strengths": [ "Seven perception tasks in one model, from boxes to alpha mattes", "Returns None for a target that isn't in the image", "Apache 2.0, ungated", "Each task's input and output is documented", "9.66 GB in BF16, so it fits a 12 GB machine" ], "weaknesses": [ "The pixel decoder ships as a pickle file", "Masks and mattes need Tencent's package from GitHub", "No quantised build from Tencent", "One image and one turn at a time, no video", "No reply from Tencent on the repository's discussions", "No context length, languages, safety results or data sources on the card" ], "agentNotes": [ "Use Tencent's package: `WeVisOneVL(\"tencent/WeVisOne-VL-4B\", backend=\"vllm\")` then `model.predict(image, task=..., query=...)`", "Tasks are detection, referring, point, count_box, count_point, ocr, ocr_grounding, segmentation and matting", "A None answer means the target isn't in the image. Treat it as a result, not an error", "mask_decoder.pt is a pickle. Load it only from the tencent repository, and check its SHA-256 against the Hub's", "Send one image a turn. Video grids are rejected by the decoder", "The card says it isn't for identifying individuals or for high-impact decisions on its own" ], "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": 70, "reach": 70, "risk": 55, "upkeep": 48 }, "provenanceScore": 68, "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": "git clone https://github.com/Tencent/WeVisOne-VL.git \u0026\u0026 cd WeVisOne-VL\npip install -r requirements.txt \u0026\u0026 pip install -e .\n# then, in Python: WeVisOneVL(\"tencent/WeVisOne-VL-4B\", backend=\"transformers\") or backend=\"vllm\"", "http": "# No HTTP API of its own. Tencent's package calls model.predict(image, task=\"detection\", query=[...]).\n# For boxes and points only, vLLM serves the backbone at http://localhost:8000/v1 with: vllm serve tencent/WeVisOne-VL-4B" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/wevisone-vl-4b" }, "sameCompany": [ "hy-memory", "tencentdb-agent-memory", "hunyuanocr", "hy-memory-30b-a3b", "hy3", "wevisone-vl-2b" ], "weights": { "trainedBy": "Tencent WeChat Vision", "params": "4.8B", "goodFor": "Finding, counting, reading and cutting out what you name in an image, on a 12 GB machine.", "runtimes": [ "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "WeVisOne-VL-4B (BF16 safetensors)", "url": "https://huggingface.co/tencent/WeVisOne-VL-4B/blob/main/model.safetensors", "who": "publisher", "precision": "bf16", "sizeGB": 9.66, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ], "note": "The only build. Masks and mattes also need mask_decoder.pt and Tencent's package" } ], "run": "vllm serve tencent/WeVisOne-VL-4B", "runBuild": "WeVisOne-VL-4B (BF16 safetensors)", "checked": "2026-11-10", "minMemoryGB": 12 }, "family": { "lab": "Tencent (Hunyuan / Hy)", "family": "WeVisOne-VL", "url": "https://www.anchorterminal.com/open-weights/#tencent", "others": [ { "name": "WeVisOne-VL-2B", "url": "https://huggingface.co/tencent/WeVisOne-VL-2B", "params": "2.4B (2,441,744,384 by the Hub's safetensors count)", "listing": "https://www.anchorterminal.com/tools/wevisone-vl-2b" } ], "olderCount": 22 }, "area": "models", "provenance": { "legalEntity": "Tencent (named in the LICENSE file's copyright line)", "domain": "tencent.com", "domainRegistered": "1998-09-14", "endpointOnVendorDomain": null, "terms": "https://huggingface.co/tencent/WeVisOne-VL-4B/blob/main/LICENSE", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/tencent/WeVisOne-VL-4B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The card names WeChat Vision, Tencent Inc. The domain's registration date is carried from our other Tencent listings.", "Nothing is hosted under this listing, so the model licence stands in for terms of service and no privacy policy is scored.", "The changelog is the commit history.", "Tencent's Security Response Center takes reports company-wide, per the Hy3 listing; nothing on the card links it." ], "score": 68 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/wevisone-vl-4b.json" }, "edges": [ { "by": 15, "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 30, the only build from Tencent is the BF16 safetensors file with its decoder, 9.7 GB, and 9.7 GB × 1.2 = 11.6 GB, within 16 GB. No runtime library has a build. Runtimes 15, the architecture is Qwen3VLForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors file, three at 5 each. Builds 0, Tencent publishes the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata with the LICENSE file in the 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. 30 + 15 + 0 + 15 + 10 + 0 = 70." }, "scores": { "a": 85, "b": 70 } }, { "by": 15, "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 (5,292 characters), Qwen3-VL's template. Tool-calling format 25, Read for a perception model as what it takes and returns. Trained tool calling 10, Read for a perception model as the inputs it was trained to use. Structured output 15, Read for a perception model as typed outputs. Reasoning control 0, the card says it writes structured output directly with no chain-of-thought reasoning. Context 0, Tencent states no context length for it. 20 + 25 + 10 + 15 + 0 + 0 = 70." }, "scores": { "a": 85, "b": 70 } }, { "by": 12, "category": "upkeep", "leads": "wevisone-vl-4b", "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 8, Intended use, limitations and the architecture are on the card. Training data 5, A general description in the README of a data engine and over 100 million annotated images, with no sources named and no cut-off. Last change 20, the last commit, on 27 October 2026, added links to the paper and project page, 14 days before this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (23 and 26 October 2026) has a reply from a tencent member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, 14 days old. File health 10, Config, template and card agree on the Qwen3-VL classes, and the card names mask_decoder.pt as required and it's there. 8 + 5 + 20 + 0 + 5 + 10 = 48." }, "scores": { "a": 36, "b": 48 } }, { "by": 5, "category": "risk", "leads": "north-mini-code-1-0", "name": "Risk", "reasons": { "a": "File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in Cohere's verified CohereLabs organisation, which cohere.com links to per our other Cohere listings (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 5, Cohere's trust centre holds a responsible disclosure policy with a bug bounty, as recorded on our Cohere listings (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.", "b": "File safety 15, the backbone is safetensors, but mask_decoder.pt, a PyTorch pickle the card calls a required component, sits beside it. Provenance 15, the files sit in the tencent organisation, which Tencent's 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 or in the README. Agent guidance 5, General cautions: the card says the release is for research, isn't for autonomous high-impact decisions or identifying individuals, and lists where masks go wrong. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide, as the Hy3 listing records (5). 15 + 15 + 0 + 5 + 15 + 5 = 55." }, "scores": { "a": 60, "b": 55 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Cohere", "b": "Tencent (WeChat Vision)", "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": "2026-10-27", "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 and WeVisOne-VL-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-4B leads on documentation and upkeep.", "question": "Which is better for AI agents, North Mini Code 1.0 or WeVisOne-VL-4B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 85 against 70", "Risk, 60 against 55", "Agent interface, 85 against 70" ], "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, 48 against 36" ], "also": null, "goodFor": "Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence", "slug": "wevisone-vl-4b", "watchFor": "The pixel decoder ships as a pickle file" } ], "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-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/agents-a1-4b-vs-wevisone-vl-4b.json", "title": "Agents-A1-4B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-north-mini-code-1-0.json", "title": "Apertus v1.5 8B vs North Mini Code 1.0", "url": "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-wevisone-vl-4b.json", "title": "Apertus v1.5 8B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-north-mini-code-1-0.json", "title": "Apodex-1.1-mini vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-wevisone-vl-4b.json", "title": "Apodex-1.1-mini vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-north-mini-code-1-0.json", "title": "Arete-27B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-4b.json", "title": "Arete-27B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-north-mini-code-1-0.json", "title": "BigBang-v1 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-wevisone-vl-4b.json", "title": "BigBang-v1 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-north-mini-code-1-0.json", "title": "Carbon-X vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-wevisone-vl-4b.json", "title": "Carbon-X vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-north-mini-code-1-0.json", "title": "Cola2 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/cola2-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-4b.json", "title": "Cola2 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-north-mini-code-1-0.json", "title": "Cosmos-Reason2-2B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-wevisone-vl-4b.json", "title": "Cosmos-Reason2-2B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-north-mini-code-1-0.json", "title": "Darwin-36B-Opus vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-wevisone-vl-4b.json", "title": "Darwin-36B-Opus vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-north-mini-code-1-0.json", "title": "DeepSeek-V3.2 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-wevisone-vl-4b.json", "title": "DeepSeek-V3.2 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-north-mini-code-1-0.json", "title": "DeepSeek V4.1 Flash vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-wevisone-vl-4b.json", "title": "DeepSeek V4.1 Flash vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-north-mini-code-1-0.json", "title": "DeepSeek-V4-Flash-0731 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-wevisone-vl-4b.json", "title": "DeepSeek-V4-Flash-0731 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-north-mini-code-1-0.json", "title": "Devstral Small 2 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-wevisone-vl-4b.json", "title": "Devstral Small 2 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-north-mini-code-1-0.json", "title": "DiffusionGemma 26B A4B IT vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-wevisone-vl-4b.json", "title": "DiffusionGemma 26B A4B IT vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-north-mini-code-1-0.json", "title": "EXAONE 4.5 33B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-wevisone-vl-4b.json", "title": "EXAONE 4.5 33B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-north-mini-code-1-0.json", "title": "Gemma 4 12B IT vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-wevisone-vl-4b.json", "title": "Gemma 4 12B IT vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-north-mini-code-1-0.json", "title": "Gemma 4 26B A4B IT vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-wevisone-vl-4b.json", "title": "Gemma 4 26B A4B IT vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-north-mini-code-1-0.json", "title": "Gemma 4 31B IT vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-wevisone-vl-4b.json", "title": "Gemma 4 31B IT vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-north-mini-code-1-0.json", "title": "Gemma 4 E2B IT vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-wevisone-vl-4b.json", "title": "Gemma 4 E2B IT vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-north-mini-code-1-0.json", "title": "Gemma 4 E4B IT vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-wevisone-vl-4b.json", "title": "Gemma 4 E4B IT vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-north-mini-code-1-0.json", "title": "GLM-4.5-Air vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-wevisone-vl-4b.json", "title": "GLM-4.5-Air vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-north-mini-code-1-0.json", "title": "GLM-4.7-Flash vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-wevisone-vl-4b.json", "title": "GLM-4.7-Flash vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-north-mini-code-1-0.json", "title": "GLM-5.3-Flash vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-wevisone-vl-4b.json", "title": "GLM-5.3-Flash vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-north-mini-code-1-0.json", "title": "GLM-5.3 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-wevisone-vl-4b.json", "title": "GLM-5.3 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-north-mini-code-1-0.json", "title": "gpt-oss-120b vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-wevisone-vl-4b.json", "title": "gpt-oss-120b vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-north-mini-code-1-0.json", "title": "gpt-oss-20b vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-wevisone-vl-4b.json", "title": "gpt-oss-20b vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-north-mini-code-1-0.json", "title": "Granite 4.2 30B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-wevisone-vl-4b.json", "title": "Granite 4.2 30B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-north-mini-code-1-0.json", "title": "Granite 4.2 3B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-wevisone-vl-4b.json", "title": "Granite 4.2 3B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-north-mini-code-1-0.json", "title": "Granite 4.2 8B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-wevisone-vl-4b.json", "title": "Granite 4.2 8B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-north-mini-code-1-0.json", "title": "Hy-Memory-30B-A3B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-4b.json", "title": "Hy-Memory-30B-A3B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-north-mini-code-1-0.json", "title": "Hy3 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/hy3-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-wevisone-vl-4b.json", "title": "Hy3 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/hy3-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-north-mini-code-1-0.json", "title": "Inkling-Small vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-wevisone-vl-4b.json", "title": "Inkling-Small vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-north-mini-code-1-0.json", "title": "Inkling vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/inkling-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-wevisone-vl-4b.json", "title": "Inkling vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/inkling-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-north-mini-code-1-0.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-wevisone-vl-4b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-north-mini-code-1-0.json", "title": "Kimi K3.1 Pro vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-4b.json", "title": "Kimi K3.1 Pro vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-north-mini-code-1-0.json", "title": "Kimi K3 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-wevisone-vl-4b.json", "title": "Kimi K3 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-north-mini-code-1-0.json", "title": "Kolibri-1 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-wevisone-vl-4b.json", "title": "Kolibri-1 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-north-mini-code-1-0.json", "title": "Kronos vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/kronos-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-wevisone-vl-4b.json", "title": "Kronos vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/kronos-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-north-mini-code-1-0.json", "title": "Laguna M.1 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-wevisone-vl-4b.json", "title": "Laguna M.1 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-north-mini-code-1-0.json", "title": "Laguna S 2.1 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-wevisone-vl-4b.json", "title": "Laguna S 2.1 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-north-mini-code-1-0.json", "title": "Laguna XS 2.1 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-wevisone-vl-4b.json", "title": "Laguna XS 2.1 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-north-mini-code-1-0.json", "title": "LBS-27B vs North Mini Code 1.0", "url": 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"url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-north-mini-code-1-0.json", "title": "LFM3-VL-2B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-4b.json", "title": "LFM3-VL-2B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-north-mini-code-1-0.json", "title": "Ling-3.0-tiny vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-wevisone-vl-4b.json", "title": "Ling-3.0-tiny vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-wevisone-vl-4b" }, { "json": 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"https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-north-mini-code-1-0.json", "title": "MiniCPM-o 4.5 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-wevisone-vl-4b.json", "title": "MiniCPM-o 4.5 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-north-mini-code-1-0.json", "title": "MiniCPM-V 4.7 1B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-4b.json", "title": "MiniCPM-V 4.7 1B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-4b" }, { "json": 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"title": "Nemotron 3 Nano 4B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-wevisone-vl-4b.json", "title": "Nemotron 3 Nano 4B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-wevisone-vl-4b" }, { "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", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-wevisone-vl-4b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-wevisone-vl-4b" }, { "json": 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"https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-4b.json", "title": "Spark-X2.6-4B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-4b.json", "title": "Step 3.7 Flash vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-wevisone-vl-4b.json", "title": "Swift 1.5 Qwen3.8-27B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-wevisone-vl-4b.json", "title": "Swift 1.5 Qwen3.8-Flash-Next vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-wevisone-vl-4b.json", "title": "ThinkingCap (Qwen3.8 27B) vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-wevisone-vl-4b.json", "title": "WeVisOne-VL-2B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/wevisone-vl-4b-vs-zaya1-8b.json", "title": "WeVisOne-VL-4B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/wevisone-vl-4b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-4b.json", "title": "Baidu Unlimited-OCR-2 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-4b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 15, "edge": "north-mini-code-1-0", "key": "reach", "name": "Reach", "north-mini-code-1-0": 85, "weight": 25, "wevisone-vl-4b": 70 }, { "by": 5, "edge": "north-mini-code-1-0", "key": "risk", "name": "Risk", "north-mini-code-1-0": 60, "weight": 20, "wevisone-vl-4b": 55 }, { "by": 15, "edge": "north-mini-code-1-0", "key": "interface", "name": "Agent interface", "north-mini-code-1-0": 85, "weight": 10, "wevisone-vl-4b": 70 }, { "by": 12, "edge": "wevisone-vl-4b", "key": "upkeep", "name": "Documentation and upkeep", "north-mini-code-1-0": 36, "weight": 10, "wevisone-vl-4b": 48 } ], "summary": "North Mini Code 1.0 and WeVisOne-VL-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-4B 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.", "wevisone-vl-4b": "A small Apache 2.0 perception model that finds, counts, reads and cuts out what you name in an image, and says None when it isn't there. Boxes and points run in vLLM, SGLang or Transformers. Masks and mattes need Tencent's package and a pickle file. Two weeks old, with no questions answered yet and no safety results." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-wevisone-vl-4b", "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-wevisone-vl-4b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-wevisone-vl-4b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-wevisone-vl-4b.min.md" }, "markdown": "North Mini Code 1.0 and WeVisOne-VL-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-4B 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- WeVisOne-VL-4B: grade C, 61/100, rank #84 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/wevisone-vl-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/wevisone-vl-4b.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 70\n- Risk, 60 against 55\n- Agent interface, 85 against 70\n\nWatch for: No limitations, safety results, languages or data description on the card\n\n### WeVisOne-VL-4B (C)\n\nGood for: Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence\n\nAhead on:\n- Documentation and upkeep, 48 against 36\n\nWatch for: The pixel decoder ships as a pickle file\n\n\n## Why the edge\n\n### Reach: North Mini Code 1.0 +15\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- WeVisOne-VL-4B (70): Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 9.7 GB, and 9.7 GB × 1.2 = 11.6 GB, within 16 GB. No runtime library has a build. Runtimes 15, the architecture is Qwen3VLForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors file, three at 5 each. Builds 0, Tencent publishes the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata with the LICENSE file in the 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. 30 + 15 + 0 + 15 + 10 + 0 = 70.\n\n### Agent interface: North Mini Code 1.0 +15\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- WeVisOne-VL-4B (70): Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template. Tool-calling format 25, Read for a perception model as what it takes and returns. Trained tool calling 10, Read for a perception model as the inputs it was trained to use. Structured output 15, Read for a perception model as typed outputs. Reasoning control 0, the card says it writes structured output directly with no chain-of-thought reasoning. Context 0, Tencent states no context length for it. 20 + 25 + 10 + 15 + 0 + 0 = 70.\n\n### Documentation and upkeep: WeVisOne-VL-4B +12\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- WeVisOne-VL-4B (48): Model card 8, Intended use, limitations and the architecture are on the card. Training data 5, A general description in the README of a data engine and over 100 million annotated images, with no sources named and no cut-off. Last change 20, the last commit, on 27 October 2026, added links to the paper and project page, 14 days before this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (23 and 26 October 2026) has a reply from a tencent member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, 14 days old. File health 10, Config, template and card agree on the Qwen3-VL classes, and the card names mask_decoder.pt as required and it's there. 8 + 5 + 20 + 0 + 5 + 10 = 48.\n\n### Risk: North Mini Code 1.0 +5\n\n- North Mini Code 1.0 (60): File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in Cohere's verified CohereLabs organisation, which cohere.com links to per our other Cohere listings (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 5, Cohere's trust centre holds a responsible disclosure policy with a bug bounty, as recorded on our Cohere listings (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.\n- WeVisOne-VL-4B (55): File safety 15, the backbone is safetensors, but mask_decoder.pt, a PyTorch pickle the card calls a required component, sits beside it. Provenance 15, the files sit in the tencent organisation, which Tencent's 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 or in the README. Agent guidance 5, General cautions: the card says the release is for research, isn't for autonomous high-impact decisions or identifying individuals, and lists where masks go wrong. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide, as the Hy3 listing records (5). 15 + 15 + 0 + 5 + 15 + 5 = 55.\n\n## Score by category\n\n| Category | Weight | North Mini Code 1.0 | WeVisOne-VL-4B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 70 | North Mini Code 1.0 +15 |\n| Risk | 20% (20 this run) | 60 | 55 | North Mini Code 1.0 +5 |\n| Agent interface | 10% (10 this run) | 85 | 70 | North Mini Code 1.0 +15 |\n| Documentation and upkeep | 10% (10 this run) | 36 | 48 | WeVisOne-VL-4B +12 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | North Mini Code 1.0 | WeVisOne-VL-4B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Cohere | Tencent (WeChat Vision) |\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 | 2026-10-27 |\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**WeVisOne-VL-4B.** A small Apache 2.0 perception model that finds, counts, reads and cuts out what you name in an image, and says None when it isn't there. Boxes and points run in vLLM, SGLang or Transformers. Masks and mattes need Tencent's package and a pickle file. Two weeks old, with no questions answered yet and no safety results.\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### WeVisOne-VL-4B\n\n1. Use Tencent's package: `WeVisOneVL(\"tencent/WeVisOne-VL-4B\", backend=\"vllm\")` then `model.predict(image, task=..., query=...)`\n2. Tasks are detection, referring, point, count_box, count_point, ocr, ocr_grounding, segmentation and matting\n3. A None answer means the target isn't in the image. Treat it as a result, not an error\n4. mask_decoder.pt is a pickle. Load it only from the tencent repository, and check its SHA-256 against the Hub's\n5. Send one image a turn. Video grids are rejected by the decoder\n6. The card says it isn't for identifying individuals or for high-impact decisions on its own\n\n## Questions\n\n### Which is better for AI agents, North Mini Code 1.0 or WeVisOne-VL-4B?\n\nNorth Mini Code 1.0 and WeVisOne-VL-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-4B 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-wevisone-vl-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-wevisone-vl-4b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"north-mini-code-1-0\", \"b\": \"wevisone-vl-4b\"}`. From a terminal: `anchor compare north-mini-code-1-0 wevisone-vl-4b`\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/wevisone-vl-4b.json\n\n## Other comparisons with North Mini Code 1.0 or WeVisOne-VL-4B\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 WeVisOne-VL-4B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-wevisone-vl-4b.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 WeVisOne-VL-4B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-wevisone-vl-4b.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 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"0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "North Mini Code 1.0 vs WeVisOne-VL-4B", "url": "" } ], "description": "North Mini Code 1.0 and WeVisOne-VL-4B score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. 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