{ "data": { "a": { "slug": "bigbang-v1", "name": "BigBang-v1", "vendor": "The Endless Frontier (endless-frontier)", "vendorUrl": "https://huggingface.co/endless-frontier", "kind": "weights", "category": "open-weights", "summary": "BigBang-v1 is The Endless Frontier's general-purpose fine-tune of Qwen3.6-35B-A3B (35B mixture of experts, 3B active), post-trained on about 10,000 synthetic research and technical problems, published on Hugging Face in BF16 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/bigbang-v1", "markdownUrl": "https://www.anchorterminal.com/tools/bigbang-v1.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/bigbang-v1.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/bigbang-v1.json", "dns": "bigbang-v1.grade.anchorterminal.com", "repo": "https://huggingface.co/endless-frontier/BigBang-v1", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "endless-frontier/BigBang-v1" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, the same licence as the Qwen3.6-35B-A3B base. Running it yourself costs the hardware, 71.9 GB for the BF16 files, the only build from the publisher (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/endless-frontier/BigBang-v1", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "derivative", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-08-06", "graded": true, "anchor": { "graded": true, "score": 56.3, "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": 87, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 60, "reach": 60, "risk": 55, "upkeep": 46 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-04" }, "negative": 0, "verdict": "A Qwen3.6-35B-A3B fine-tuned on synthetic research problems, under plain Apache 2.0, for search, science and coding work. The publisher ships BF16 only, about 86 GB to run, and has no report, safety results or replies to questions, so smaller builds and support come from others.", "bestFor": "Agents with 96 GB or more that want an Apache 2.0 mixture-of-experts model tuned for research, search and coding, and can do without publisher support.", "strengths": [ "Apache 2.0 like its base, not gated", "Same architecture, config and template as Qwen3.6-35B-A3B, so released llama.cpp, Ollama 0.40.14, vLLM, SGLang and Transformers run it", "Safetensors only with a SHA-256 for every weight file", "262,144 tokens of context and several tool calls a turn" ], "weaknesses": [ "BF16 is the only build from the publisher, needing about 86 GB", "No report, safety results, agent guidance or security contact", "Training data described only as about 10,000 synthetic examples", "No reply from the publisher to any of 7 discussions, and no change to the files or card since 6 August 2026", "Every benchmark figure is the publisher's own, with no set-up stated" ], "agentNotes": [ "On the reference machine use `vllm serve endless-frontier/BigBang-v1 --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`", "The card says to keep at least 128K of context so its thinking isn't cut short", "Thinking control isn't documented, try the base template's `enable_thinking` false in chat_template_kwargs and check the output", "For Ollama or llama.cpp you'll need someone else's GGUF; the publisher ships BF16 safetensors only", "Validate JSON output yourself or use the runtime's guided decoding" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.3, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 56.3 } ], "editorialScores": { "interface": 60, "reach": 60, "risk": 55, "upkeep": 46 }, "provenanceScore": 19, "checked": { "categories": { "interface": "2026-11-04", "reach": "2026-11-04", "risk": "2026-11-04", "upkeep": "2026-11-04" }, "last": "2026-11-04", "lastMoved": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "vllm serve endless-frontier/BigBang-v1 --max-model-len 262144 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or, with llama.cpp and bartowski's GGUF, a third party's conversion:\nllama-server -hf bartowski/endless-frontier_BigBang-v1-GGUF:Q4_K_M --jinja", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"endless-frontier/BigBang-v1\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/bigbang-v1" }, "weights": { "trainedBy": "The Endless Frontier (endless-frontier)", "builtOn": "Qwen/Qwen3.6-35B-A3B (Apache 2.0), declared in the card's metadata (base_model) and text. Identical config.json, parameter count and chat template, and the repository's LICENSE is Qwen's Apache 2.0 file. BigBang is also Apache 2.0", "params": "35B (3B active)", "active": "3B", "context": 262144, "goodFor": "General-purpose mixture-of-experts model tuned for scientific research, search and coding tasks with thinking on, text and images in.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "bartowski endless-frontier_BigBang-v1-GGUF (Q4_K_M)", "url": "https://huggingface.co/bartowski/endless-frontier_BigBang-v1-GGUF", "who": "bartowski", "precision": "q4_k_m", "sizeGB": 22.76, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not Endless Frontier's. 21.86 GB of Q4_K_M weights plus the 0.90 GB F16 projector, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it" }, { "name": "bartowski endless-frontier_BigBang-v1-GGUF (Q8_0)", "url": "https://huggingface.co/bartowski/endless-frontier_BigBang-v1-GGUF", "who": "bartowski", "precision": "q8_0", "sizeGB": 38.71, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not Endless Frontier's. 37.81 GB of Q8_0 weights plus the 0.90 GB F16 projector, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it" }, { "name": "BigBang-v1", "url": "https://huggingface.co/endless-frontier/BigBang-v1", "who": "publisher", "precision": "bf16", "sizeGB": 71.9, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ] } ], "run": "vllm serve endless-frontier/BigBang-v1 --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "BigBang-v1", "checked": "2026-11-04", "minMemoryGB": 32 }, "family": { "lab": "The Endless Frontier (endless-frontier)", "family": "BigBang", "url": "https://www.anchorterminal.com/open-weights/#endless-frontier", "olderCount": 2, "unrestrictedCopies": 3 }, "area": "models", "provenance": { "legalEntity": "", "domain": "", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "No legal entity, website, paper or GitHub repository is named on the card. The Hugging Face organisation endless-frontier (fullname The Endless Frontier, 17 members, three models) isn't verified.", "Nothing is hosted by the publisher, so the licence stands in for terms of service and no privacy policy is scored.", "No changelog. The commit messages are uploads and benchmark-table fixes.", "No security.txt, since there is no domain." ], "score": 19 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/bigbang-v1.json" }, "answer": "North Mini Code 1.0 scores 61 (C) on agent readiness against BigBang-v1's 56.3 (C), and leads in 3 of 4 scored categories. BigBang-v1 leads on documentation and upkeep.", "b": { "slug": "north-mini-code-1-0", "name": "North Mini Code 1.0", "vendor": "Cohere", "vendorUrl": "https://cohere.com", "kind": "weights", "category": "open-weights", "summary": "North Mini Code 1.0 is Cohere's open-weight coding agent model, a mixture of experts of 30B parameters with 3B active, trained for tool use, with interleaved thinking and a 256K context. Apache 2.0. Cohere's 4-bit build is 19 GB.", "url": "https://www.anchorterminal.com/tools/north-mini-code-1-0", "markdownUrl": "https://www.anchorterminal.com/tools/north-mini-code-1-0.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/north-mini-code-1-0.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/north-mini-code-1-0.json", "dns": "north-mini-code-1-0.grade.anchorterminal.com", "repo": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "CohereLabs/North-Mini-Code-1.0" }, { "registry": "huggingface", "name": "CohereLabs/North-Mini-Code-1.0-fp8" }, { "registry": "huggingface", "name": "CohereLabs/North-Mini-Code-1.0-w4a16" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the repositories is gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use under Apache 2.0. The card calls it a research release. Running it yourself takes 19.4 GB at 4 bits (w4a16), 32.1 GB in FP8 or 61.0 GB in BF16 (checked 2026-11-10).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "coding", "agentic", "reasoning", "tool-calling", "long-context", "apache-2.0", "safetensors", "fp8", "4-bit", "self-hosted" ], "lastRelease": "2026-07-29", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 63, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 85, "reach": 85, "risk": 60, "upkeep": 36 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A coding agent model that fits one 24 GB GPU at 4 bits, trained for tool use, with a 256K context, Apache 2.0 and four released runtimes. It's a research release and reads like one: nothing on limitations, safety or data, the card's run instructions are out of date, and Cohere hasn't answered a question on the repository.", "bestFor": "Developers who want a coding agent on their own GPU with a permissive licence", "strengths": [ "Trained for tool use in coding agents, with parsers in released vLLM", "19 GB at 4 bits from Cohere, and a 256K context", "Apache 2.0, ungated", "Four released runtimes carry the architecture", "Tool use documented with a worked example" ], "weaknesses": [ "No limitations, safety results, languages or data description on the card", "No reply from Cohere on the repository's discussions", "The card's instructions still point at unreleased vLLM and Transformers", "No GGUF from Cohere", "Text only", "No hosted API named" ], "agentNotes": [ "Serve it with vLLM 0.33.0 or later and `--tool-call-parser cohere_command4 --reasoning-parser cohere_command4 --enable-auto-tool-choice`, with `cohere_melody` installed", "Pass the model's thinking back with its tool calls on every turn. The card says results drop without it", "Describe tools in JSON schema and append tool results as a dictionary with the tool role", "Use temperature 1.0 and top_p 0.95", "For llama.cpp the GGUF files are Unsloth's or bartowski's conversions, not Cohere's", "It's text only. Don't send images" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 85, "reach": 85, "risk": 60, "upkeep": 36 }, "provenanceScore": 75, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install \"vllm\u003e=0.33.0\" \"cohere_melody\u003e=0.9.0\"\nvllm serve CohereLabs/North-Mini-Code-1.0 -tp 2 --max-model-len 320000 --tool-call-parser cohere_command4 --reasoning-parser cohere_command4 --enable-auto-tool-choice", "http": "# vLLM serves an OpenAI-compatible API at http://localhost:8000/v1. Use temperature 1.0 and top_p 0.95,\n# and send the model's earlier thinking back with each turn" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/north-mini-code-1-0" }, "sameCompany": [ "cohere-chat", "cohere-embed", "cohere-transcribe-03-2026", "cohere-north" ], "weights": { "trainedBy": "Cohere and Cohere Labs", "params": "30B", "active": "3B", "context": 256000, "goodFor": "A local coding agent: repository work, terminal tasks and long tool-calling sessions, on one 24 GB GPU at 4 bits.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "North-Mini-Code-1.0-w4a16", "url": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0-w4a16", "who": "publisher", "precision": "w4a16", "sizeGB": 19.38, "memoryGB": 24, "format": "safetensors", "runsOn": [ "vLLM" ], "note": "Cohere's 4-bit weight build" }, { "name": "unsloth North-Mini-Code-1.0-GGUF (UD-Q4_K_M)", "url": "https://huggingface.co/unsloth/North-Mini-Code-1.0-GGUF", "who": "unsloth", "precision": "q4_k_m", "sizeGB": 19.2, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From Unsloth, a third party's conversion and not Cohere's. 19.20 GB of UD-Q4_K_M weights, from the Hub's model information on 10 November 2026. Reach doesn't count it" }, { "name": "North-Mini-Code-1.0-fp8", "url": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0-fp8", "who": "publisher", "precision": "fp8", "sizeGB": 32.06, "memoryGB": 48, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ], "note": "Cohere's FP8 build" }, { "name": "unsloth North-Mini-Code-1.0-GGUF (Q8_0)", "url": "https://huggingface.co/unsloth/North-Mini-Code-1.0-GGUF", "who": "unsloth", "precision": "q8_0", "sizeGB": 32.44, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From Unsloth, a third party's conversion and not Cohere's. 32.44 GB of Q8_0 weights, from the Hub's model information on 10 November 2026. Reach doesn't count it" }, { "name": "North-Mini-Code-1.0 (BF16 safetensors)", "url": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0", "who": "publisher", "precision": "bf16", "sizeGB": 60.97, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ], "note": "49 files" } ], "run": "vllm serve CohereLabs/North-Mini-Code-1.0-w4a16 --tool-call-parser cohere_command4 --reasoning-parser cohere_command4 --enable-auto-tool-choice", "runBuild": "North-Mini-Code-1.0-w4a16", "checked": "2026-11-10", "minMemoryGB": 24 }, "family": { "lab": "Cohere (Cohere Labs)", "family": "North Mini Code", "url": "https://www.anchorterminal.com/open-weights/#cohere", "olderCount": 29 }, "area": "models", "provenance": { "legalEntity": "Cohere Inc.", "domain": "cohere.com", "domainRegistered": "2000-03-07", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The card says Developed by Cohere and Cohere Labs. Legal entity, domain and security facts are carried from our other Cohere listings.", "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored.", "The changelog is the commit history, 8 commits from 5 to 15 June 2026." ], "score": 75 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/north-mini-code-1-0.json" }, "edges": [ { "by": 25, "category": "reach", "leads": "north-mini-code-1-0", "name": "Reach", "reasons": { "a": "Memory 10 of 30, the only build from the publisher is BF16 at 71.9 GB, 86.3 GB with a fifth added, which fits the reference machine but not 64 GB. Runtimes 25, it keeps the base's qwen3_5_moe architecture, which llama.cpp, vLLM, SGLang and Transformers run in released versions, and Ollama 0.40.14 runs through the llama.cpp build it pins. Builds 0, no quantised build from the publisher. Terms 15, Apache 2.0 like its base. Download 10, not gated. Hosted 0, one inference provider, Featherless AI, where the line needs two. 10 + 25 + 0 + 15 + 10 + 0 = 60.", "b": "Memory 25, the smallest build from Cohere is the w4a16 repository, 19.38 GB, and 19.38 GB × 1.2 = 23.3 GB, within 24 GB. Unsloth's GGUF isn't read for this line. Runtimes 20, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the cohere2_moe model, and llama.cpp b12242 has the cohere2moe architecture. Builds 15, Quantised builds from Cohere at two precisions, FP8 and 4-bit weights (w4a16), each in its own repository. Terms 15, Apache 2.0 in the metadata and on the card, an OSI licence with nothing added. Download 10, the Hub API reports gated false for the model and its builds on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 25 + 20 + 15 + 15 + 10 + 0 = 85." }, "scores": { "a": 60, "b": 85 } }, { "by": 25, "category": "interface", "leads": "north-mini-code-1-0", "name": "Agent interface", "reasons": { "a": "Chat template 20, the base's template in the repository. Tool-calling format 10 of 25, the card names the qwen3_coder parser and nothing more. Trained tool calling 20, tuned for tool use with several calls a turn. Structured output 0. Reasoning control 0, the card doesn't say how to turn thinking off or budget it. Context 10, 262,144 tokens. 20 + 10 + 20 + 0 + 0 + 10 = 60.", "b": "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." }, "scores": { "a": 60, "b": 85 } }, { "by": 10, "category": "upkeep", "leads": "bigbang-v1", "name": "Documentation and upkeep", "reasons": { "a": "Model card 11 of 20, limitations, architecture and a cut-off are missing. Training data 5, a general description of about 10,000 synthetic examples. Last change 15, the card last changed on 6 August 2026, 90 days before this check. Responsiveness 0, no publisher reply to any of 7 questions. Version policy 5, a first release. File health 10, the files agree with the card and the base. 11 + 5 + 15 + 0 + 5 + 10 = 46.", "b": "Model card 11, Intended use, the architecture, the parameters and the context are on the card. Training data 0, the card gives the training method and nothing about the data. Last change 10, the main repository last changed on 15 June 2026 (outdated chat templates removed), 148 days before this check, and the draft model's repository on 29 July, 104 days. Responsiveness 0, none of the 11 most recent discussions that aren't pull requests (7 June to 6 November 2026) has a reply from a CohereLabs member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, with each build in its own repository. File health 10, Config, template and card agree on the architecture, and the June commit that removed broken templates says so. 11 + 0 + 10 + 0 + 5 + 10 = 36." }, "scores": { "a": 46, "b": 36 } }, { "by": 5, "category": "risk", "leads": "north-mini-code-1-0", "name": "Risk", "reasons": { "a": "File safety 25, safetensors only and nothing from the repository runs. Provenance 15, the files are in the publisher's own organisation with a SHA-256 for every weight file. Safety evaluation 0. Agent guidance 0. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 0, no website or security contact. 25 + 15 + 0 + 0 + 15 + 0 = 55.", "b": "File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in Cohere's verified CohereLabs organisation, which cohere.com links to per our other Cohere listings (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 5, Cohere's trust centre holds a responsible disclosure policy with a bug bounty, as recorded on our Cohere listings (5). 25 + 15 + 0 + 0 + 15 + 5 = 60." }, "scores": { "a": 55, "b": 60 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "The Endless Frontier (endless-frontier)", "b": "Cohere", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-08-06", "b": "2026-07-29", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "2.3/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "North Mini Code 1.0 scores 61 (C) on agent readiness against BigBang-v1's 56.3 (C), and leads in 3 of 4 scored categories. BigBang-v1 leads on documentation and upkeep.", "question": "Which is better for AI agents, BigBang-v1 or North Mini Code 1.0?" } ], "goodFor": [ { "aheadOn": [ "Documentation and upkeep, 46 against 36" ], "also": null, "goodFor": "Agents with 96 GB or more that want an Apache 2.0 mixture-of-experts model tuned for research, search and coding, and can do without publisher support.", "slug": "bigbang-v1", "watchFor": "BF16 is the only build from the publisher, needing about 86 GB" }, { "aheadOn": [ "Reach, 85 against 60", "Risk, 60 against 55", "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" } ], "history": { "bigbang-v1": [ { "causes": [ "correction" ], "date": "2026-11-04", "delta": 1.9, "from": 54.4, "fromGrade": "C", "to": 56.3, "toGrade": "C" } ], "north-mini-code-1-0": 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"json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-north-mini-code-1-0.json", "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/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-3-1-flash-1019-vs-north-mini-code-1-0.json", "title": "Ling-3.1-flash-1019 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-north-mini-code-1-0.json", "title": "LongCat-2.5 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-north-mini-code-1-0.json", "title": "MiMo-V2.6 Distill Qwen 9B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-north-mini-code-1-0.json", "title": "MiMo-V2.6-Flash-RL vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-north-mini-code-1-0.json", "title": "MiniCPM-o 4.5 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-north-mini-code-1-0" }, { "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-4-7-35b-a3b-vs-north-mini-code-1-0.json", "title": "MiniCPM-V 4.7 35B-A3B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-north-mini-code-1-0.json", "title": "MiniCPM5-2B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-north-mini-code-1-0.json", "title": "MiniMax-M2.5 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-north-mini-code-1-0.json", "title": "MiniMax-M2.7 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-north-mini-code-1-0.json", "title": "MiniMax-M3.1-Flash vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-north-mini-code-1-0.json", "title": "Mistral Large 4 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-north-mini-code-1-0.json", "title": "Muse Glimmer 30B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-north-mini-code-1-0.json", "title": "Nanbeige4.2-3B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-north-mini-code-1-0.json", "title": "Nanbeige4.3-33B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-north-mini-code-1-0.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-north-mini-code-1-0.json", "title": "Nemotron 3.5 Lightning 30B A3B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-north-mini-code-1-0.json", "title": "Nemotron 3 Nano 30B A3B vs North Mini Code 1.0", "url": 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"https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-north-mini-code-1-0.json", "title": "Nemotron 3 Ultra 550B A55B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-occamy-1-0.json", "title": "North Mini Code 1.0 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-olmo-3-7b-think.json", "title": "North Mini Code 1.0 vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ornith-1-5-122b.json", "title": "North Mini Code 1.0 vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ornith-1-5-35b-a3b.json", "title": "North Mini Code 1.0 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ornith-1-5-397b.json", "title": "North Mini Code 1.0 vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ornith-1-5-9b.json", "title": "North Mini Code 1.0 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-otel-2-0-llm-31b-it.json", "title": "North Mini Code 1.0 vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-otel-2-0-llm-31b-it" }, { "json": 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vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-vl-8b-instruct.json", "title": "North Mini Code 1.0 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwopus3-8-27b-flash-v2.json", "title": "North Mini Code 1.0 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-recursive-flint.json", "title": "North Mini Code 1.0 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ripple-65b-preview.json", "title": "North Mini Code 1.0 vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-sensenova-loop-9b-preview.json", "title": "North Mini Code 1.0 vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-smollm3-3b.json", "title": "North Mini Code 1.0 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-spark-x2-6-4b.json", "title": "North Mini Code 1.0 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-step-3-7-flash.json", "title": "North Mini Code 1.0 vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-swift-1-5-qwen3-8-27b.json", "title": "North Mini Code 1.0 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-swift-1-5-qwen3-8-flash-next.json", "title": "North Mini Code 1.0 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-thinkingcap-qwen3-8-27b.json", "title": "North Mini Code 1.0 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-unlimited-ocr-2.json", "title": "North Mini Code 1.0 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-wevisone-vl-2b.json", "title": "North Mini Code 1.0 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-wevisone-vl-4b.json", "title": "North Mini Code 1.0 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-zaya1-8b.json", "title": "North Mini Code 1.0 vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "bigbang-v1": 60, "by": 25, "edge": "north-mini-code-1-0", "key": "reach", "name": "Reach", "north-mini-code-1-0": 85, "weight": 25 }, { "bigbang-v1": 55, "by": 5, "edge": "north-mini-code-1-0", "key": "risk", "name": "Risk", "north-mini-code-1-0": 60, "weight": 20 }, { "bigbang-v1": 60, "by": 25, "edge": "north-mini-code-1-0", "key": "interface", "name": "Agent interface", "north-mini-code-1-0": 85, "weight": 10 }, { "bigbang-v1": 46, "by": 10, "edge": "bigbang-v1", "key": "upkeep", "name": "Documentation and upkeep", "north-mini-code-1-0": 36, "weight": 10 } ], "summary": "North Mini Code 1.0 scores 61 (C) on agent readiness against BigBang-v1's 56.3 (C), and leads in 3 of 4 scored categories. BigBang-v1 leads on documentation and upkeep. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "bigbang-v1": "A Qwen3.6-35B-A3B fine-tuned on synthetic research problems, under plain Apache 2.0, for search, science and coding work. The publisher ships BF16 only, about 86 GB to run, and has no report, safety results or replies to questions, so smaller builds and support come from others.", "north-mini-code-1-0": "A coding agent model that fits one 24 GB GPU at 4 bits, trained for tool use, with a 256K context, Apache 2.0 and four released runtimes. It's a research release and reads like one: nothing on limitations, safety or data, the card's run instructions are out of date, and Cohere hasn't answered a question on the repository." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/bigbang-v1-vs-north-mini-code-1-0", "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-north-mini-code-1-0.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/bigbang-v1-vs-north-mini-code-1-0.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/bigbang-v1-vs-north-mini-code-1-0.min.md" }, "markdown": "North Mini Code 1.0 scores 61 (C) on agent readiness against BigBang-v1's 56.3 (C), and leads in 3 of 4 scored categories. BigBang-v1 leads on documentation and upkeep. Both do open-weight models you run yourself.\n\n- BigBang-v1: grade C, 56.3/100, rank #116 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/bigbang-v1.md · JSON https://www.anchorterminal.com/api/v1/tools/bigbang-v1.json\n- North Mini Code 1.0: grade C, 61/100, rank #80 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/north-mini-code-1-0.md · JSON https://www.anchorterminal.com/api/v1/tools/north-mini-code-1-0.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### BigBang-v1 (C)\n\nGood for: Agents with 96 GB or more that want an Apache 2.0 mixture-of-experts model tuned for research, search and coding, and can do without publisher support.\n\nAhead on:\n- Documentation and upkeep, 46 against 36\n\nWatch for: BF16 is the only build from the publisher, needing about 86 GB\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 60\n- Risk, 60 against 55\n- Agent interface, 85 against 60\n\nWatch for: No limitations, safety results, languages or data description on the card\n\n\n## Why the edge\n\n### Reach: North Mini Code 1.0 +25\n\n- BigBang-v1 (60): Memory 10 of 30, the only build from the publisher is BF16 at 71.9 GB, 86.3 GB with a fifth added, which fits the reference machine but not 64 GB. Runtimes 25, it keeps the base's qwen3_5_moe architecture, which llama.cpp, vLLM, SGLang and Transformers run in released versions, and Ollama 0.40.14 runs through the llama.cpp build it pins. Builds 0, no quantised build from the publisher. Terms 15, Apache 2.0 like its base. Download 10, not gated. Hosted 0, one inference provider, Featherless AI, where the line needs two. 10 + 25 + 0 + 15 + 10 + 0 = 60.\n- North Mini Code 1.0 (85): Memory 25, the smallest build from Cohere is the w4a16 repository, 19.38 GB, and 19.38 GB × 1.2 = 23.3 GB, within 24 GB. Unsloth's GGUF isn't read for this line. Runtimes 20, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the cohere2_moe model, and llama.cpp b12242 has the cohere2moe architecture. Builds 15, Quantised builds from Cohere at two precisions, FP8 and 4-bit weights (w4a16), each in its own repository. Terms 15, Apache 2.0 in the metadata and on the card, an OSI licence with nothing added. Download 10, the Hub API reports gated false for the model and its builds on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 25 + 20 + 15 + 15 + 10 + 0 = 85.\n\n### Agent interface: North Mini Code 1.0 +25\n\n- BigBang-v1 (60): Chat template 20, the base's template in the repository. Tool-calling format 10 of 25, the card names the qwen3_coder parser and nothing more. Trained tool calling 20, tuned for tool use with several calls a turn. Structured output 0. Reasoning control 0, the card doesn't say how to turn thinking off or budget it. Context 10, 262,144 tokens. 20 + 10 + 20 + 0 + 0 + 10 = 60.\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\n### Documentation and upkeep: BigBang-v1 +10\n\n- BigBang-v1 (46): Model card 11 of 20, limitations, architecture and a cut-off are missing. Training data 5, a general description of about 10,000 synthetic examples. Last change 15, the card last changed on 6 August 2026, 90 days before this check. Responsiveness 0, no publisher reply to any of 7 questions. Version policy 5, a first release. File health 10, the files agree with the card and the base. 11 + 5 + 15 + 0 + 5 + 10 = 46.\n- North Mini Code 1.0 (36): Model card 11, Intended use, the architecture, the parameters and the context are on the card. Training data 0, the card gives the training method and nothing about the data. Last change 10, the main repository last changed on 15 June 2026 (outdated chat templates removed), 148 days before this check, and the draft model's repository on 29 July, 104 days. Responsiveness 0, none of the 11 most recent discussions that aren't pull requests (7 June to 6 November 2026) has a reply from a CohereLabs member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, with each build in its own repository. File health 10, Config, template and card agree on the architecture, and the June commit that removed broken templates says so. 11 + 0 + 10 + 0 + 5 + 10 = 36.\n\n### Risk: North Mini Code 1.0 +5\n\n- BigBang-v1 (55): File safety 25, safetensors only and nothing from the repository runs. Provenance 15, the files are in the publisher's own organisation with a SHA-256 for every weight file. Safety evaluation 0. Agent guidance 0. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 0, no website or security contact. 25 + 15 + 0 + 0 + 15 + 0 = 55.\n- North Mini Code 1.0 (60): File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in Cohere's verified CohereLabs organisation, which cohere.com links to per our other Cohere listings (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 5, Cohere's trust centre holds a responsible disclosure policy with a bug bounty, as recorded on our Cohere listings (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.\n\n## Score by category\n\n| Category | Weight | BigBang-v1 | North Mini Code 1.0 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 60 | 85 | North Mini Code 1.0 +25 |\n| Risk | 20% (20 this run) | 55 | 60 | North Mini Code 1.0 +5 |\n| Agent interface | 10% (10 this run) | 60 | 85 | North Mini Code 1.0 +25 |\n| Documentation and upkeep | 10% (10 this run) | 46 | 36 | BigBang-v1 +10 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **56.3 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | BigBang-v1 | North Mini Code 1.0 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | The Endless Frontier (endless-frontier) | Cohere |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-06 | 2026-07-29 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 2.3/5 (4) | none |\n\n## How the scores have moved\n\n### BigBang-v1\n\n- +1.9 on 4 November 2026: correction\n\n### North Mini Code 1.0\n\nNo score change since it was listed.\n\n## Verdicts\n\n**BigBang-v1.** A Qwen3.6-35B-A3B fine-tuned on synthetic research problems, under plain Apache 2.0, for search, science and coding work. The publisher ships BF16 only, about 86 GB to run, and has no report, safety results or replies to questions, so smaller builds and support come from others.\n\n**North Mini Code 1.0.** A coding agent model that fits one 24 GB GPU at 4 bits, trained for tool use, with a 256K context, Apache 2.0 and four released runtimes. It's a research release and reads like one: nothing on limitations, safety or data, the card's run instructions are out of date, and Cohere hasn't answered a question on the repository.\n\n## Before you call either\n\n### BigBang-v1\n\n1. On the reference machine use `vllm serve endless-frontier/BigBang-v1 --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`\n2. The card says to keep at least 128K of context so its thinking isn't cut short\n3. Thinking control isn't documented, try the base template's `enable_thinking` false in chat_template_kwargs and check the output\n4. For Ollama or llama.cpp you'll need someone else's GGUF; the publisher ships BF16 safetensors only\n5. Validate JSON output yourself or use the runtime's guided decoding\n\n### North Mini Code 1.0\n\n1. Serve it with vLLM 0.33.0 or later and `--tool-call-parser cohere_command4 --reasoning-parser cohere_command4 --enable-auto-tool-choice`, with `cohere_melody` installed\n2. Pass the model's thinking back with its tool calls on every turn. The card says results drop without it\n3. Describe tools in JSON schema and append tool results as a dictionary with the tool role\n4. Use temperature 1.0 and top_p 0.95\n5. For llama.cpp the GGUF files are Unsloth's or bartowski's conversions, not Cohere's\n6. It's text only. Don't send images\n\n## Questions\n\n### Which is better for AI agents, BigBang-v1 or North Mini Code 1.0?\n\nNorth Mini Code 1.0 scores 61 (C) on agent readiness against BigBang-v1's 56.3 (C), and leads in 3 of 4 scored categories. BigBang-v1 leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/bigbang-v1-vs-north-mini-code-1-0.json, and with the fewest tokens: https://www.anchorterminal.com/compare/bigbang-v1-vs-north-mini-code-1-0.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"bigbang-v1\", \"b\": \"north-mini-code-1-0\"}`. 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