{ "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 Ornith-1.5-122B score within a point of each other on agent readiness, 61 (C) and 61 (C).", "b": { "slug": "ornith-1-5-122b", "name": "Ornith-1.5-122B", "vendor": "Ornith", "vendorUrl": "https://ornith.ai", "kind": "weights", "category": "open-weights", "summary": "Ornith-1.5-122B is Ornith's open-weight mixture-of-experts reasoning model for coding and agent work, about 125B parameters on Qwen3.5's architecture with a 262,144-token context, published in BF16, FP8, NVFP4, GGUF and MLX and marked MIT.", "url": "https://www.anchorterminal.com/tools/ornith-1-5-122b", "markdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-122b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-122b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ornith-1-5-122b.json", "dns": "ornith-1-5-122b.grade.anchorterminal.com", "repo": "https://huggingface.co/ornith-ai/Ornith-1.5-122B", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-122B" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-122B-GGUF" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-122B-FP8" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-122B-NVFP4" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-122B-MLX-4bit" }, { "registry": "ollama", "name": "ornith-1.5:122b-q4_K_M" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and Ornith's six build repositories for this size are not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the MIT licence named in the card's metadata. The repository has no LICENSE file, and the card's licence link returns 404. Running it yourself costs the hardware, about 250 GB for the BF16 files, 69.7 GB for Ornith's MLX 4-bit build or 75.8 GB for its Q4_K_M GGUF (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/ornith-ai/Ornith-1.5-122B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "coding", "mit", "safetensors", "gguf", "mlx", "fp8", "nvfp4", "self-hosted" ], "lastRelease": "2026-10-24", "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": 64, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 85, "reach": 75, "risk": 50, "upkeep": 36 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "The largest Ornith that fits a 128 GB machine, a coding and terminal-agent cousin on Qwen3.5-122B-A10B's architecture with XML tool calls and 262,144 tokens of context. Its 4-bit builds need 84 to 93 GB. The main risk is an absent publisher, with no licence file, safety results or replies, and a card that misstates the hardware.", "bestFor": "Local coding and terminal agents on a 128 GB unified-memory machine or a 96 GB GPU setup, where a clear licence file and an answering publisher aren't required", "strengths": [ "4-bit builds from Ornith itself in GGUF, MLX and NVFP4, at 70 to 78 GB, plus Ollama's Q4_K_M", "Runs in vLLM, SGLang, Transformers, llama.cpp, Ollama and LM Studio from their released versions", "The card names the tool and reasoning parsers for vLLM and SGLang, with a tool-call example", "262,144 tokens of native context", "Safetensors and GGUF only, no gate and no code in the repository" ], "weaknesses": [ "No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on", "No safety evaluation, agent-use guidance or security contact", "The card says a single 80 GB GPU serves it, which its 250 GB of BF16 files don't fit, and names a Mobile variant that isn't published", "Ornith answered none of the 5 discussions, and the MLX builds were uploaded again with no note", "The two chat templates disagree on keeping earlier reasoning, and structured output isn't documented" ], "agentNotes": [ "Start it with `ollama run ornith-1.5:122b-q4_K_M`, or `llama-server -hf ornith-ai/Ornith-1.5-122B-GGUF:Q4_K_M --jinja -c 262144`", "Budget about 92 GB for the Q4_K_M GGUF with a working context, and less context if the machine has under 100 GB for the model", "In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed", "Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention", "Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it", "Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 for general tasks, as the card recommends", "Add mmproj-Ornith-1.5-122B-BF16.gguf (0.9 GB) for image input in llama.cpp, and validate JSON output yourself, since structured output isn't documented" ], "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": 75, "risk": 50, "upkeep": 36 }, "provenanceScore": 19, "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": "ollama run ornith-1.5:122b-q4_K_M\n# or, with llama.cpp and Ornith's own GGUF:\nllama-server -hf ornith-ai/Ornith-1.5-122B-GGUF:Q4_K_M --jinja -c 262144\n# or, with the card's vLLM command (abridged):\nvllm serve ornith-ai/Ornith-1.5-122B --served-model-name Ornith-1.5-122B --max-model-len 262144 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"ornith-1.5:122b-q4_K_M\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/ornith-1-5-122b" }, "sameCompany": [ "ornith-1-5-35b-a3b", "ornith-1-5-397b", "ornith-1-5-9b" ], "weights": { "trainedBy": "Ornith", "builtOn": "Qwen3.5-122B-A10B, inferred. Not declared in the metadata. Same qwen3_5_moe architecture and an identical parameter count to Qwen3.5-122B-A10B (Apache 2.0); the card says Ornith-1.0 was built on Qwen3.5 and Gemma 4 and that 1.5 extends it", "params": "125B", "context": 262144, "goodFor": "Coding and terminal-agent mixture-of-experts model with tool calling and thinking on by default, text and images in, the largest Ornith that fits a 128 GB machine at 4 bits.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Ornith-1.5-122B-NVFP4", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 77.7, "memoryGB": 96, "format": "safetensors", "note": "NVFP4 needs NVIDIA Blackwell" }, { "name": "Ornith-1.5-122B-Q4_K_M", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 75.8, "memoryGB": 96, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Add mmproj-Ornith-1.5-122B-BF16.gguf (0.9 GB) for image input" }, { "name": "Ornith-1.5-122B-MLX-4bit", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-MLX-4bit", "who": "publisher", "precision": "4bit", "sizeGB": 69.7, "memoryGB": 96, "format": "mlx", "note": "MLX runs on Apple silicon only" }, { "name": "ollama ornith-1.5:122b-q4_K_M", "url": "https://ollama.com/library/ornith-1.5:122b-q4_K_M", "who": "ollama", "precision": "q4_k_m", "sizeGB": 77, "memoryGB": 96, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M (76 GB) with a BF16 vision projector (912 MB), as Ollama's tag page shows it, rounded to whole GB. The 73 GB ornith-1.5:122b tag is marked for MLX and llama.cpp and its quantisation wasn't established, so it isn't listed as a build" }, { "name": "Ornith-1.5-122B-Q6_K", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 102.4, "memoryGB": 128, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "Ornith-1.5-122B-Q5_K_M", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-Q5_K_M.gguf", "who": "publisher", "precision": "q5_k_m", "sizeGB": 88.7, "memoryGB": 128, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "Ornith-1.5-122B-FP8", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 133, "memoryGB": 192, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ], "note": "Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-122B-Q8_0", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 132.6, "memoryGB": 192, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-122B-MLX-8bit", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-MLX-8bit", "who": "publisher", "precision": "8bit", "sizeGB": 130.7, "memoryGB": 192, "format": "mlx", "note": "MLX runs on Apple silicon only" }, { "name": "Ornith-1.5-122B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B", "who": "publisher", "precision": "bf16", "sizeGB": 250.2, "memoryGB": 512, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ], "note": "Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-122B-BF16", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-BF16.gguf", "who": "publisher", "precision": "bf16", "sizeGB": 249.4, "memoryGB": 512, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-122B-MLX", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-MLX", "who": "publisher", "precision": "bf16", "sizeGB": 245.2, "memoryGB": 512, "format": "mlx", "note": "MLX runs on Apple silicon only" } ], "run": "ollama run ornith-1.5:122b-q4_K_M", "runBuild": "ollama ornith-1.5:122b-q4_K_M", "checked": "2026-11-10", "minMemoryGB": 96 }, "family": { "lab": "Ornith", "family": "Ornith-1", "url": "https://www.anchorterminal.com/open-weights/#ornith", "others": [ { "name": "Ornith-1.0-9B", "url": "https://huggingface.co/ornith-ai/Ornith-1.0-9B", "params": "9B (dense)" }, { "name": "Ornith-1.0-35B", "url": "https://huggingface.co/ornith-ai/Ornith-1.0-35B", "params": "35B (MoE)" }, { "name": "Ornith-1.0-397B", "url": "https://huggingface.co/ornith-ai/Ornith-1.0-397B", "params": "397B (MoE)" }, { "name": "Ornith-1.5-9B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B", "params": "9B (dense)", "listing": "https://www.anchorterminal.com/tools/ornith-1-5-9b" }, { "name": "Ornith-1.5-9B-DFlash", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-DFlash", "params": "1.29B (Hub safetensors count)" }, { "name": "Ornith-1.5-35B-A3B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B", "params": "35B total, 3B active (MoE)", "listing": "https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b" }, { "name": "Ornith-1.5-35B-A3B-DFlash", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-DFlash", "params": "0.39B (Hub safetensors count)" }, { "name": "Ornith-1.5-397B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B", "params": "397B (MoE)", "listing": "https://www.anchorterminal.com/tools/ornith-1-5-397b" }, { "name": "Ornith-1.5-397B-DFlash", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-DFlash", "params": "1.29B (Hub safetensors count)" } ] }, "area": "models", "provenance": { "legalEntity": "", "domain": "ornith.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "No legal entity named on the card, the blog or the repository. The citation reads Ornith Team. Ollama's copy of the licence names deepreinforce.ai as the copyright holder.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored. The licence is MIT in the card's metadata only; the LICENSE file the card links returns 404.", "No changelog for the files. The commit messages are uploads, deletions and README updates.", "ornith.ai/.well-known/security.txt redirects to the home page, so there is no security.txt.", "unchecked: the domain's registration date." ], "score": 19 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/ornith-1-5-122b.json" }, "edges": [ { "by": 10, "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 10, since Ornith's own MLX 4-bit build is 69.7 GB and needs about 83.6 GB with the fifth added, over 64 GB and inside the reference machine's 107.4 GB, as do its Q4_K_M GGUF, its NVFP4 build and Ollama's build. Runtimes 25, the most the line gives, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2, llama.cpp's qwen35moe architecture, Ollama's library and LM Studio's catalogue for the qwen35moe architecture. Builds 15, Ornith publishes FP8, NVFP4, GGUF and MLX at several precisions. Terms 15, MIT as named in the card's metadata, with nothing added, read by its terms though the repository has no licence file. Download 10, nothing is gated. Hosted 0, Ornith runs no API and no inference provider serves it. 10 + 25 + 15 + 15 + 10 + 0 = 75." }, "scores": { "a": 85, "b": 75 } }, { "by": 10, "category": "risk", "leads": "north-mini-code-1-0", "name": "Risk", "reasons": { "a": "File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in Cohere's verified CohereLabs organisation, which cohere.com links to per our other Cohere listings (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 5, Cohere's trust centre holds a responsible disclosure policy with a bug bounty, as recorded on our Cohere listings (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.", "b": "File safety 25, safetensors and GGUF only and no Python in any of Ornith's repositories for this size, so the card's --trust-remote-code flag loads nothing from the repository. Provenance 15, the files come from the ornith-ai organisation Ornith's blog links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 10, MIT carries no policy, threshold or changeable terms, but the card's licence link is broken. Security programme 0, no security.txt, disclosure policy or security contact." }, "scores": { "a": 60, "b": 50 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Cohere", "b": "Ornith", "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": "MIT", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-07-29", "b": "2026-10-24", "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" } ], "faq": [ { "answer": "North Mini Code 1.0 and Ornith-1.5-122B score within a point of each other on agent readiness, 61 (C) and 61 (C).", "question": "Which is better for AI agents, North Mini Code 1.0 or Ornith-1.5-122B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 85 against 75", "Risk, 60 against 50" ], "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": null, "also": null, "goodFor": "Local coding and terminal agents on a 128 GB unified-memory machine or a 96 GB GPU setup, where a clear licence file and an answering publisher aren't required", "slug": "ornith-1-5-122b", "watchFor": "No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on" } ], "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-ornith-1-5-122b.json", "title": "Agents-A1-4B vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "Apertus v1.5 8B vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "Apodex-1.1-mini vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "Arete-27B vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "BigBang-v1 vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "Carbon-X vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "Cola2 vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "Cosmos-Reason2-2B vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "Darwin-36B-Opus vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "DeepSeek-V3.2 vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "DeepSeek V4.1 Flash vs Ornith-1.5-122B", "url": 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"https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "Gemma 4 12B IT vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "Gemma 4 26B A4B IT vs Ornith-1.5-122B", "url": 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"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-ornith-1-5-122b.json", "title": "Gemma 4 E4B IT vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "GLM-4.5-Air vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "GLM-4.7-Flash vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "GLM-5.3-Flash vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "GLM-5.3 vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "gpt-oss-120b vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "gpt-oss-20b vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "Granite 4.2 30B vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "Granite 4.2 3B vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "Granite 4.2 8B vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "Hy-Memory-30B-A3B vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "Hy3 vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "Inkling-Small vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "Inkling vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "Kimi K3.1 Pro vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "Kimi K3 vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-122b" }, { "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": 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"title": "Laguna M.1 vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "Laguna S 2.1 vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "Laguna XS 2.1 vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-122b" }, { "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": "https://www.anchorterminal.com/compare/lbs-27b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-ornith-1-5-122b.json", "title": "LBS-27B vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-north-mini-code-1-0.json", "title": "LFM2.5-2.6B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-122b.json", "title": "LFM2.5-2.6B vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-north-mini-code-1-0.json", "title": "LFM3-1.7B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ornith-1-5-122b.json", "title": "LFM3-1.7B vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-north-mini-code-1-0.json", "title": "LFM3-Audio-2B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-ornith-1-5-122b.json", "title": "LFM3-Audio-2B vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-north-mini-code-1-0.json", "title": "LFM3-Omni-2B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-ornith-1-5-122b.json", "title": "LFM3-Omni-2B vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "LFM3-VL-2B vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ornith-1-5-122b" }, { "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": 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"https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-122b.json", "title": "LongCat-2.5 vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-122b" }, { "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-distill-qwen-9b-vs-ornith-1-5-122b.json", "title": "MiMo-V2.6 Distill Qwen 9B vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-ornith-1-5-122b" }, { "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/mimo-v2-6-flash-rl-vs-ornith-1-5-122b.json", "title": "MiMo-V2.6-Flash-RL vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ornith-1-5-122b" }, { "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-o-4-5-vs-ornith-1-5-122b.json", "title": "MiniCPM-o 4.5 vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-122b" }, { "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": 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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" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-122b.json", "title": "occamy-1.0 vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-122b.json", "title": "Olmo 3 7B Think vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ornith-1-5-35b-a3b.json", "title": "Ornith-1.5-122B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ornith-1-5-397b.json", "title": "Ornith-1.5-122B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ornith-1-5-9b.json", "title": "Ornith-1.5-122B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-otel-2-0-llm-31b-it.json", "title": "Ornith-1.5-122B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen-agentworld-35b-a3b.json", "title": "Ornith-1.5-122B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Ornith-1.5-122B vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-4b-instruct-2507.json", "title": "Ornith-1.5-122B vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-0-8b.json", "title": "Ornith-1.5-122B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-122b-a10b.json", "title": "Ornith-1.5-122B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-2b.json", "title": "Ornith-1.5-122B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-397b-a17b.json", "title": "Ornith-1.5-122B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-4b.json", "title": "Ornith-1.5-122B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-9b.json", "title": "Ornith-1.5-122B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-6-27b.json", "title": "Ornith-1.5-122B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-6-35b-a3b.json", "title": "Ornith-1.5-122B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-8-27b.json", "title": "Ornith-1.5-122B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-8-flash-next.json", "title": "Ornith-1.5-122B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Ornith-1.5-122B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-coder-next.json", "title": "Ornith-1.5-122B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Ornith-1.5-122B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-2b-instruct.json", "title": "Ornith-1.5-122B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Ornith-1.5-122B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-32b-instruct.json", "title": "Ornith-1.5-122B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-4b-instruct.json", "title": "Ornith-1.5-122B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-8b-instruct.json", "title": "Ornith-1.5-122B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwopus3-8-27b-flash-v2.json", "title": "Ornith-1.5-122B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-recursive-flint.json", "title": "Ornith-1.5-122B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ripple-65b-preview.json", "title": "Ornith-1.5-122B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-sensenova-loop-9b-preview.json", "title": "Ornith-1.5-122B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-smollm3-3b.json", "title": "Ornith-1.5-122B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-spark-x2-6-4b.json", "title": "Ornith-1.5-122B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-step-3-7-flash.json", "title": "Ornith-1.5-122B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-swift-1-5-qwen3-8-27b.json", "title": "Ornith-1.5-122B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Ornith-1.5-122B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-thinkingcap-qwen3-8-27b.json", "title": "Ornith-1.5-122B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-unlimited-ocr-2.json", "title": "Ornith-1.5-122B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-2b.json", "title": "Ornith-1.5-122B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-4b.json", "title": "Ornith-1.5-122B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-zaya1-8b.json", "title": "Ornith-1.5-122B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 10, "edge": "north-mini-code-1-0", "key": "reach", "name": "Reach", "north-mini-code-1-0": 85, "ornith-1-5-122b": 75, "weight": 25 }, { "by": 10, "edge": "north-mini-code-1-0", "key": "risk", "name": "Risk", "north-mini-code-1-0": 60, "ornith-1-5-122b": 50, "weight": 20 }, { "by": 0, "edge": "", "key": "interface", "name": "Agent interface", "north-mini-code-1-0": 85, "ornith-1-5-122b": 85, "weight": 10 }, { "by": 0, "edge": "", "key": "upkeep", "name": "Documentation and upkeep", "north-mini-code-1-0": 36, "ornith-1-5-122b": 36, "weight": 10 } ], "summary": "North Mini Code 1.0 and Ornith-1.5-122B score within a point of each other on agent readiness, 61 (C) and 61 (C). 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.", "ornith-1-5-122b": "The largest Ornith that fits a 128 GB machine, a coding and terminal-agent cousin on Qwen3.5-122B-A10B's architecture with XML tool calls and 262,144 tokens of context. Its 4-bit builds need 84 to 93 GB. The main risk is an absent publisher, with no licence file, safety results or replies, and a card that misstates the hardware." } }, "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-ornith-1-5-122b", "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ornith-1-5-122b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ornith-1-5-122b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ornith-1-5-122b.min.md" }, "markdown": "North Mini Code 1.0 and Ornith-1.5-122B score within a point of each other on agent readiness, 61 (C) and 61 (C). 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- Ornith-1.5-122B: grade C, 61/100, rank #81 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-122b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-122b.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 75\n- Risk, 60 against 50\n\nWatch for: No limitations, safety results, languages or data description on the card\n\n### Ornith-1.5-122B (C)\n\nGood for: Local coding and terminal agents on a 128 GB unified-memory machine or a 96 GB GPU setup, where a clear licence file and an answering publisher aren't required\n\nWatch for: No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on\n\n\n## Why the edge\n\n### Reach: North Mini Code 1.0 +10\n\n- North Mini Code 1.0 (85): Memory 25, the smallest build from Cohere is the w4a16 repository, 19.38 GB, and 19.38 GB × 1.2 = 23.3 GB, within 24 GB. Unsloth's GGUF isn't read for this line. Runtimes 20, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the cohere2_moe model, and llama.cpp b12242 has the cohere2moe architecture. Builds 15, Quantised builds from Cohere at two precisions, FP8 and 4-bit weights (w4a16), each in its own repository. Terms 15, Apache 2.0 in the metadata and on the card, an OSI licence with nothing added. Download 10, the Hub API reports gated false for the model and its builds on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 25 + 20 + 15 + 15 + 10 + 0 = 85.\n- Ornith-1.5-122B (75): Memory 10, since Ornith's own MLX 4-bit build is 69.7 GB and needs about 83.6 GB with the fifth added, over 64 GB and inside the reference machine's 107.4 GB, as do its Q4_K_M GGUF, its NVFP4 build and Ollama's build. Runtimes 25, the most the line gives, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2, llama.cpp's qwen35moe architecture, Ollama's library and LM Studio's catalogue for the qwen35moe architecture. Builds 15, Ornith publishes FP8, NVFP4, GGUF and MLX at several precisions. Terms 15, MIT as named in the card's metadata, with nothing added, read by its terms though the repository has no licence file. Download 10, nothing is gated. Hosted 0, Ornith runs no API and no inference provider serves it. 10 + 25 + 15 + 15 + 10 + 0 = 75.\n\n### Risk: North Mini Code 1.0 +10\n\n- North Mini Code 1.0 (60): File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in Cohere's verified CohereLabs organisation, which cohere.com links to per our other Cohere listings (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 5, Cohere's trust centre holds a responsible disclosure policy with a bug bounty, as recorded on our Cohere listings (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.\n- Ornith-1.5-122B (50): File safety 25, safetensors and GGUF only and no Python in any of Ornith's repositories for this size, so the card's --trust-remote-code flag loads nothing from the repository. Provenance 15, the files come from the ornith-ai organisation Ornith's blog links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 10, MIT carries no policy, threshold or changeable terms, but the card's licence link is broken. Security programme 0, no security.txt, disclosure policy or security contact.\n\n## Score by category\n\n| Category | Weight | North Mini Code 1.0 | Ornith-1.5-122B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 75 | North Mini Code 1.0 +10 |\n| Risk | 20% (20 this run) | 60 | 50 | North Mini Code 1.0 +10 |\n| Agent interface | 10% (10 this run) | 85 | 85 | even |\n| Documentation and upkeep | 10% (10 this run) | 36 | 36 | even |\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 | Ornith-1.5-122B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Cohere | Ornith |\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 | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-29 | 2026-10-24 |\n| Terms last updated | no document linked | no document linked |\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**Ornith-1.5-122B.** The largest Ornith that fits a 128 GB machine, a coding and terminal-agent cousin on Qwen3.5-122B-A10B's architecture with XML tool calls and 262,144 tokens of context. Its 4-bit builds need 84 to 93 GB. The main risk is an absent publisher, with no licence file, safety results or replies, and a card that misstates the hardware.\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### Ornith-1.5-122B\n\n1. Start it with `ollama run ornith-1.5:122b-q4_K_M`, or `llama-server -hf ornith-ai/Ornith-1.5-122B-GGUF:Q4_K_M --jinja -c 262144`\n2. Budget about 92 GB for the Q4_K_M GGUF with a working context, and less context if the machine has under 100 GB for the model\n3. In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed\n4. Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n5. Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it\n6. Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 for general tasks, as the card recommends\n7. Add mmproj-Ornith-1.5-122B-BF16.gguf (0.9 GB) for image input in llama.cpp, and validate JSON output yourself, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, North Mini Code 1.0 or Ornith-1.5-122B?\n\nNorth Mini Code 1.0 and Ornith-1.5-122B score within a point of each other on agent readiness, 61 (C) and 61 (C).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ornith-1-5-122b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ornith-1-5-122b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"north-mini-code-1-0\", \"b\": \"ornith-1-5-122b\"}`. From a terminal: `anchor compare north-mini-code-1-0 ornith-1-5-122b`\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/ornith-1-5-122b.json\n\n## Other comparisons with North Mini Code 1.0 or Ornith-1.5-122B\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 Ornith-1.5-122B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-122b.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 Ornith-1.5-122B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-122b.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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Unlimited-OCR-2](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-unlimited-ocr-2.md)\n- [North Mini Code 1.0 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-wevisone-vl-2b.md)\n- [North Mini Code 1.0 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-wevisone-vl-4b.md)\n- [North Mini Code 1.0 vs ZAYA1-8B](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-zaya1-8b.md)\n- [occamy-1.0 vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-122b.md)\n- [Olmo 3 7B Think vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-122b.md)\n- [Ornith-1.5-122B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ornith-1-5-35b-a3b.md)\n- [Ornith-1.5-122B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ornith-1-5-397b.md)\n- [Ornith-1.5-122B vs 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Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Ornith-1.5-122B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-thinkingcap-qwen3-8-27b.md)\n- [Ornith-1.5-122B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-unlimited-ocr-2.md)\n- [Ornith-1.5-122B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-2b.md)\n- [Ornith-1.5-122B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-4b.md)\n- [Ornith-1.5-122B vs ZAYA1-8B](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-zaya1-8b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": 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