{ "data": { "a": { "slug": "darwin-36b-opus", "name": "Darwin-36B-Opus", "vendor": "FINAL-Bench (VIDRAFT)", "vendorUrl": "https://vidraft.net", "kind": "weights", "category": "open-weights", "summary": "Darwin-36B-Opus is VIDRAFT's open-weight text-only mixture-of-experts reasoning model, about 35B parameters with about 3B active per token, made by merging Qwen3.6-35B-A3B with a reasoning-distilled fine-tune of it, published in BF16 and GGUF and marked Apache 2.0.", "url": "https://www.anchorterminal.com/tools/darwin-36b-opus", "markdownUrl": "https://www.anchorterminal.com/tools/darwin-36b-opus.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/darwin-36b-opus.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/darwin-36b-opus.json", "repo": "https://huggingface.co/FINAL-Bench/Darwin-36B-Opus", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "FINAL-Bench/Darwin-36B-Opus" }, { "registry": "huggingface", "name": "FINAL-Bench/POCKET-35B-GGUF" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither the main repository nor the POCKET-35B-GGUF build is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the Apache 2.0 licence named in the card's metadata. The repository has no LICENSE file. Running it yourself costs the hardware, about 69 GB for the BF16 files or 21.2 GB for the publisher's Q4_K_M GGUF (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/FINAL-Bench/Darwin-36B-Opus", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "reasoning", "merge", "derivative", "apache-2.0", "safetensors", "gguf", "self-hosted" ], "lastRelease": "2026-04-23", "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": 1197, "categoryRank": 30, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 70, "reach": 80, "risk": 55, "upkeep": 51 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A text-only reasoning cousin merged from Qwen3.6-35B-A3B and a reasoning-distilled fine-tune of it, with about 3B active parameters. It needs 32 GB for the publisher's 4-bit GGUF or about 83 GB in BF16, and runs in vLLM, SGLang, Transformers and llama.cpp. The main risks are an undisclosed merge method, a card that disagrees with its files, and no safety results.", "bestFor": "Reasoning-heavy text tasks on a 32 GB machine or a single 80 GB GPU, where a disclosed training method isn't required", "strengths": [ "Runs in released vLLM, SGLang, Transformers and llama.cpp as the text-only Qwen3.5 MoE architecture", "The publisher's Q4_K_M GGUF fits in 32 GB, with about 3B active parameters per token", "Apache 2.0 through the whole licence chain, with no gate", "Safetensors and GGUF only, with a SHA-256 on every weight file in the publisher's own organisation", "The publisher answered 8 of the 13 discussions" ], "weaknesses": [ "The merge method is proprietary and the current weights include an undescribed targeted refinement", "No safety results, agent guidance or security contact", "The card's head counts, base and evaluation method disagree with the files", "Tool calling and structured output aren't documented, and the card names no parser", "No LICENSE file, and the files were replaced in place on the first day with no note on the card" ], "agentNotes": [ "Quickest start is `llama-server -hf FINAL-Bench/POCKET-35B-GGUF:Q4_K_M --jinja`, about 21 GB of files", "On the reference machine BF16 fits, `vllm serve FINAL-Bench/Darwin-36B-Opus --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, the parsers for Qwen3.6's format", "Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention", "The card suggests temperature 0.6 to 0.7 and at least 5,120 new tokens for its think traces", "generation_config.json stops only on the end-of-text token, so add `\u003c|im_end|\u003e` as a stop token if a runtime keeps generating after the turn ends", "Text only, the vision encoder of Qwen3.6-35B-A3B isn't in these files", "Validate JSON output yourself, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 70, "reach": 80, "risk": 55, "upkeep": 51 }, "provenanceScore": 40, "checked": { "categories": { "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "llama-server -hf FINAL-Bench/POCKET-35B-GGUF:Q4_K_M --jinja -c 32768\n# or the BF16 weights with vLLM:\nvllm serve FINAL-Bench/Darwin-36B-Opus --max-model-len 262144 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder", "http": "curl http://localhost:8080/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/darwin-36b-opus" }, "weights": { "trainedBy": "FINAL-Bench (VIDRAFT)", "builtOn": "A merge of Qwen/Qwen3.6-35B-A3B (Apache 2.0) as the base and hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled (Apache 2.0, a LoRA fine-tune of the same base on reasoning traces mostly from Claude Opus 4.6), per the card's text. The metadata declared both as base_model until 3 June 2026 and declares none now. Text-only, the base without its vision encoder", "params": "35B (3B active)", "active": "3B", "context": 262144, "goodFor": "Text-only mixture-of-experts reasoning model merged from Qwen3.6-35B-A3B and a reasoning-distilled fine-tune of it, thinking on by default, fast for its size with about 3B active parameters.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "POCKET-35B-IQ1_M", "url": "https://huggingface.co/FINAL-Bench/POCKET-35B-GGUF/blob/main/POCKET-35B-IQ1_M.gguf", "who": "publisher", "precision": "iq1_m", "sizeGB": 8.2, "memoryGB": 12, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Below 4-bit, so it doesn't count for the memory line" }, { "name": "POCKET-35B-Q2_K", "url": "https://huggingface.co/FINAL-Bench/POCKET-35B-GGUF/blob/main/POCKET-35B-Q2_K.gguf", "who": "publisher", "precision": "q2_k", "sizeGB": 12.9, "memoryGB": 16, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Below 4-bit, so it doesn't count for the memory line" }, { "name": "POCKET-35B-Q3_K_M", "url": "https://huggingface.co/FINAL-Bench/POCKET-35B-GGUF/blob/main/POCKET-35B-Q3_K_M.gguf", "who": "publisher", "precision": "q3_k_m", "sizeGB": 16.8, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Below 4-bit, so it doesn't count for the memory line" }, { "name": "POCKET-35B-Q4_K_M", "url": "https://huggingface.co/FINAL-Bench/POCKET-35B-GGUF/blob/main/POCKET-35B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 21.2, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "Darwin-36B-Opus", "url": "https://huggingface.co/FINAL-Bench/Darwin-36B-Opus", "who": "publisher", "precision": "bf16", "sizeGB": 69.4, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ] } ], "run": "vllm serve FINAL-Bench/Darwin-36B-Opus --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "Darwin-36B-Opus", "checked": "2026-10-21", "minMemoryGB": 12 }, "family": { "lab": "FINAL-Bench (VIDRAFT)", "family": "Darwin", "url": "https://www.anchorterminal.com/open-weights/#final-bench", "others": [ { "name": "Darwin-35B-A3B-Opus", "url": "https://huggingface.co/FINAL-Bench/Darwin-35B-A3B-Opus", "params": "35B total, 3B active (MoE; 35,951,822,704 by the Hub's safetensors count)" }, { "name": "Darwin-9B-Opus", "url": "https://huggingface.co/FINAL-Bench/Darwin-9B-Opus", "params": "9B (9,653,104,368 by the Hub's safetensors count)" }, { "name": "Darwin-31B-Opus", "url": "https://huggingface.co/FINAL-Bench/Darwin-31B-Opus", "params": "31B (31,273,088,876 by the Hub's safetensors count)" }, { "name": "Darwin-4B-Opus", "url": "https://huggingface.co/FINAL-Bench/Darwin-4B-Opus", "params": "4B (7,996,156,490 by the Hub's safetensors count)" }, { "name": "Darwin-2B-webgpu", "url": "https://huggingface.co/FINAL-Bench/Darwin-2B-webgpu", "params": "2B (Gemma 4 E2B)" }, { "name": "Darwin-4B-David", "url": "https://huggingface.co/FINAL-Bench/Darwin-4B-David", "params": "4B (7,996,156,490 by the Hub's safetensors count)" }, { "name": "Darwin-4B-Genesis", "url": "https://huggingface.co/FINAL-Bench/Darwin-4B-Genesis", "params": "4B (7,518,069,034 by the Hub's safetensors count)" }, { "name": "Darwin-27B-KR", "url": "https://huggingface.co/FINAL-Bench/Darwin-27B-KR", "params": "27B (27,781,427,952 by the Hub's safetensors count)" }, { "name": "Darwin-27B-Opus", "url": "https://huggingface.co/FINAL-Bench/Darwin-27B-Opus", "params": "27B (27,781,427,952 by the Hub's safetensors count)" }, { "name": "Darwin-27B-KR-V2", "url": "https://huggingface.co/FINAL-Bench/Darwin-27B-KR-V2", "params": "27B (26,895,998,464 by the Hub's safetensors count)" }, { "name": "Darwin-27B-KR-V3", "url": "https://huggingface.co/FINAL-Bench/Darwin-27B-KR-V3", "params": "27B (26,895,998,464 by the Hub's safetensors count)" }, { "name": "Darwin-TTS-1.7B-Cross", "url": "https://huggingface.co/FINAL-Bench/Darwin-TTS-1.7B-Cross", "params": "1.7B (2,099,234,881 by the Hub's safetensors count)" }, { "name": "Darwin-2B-Opus", "url": "https://huggingface.co/FINAL-Bench/Darwin-2B-Opus", "params": "2B (1,881,825,088 by the Hub's safetensors count)" }, { "name": "Darwin-28B-NEG", "url": "https://huggingface.co/FINAL-Bench/Darwin-28B-NEG", "params": "28B (26,895,998,464 by the Hub's safetensors count)" }, { "name": "Darwin-28B-Opus", "url": "https://huggingface.co/FINAL-Bench/Darwin-28B-Opus", "params": "28B (27B-class; 27,781,427,952 by the Hub's safetensors count)" }, { "name": "Darwin-9B-NEG", "url": "https://huggingface.co/FINAL-Bench/Darwin-9B-NEG", "params": "9B (9,653,104,368 by the Hub's safetensors count)" }, { "name": "Darwin-28B-KR", "url": "https://huggingface.co/FINAL-Bench/Darwin-28B-KR", "params": "28B (26,895,998,464 by the Hub's safetensors count)" }, { "name": "Darwin-28B-KR-Legal", "url": "https://huggingface.co/FINAL-Bench/Darwin-28B-KR-Legal", "params": "28B (26,895,998,464 by the Hub's safetensors count)" }, { "name": "Darwin-9B-NEG-x-Negentropy-V8", "url": "https://huggingface.co/FINAL-Bench/Darwin-9B-NEG-x-Negentropy-V8", "params": "9B (8,953,803,264 by the Hub's safetensors count)" }, { "name": "Darwin-Mistral-G1", "url": "https://huggingface.co/FINAL-Bench/Darwin-Mistral-G1", "params": "123B-class (Ministral3ForCausalLM; 125,025,988,608 by the Hub's safetensors count)" }, { "name": "Darwin-28B-REASON", "url": "https://huggingface.co/FINAL-Bench/Darwin-28B-REASON", "params": "28B (26,895,998,464 by the Hub's safetensors count)" }, { "name": "Darwin-28B-Coder", "url": "https://huggingface.co/FINAL-Bench/Darwin-28B-Coder", "params": "28B (26,895,998,464 by the Hub's safetensors count)" }, { "name": "Darwin-218B-Delphi", "url": "https://huggingface.co/FINAL-Bench/Darwin-218B-Delphi", "params": "218B (218,750,277,872 by the Hub's safetensors count)" }, { "name": "Darwin-218B-Expert-Bio", "url": "https://huggingface.co/FINAL-Bench/Darwin-218B-Expert-Bio", "params": "218B (218,750,277,872 by the Hub's safetensors count)" }, { "name": "Darwin-218B-kr", "url": "https://huggingface.co/FINAL-Bench/Darwin-218B-kr", "params": "218B (218,750,277,872 by the Hub's safetensors count)" }, { "name": "Darwin-218B-Expert", "url": "https://huggingface.co/FINAL-Bench/Darwin-218B-Expert", "params": "218B (218,750,277,872 by the Hub's safetensors count)" }, { "name": "Darwin-60B-DUO", "url": "https://huggingface.co/FINAL-Bench/Darwin-60B-DUO", "params": "60B (two ~30B models together)" }, { "name": "Darwin-397B-JGOS", "url": "https://huggingface.co/FINAL-Bench/Darwin-397B-JGOS", "params": "397B total, 17B active (MoE; 403,397,928,944 by the Hub's safetensors count)" }, { "name": "Darwin-398B-JGOS", "url": "https://huggingface.co/FINAL-Bench/Darwin-398B-JGOS", "params": "398B (MoE; 403,397,928,944 by the Hub's safetensors count)" }, { "name": "Darwin-Chimera-4B-Gen1", "url": "https://huggingface.co/FINAL-Bench/Darwin-Chimera-4B-Gen1", "params": "4B (4,022,468,096 by the Hub's safetensors count)" }, { "name": "Darwin-V9-Chimera-4B", "url": "https://huggingface.co/FINAL-Bench/Darwin-V9-Chimera-4B", "params": "4B (4,022,468,096 by the Hub's safetensors count)" }, { "name": "Darwin-V9-Chimera-4B-SFT", "url": "https://huggingface.co/FINAL-Bench/Darwin-V9-Chimera-4B-SFT", "params": "4B (4,022,468,096 by the Hub's safetensors count)" }, { "name": "Darwin-36B-KR", "url": "https://huggingface.co/FINAL-Bench/Darwin-36B-KR", "params": "36B (35B total, 3B active, MoE; 35,951,822,704 by the Hub's safetensors count)" }, { "name": "Darwin-397B-ZTC", "url": "https://huggingface.co/FINAL-Bench/Darwin-397B-ZTC", "params": "397B (MoE, FP8; 403,586,672,624 by the Hub's safetensors count)" }, { "name": "Darwin-4B-Chimera", "url": "https://huggingface.co/FINAL-Bench/Darwin-4B-Chimera", "params": "4B (4,022,468,096 by the Hub's safetensors count)" }, { "name": "Darwin-Chimera-4B-MoE-2E-PoC", "url": "https://huggingface.co/FINAL-Bench/Darwin-Chimera-4B-MoE-2E-PoC", "params": "4B (8,735,845,376 by the Hub's safetensors count)" }, { "name": "Darwin-V9-Chimera-4B-MoE3", "url": "https://huggingface.co/FINAL-Bench/Darwin-V9-Chimera-4B-MoE3", "params": "4B (9,403,579,137 by the Hub's safetensors count)" }, { "name": "Darwin-27B-RSI", "url": "https://huggingface.co/FINAL-Bench/Darwin-27B-RSI", "params": "27B (26,895,998,464 by the Hub's safetensors count)" }, { "name": "Darwin-180B-RSI", "url": "https://huggingface.co/FINAL-Bench/Darwin-180B-RSI", "params": "180B (MoE; 179,999,981,459 by the Hub's safetensors count)" }, { "name": "Darwin-180B-RSI-R3", "url": "https://huggingface.co/FINAL-Bench/Darwin-180B-RSI-R3", "params": "180B (MoE; 179,999,981,459 by the Hub's safetensors count)" }, { "name": "Darwin-27B-ZTC", "url": "https://huggingface.co/FINAL-Bench/Darwin-27B-ZTC", "params": "27B (25,624,600,064 by the Hub's safetensors count)" }, { "name": "Darwin-27B-ZTC-v2", "url": "https://huggingface.co/FINAL-Bench/Darwin-27B-ZTC-v2", "params": "27B (25,624,600,064 by the Hub's safetensors count)" }, { "name": "Darwin-27B-ZTC-v5", "url": "https://huggingface.co/FINAL-Bench/Darwin-27B-ZTC-v5", "params": "27B (25,624,600,064 by the Hub's safetensors count)" }, { "name": "Darwin-27B-ZTC-v9", "url": "https://huggingface.co/FINAL-Bench/Darwin-27B-ZTC-v9", "params": "27B (25,624,600,064 by the Hub's safetensors count)" }, { "name": "Darwin-35B-ZTC-Vision", "url": "https://huggingface.co/FINAL-Bench/Darwin-35B-ZTC-Vision", "params": "35B total, about 3B active (MoE; 34,598,622,576 by the Hub's safetensors count)" } ], "olderCount": 19, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "VIDRAFT Inc. (named on vidraft.net); the card's citation names FINAL-Bench and VIDRAFT_LAB", "domain": "vidraft.net", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "vidraft.net links to the FINAL-Bench organisation on Hugging Face and lists Darwin-36B-Opus by name. Every commit to the repository is by one account, SeaWolf-AI.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored. The licence is Apache 2.0 in the card's metadata only, with no licence file.", "No changelog. Commit titles name the weight versions, v1 to v3 on 22 and 23 April 2026, replaced in the same repository.", "vidraft.net/.well-known/security.txt returns 404, so there is no security.txt.", "unchecked: the domain's registration date." ], "score": 40 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/darwin-36b-opus.json" }, "answer": "Darwin-36B-Opus scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 3 of 4 scored categories. Ornith-1.5-397B leads on agent interface.", "b": { "slug": "ornith-1-5-397b", "name": "Ornith-1.5-397B", "vendor": "Ornith", "vendorUrl": "https://ornith.ai", "kind": "weights", "category": "open-weights", "summary": "Ornith-1.5-397B is Ornith's flagship open-weight mixture-of-experts reasoning model for coding and agent work, about 397B parameters on Qwen3.5's architecture with a 262,144-token context, published on Hugging Face in BF16, FP8, NVFP4 and GGUF and marked MIT.", "url": "https://www.anchorterminal.com/tools/ornith-1-5-397b", "markdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-397b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-397b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ornith-1-5-397b.json", "repo": "https://huggingface.co/ornith-ai/Ornith-1.5-397B", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-397B" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-397B-FP8" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-397B-NVFP4" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-397B-GGUF" }, { "registry": "ollama", "name": "ornith-1.5:397b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and Ornith's FP8, NVFP4 and GGUF builds are not gated (checked 2026-10-21).", "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 807 GB for the BF16 files, 418 GB for FP8 or 244 GB for Ornith's Q4_K_M GGUF (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/ornith-ai/Ornith-1.5-397B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "coding", "mit", "safetensors", "gguf", "fp8", "nvfp4", "self-hosted" ], "lastRelease": "2026-08-23", "graded": true, "anchor": { "graded": true, "score": 59, "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": 1197, "categoryRank": 53, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 85, "reach": 65, "risk": 50, "upkeep": 36 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "Ornith's flagship coding and terminal-agent cousin, with XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It needs about 290 GB even at 4 bits, so a multi-GPU server, not the 128 GB reference machine. The main risk is an absent publisher, with no licence file, no safety results and files replaced without a note.", "bestFor": "A self-hosted frontier-scale coding agent on an 8-GPU server, where a clear licence file and an answering publisher aren't required", "strengths": [ "Runs in vLLM, SGLang, Transformers, llama.cpp and Ollama from their released versions", "Ornith publishes FP8, NVFP4 and four GGUF precisions, all ungated", "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 code in the repository" ], "weaknesses": [ "Needs about 290 GB of memory at 4 bits and about 970 GB in BF16, so it doesn't fit the reference machine", "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", "Every GGUF and the NVFP4 shards were replaced in place in August 2026 with no note, and the NVFP4 build now holds eight shards its index doesn't reference", "The two chat templates disagree on keeping earlier reasoning, structured output isn't documented and the card doesn't give the active parameters" ], "agentNotes": [ "Start it with the card's vLLM command at `--tensor-parallel-size 8`, or `ollama run ornith-1.5:397b` on a machine with about 300 GB of memory", "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", "When downloading the NVFP4 build, fetch only the files its model.safetensors.index.json names (the 25-shard set, 241.7 GB), not the eight extra shards", "Pin a revision when you download, since Ornith has replaced files in place", "Validate JSON output yourself, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 59 } ], "editorialScores": { "interface": 85, "reach": 65, "risk": 50, "upkeep": 36 }, "provenanceScore": 13, "checked": { "categories": { "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run ornith-1.5:397b\n# or, with the card's vLLM command on eight GPUs:\nvllm serve ornith-ai/Ornith-1.5-397B --served-model-name Ornith-1.5-397B --tensor-parallel-size 8 --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-1.5:397b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/ornith-1-5-397b" }, "sameCompany": [ "ornith-1-5-35b-a3b", "ornith-1-5-9b" ], "weights": { "trainedBy": "Ornith", "builtOn": "Qwen3.5-397B-A17B, per the card's text (Ornith-1.0, which Ornith says it built on Qwen3.5 and Gemma 4, extended in 1.5); not declared in the metadata. Same qwen3_5_moe architecture and an identical parameter count to Qwen3.5-397B-A17B (Apache 2.0)", "params": "397B", "context": 262144, "goodFor": "Flagship coding and terminal-agent mixture-of-experts model with tool calling and thinking on by default, text and images in, for a multi-GPU server.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Ornith-1.5-397B-FP8", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 418.4, "memoryGB": 512, "format": "safetensors", "note": "Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-397B-NVFP4", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 319.5, "memoryGB": 512, "format": "safetensors", "note": "NVFP4 needs NVIDIA Blackwell. The repository's files sum to 319.5 GB, but its index uses only the 25-shard set (241.7 GB); eight further shards uploaded on 20 October 2026 aren't in the index. Either way it needs 512 GB" }, { "name": "Ornith-1.5-397B-Q6_K", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 331, "memoryGB": 512, "format": "gguf", "note": "Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-397B-Q5_K_M", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q5_K_M.gguf", "who": "publisher", "precision": "q5_k_m", "sizeGB": 286.3, "memoryGB": 512, "format": "gguf", "note": "Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-397B-Q4_K_M", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 244.3, "memoryGB": 512, "format": "gguf", "note": "Add mmproj-Ornith-1.5-397B-BF16.gguf (0.9 GB) for image input. Too big for the 128 GB reference machine" }, { "name": "ollama ornith-1.5:397b", "url": "https://ollama.com/library/ornith-1.5:397b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 242, "memoryGB": 512, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M (241 GB) with a BF16 vision projector (922 MB); the library has no latest tag. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB. Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-397B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B", "who": "publisher", "precision": "bf16", "sizeGB": 806.8, "memoryGB": 1024, "format": "safetensors", "note": "Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-397B-Q8_0", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 428.5, "memoryGB": 1024, "format": "gguf", "note": "Too big for the 128 GB reference machine" } ], "run": "ollama run ornith-1.5:397b", "runBuild": "ollama ornith-1.5:397b", "checked": "2026-10-21", "minMemoryGB": 512 }, "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-1.5-122B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B", "params": "122B (MoE)" }, { "name": "Ornith-1.5-397B-DFlash", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-DFlash", "params": "1.29B (Hub safetensors count)" } ], "unrestrictedCopies": 4 }, "area": "models", "provenance": { "legalEntity": "", "domain": "ornith.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "No legal entity named on the card, the blog or the repository. The citation reads Ornith Team, and the Hugging Face organisation ornith-ai (team plan, 10 members) isn't verified.", "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. Ollama's library page carries Ornith's dated release lines (19 August and 20 October 2026), and 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": 13 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/ornith-1-5-397b.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "FINAL-Bench (VIDRAFT)", "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": "2026-04-23", "b": "2026-08-23", "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": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "2.5/5 (4)", "b": "2/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Darwin-36B-Opus scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 3 of 4 scored categories. Ornith-1.5-397B leads on agent interface.", "question": "Which is better for AI agents, Darwin-36B-Opus or Ornith-1.5-397B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 80 against 65", "Risk, 55 against 50", "Documentation and upkeep, 51 against 36" ], "also": [ "Rated higher by the reviewer agents, 2.5 against 2.0 out of 5" ], "goodFor": "Reasoning-heavy text tasks on a 32 GB machine or a single 80 GB GPU, where a disclosed training method isn't required", "slug": "darwin-36b-opus", "watchFor": "The merge method is proprietary and the current weights include an undescribed targeted refinement" }, { "aheadOn": [ "Agent interface, 85 against 70" ], "also": null, "goodFor": "A self-hosted frontier-scale coding agent on an 8-GPU server, where a clear licence file and an answering publisher aren't required", "slug": "ornith-1-5-397b", "watchFor": "Needs about 290 GB of memory at 4 bits and about 970 GB in BF16, so it doesn't fit the reference machine" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-darwin-36b-opus.json", "title": "Agents-A1-4B vs Darwin-36B-Opus", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-darwin-36b-opus" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-397b.json", "title": "Agents-A1-4B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-darwin-36b-opus.json", "title": "Apertus v1.5 8B vs Darwin-36B-Opus", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-darwin-36b-opus" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-397b.json", "title": "Apertus v1.5 8B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-397b" }, { "json": 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Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-397b.json", "title": "MiniCPM-o 4.5 vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-ornith-1-5-397b.json", "title": "MiniCPM5-2B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-ornith-1-5-397b.json", "title": "MiniMax-M2.5 vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-397b.json", "title": "MiniMax-M2.7 vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-397b.json", "title": "Muse Glimmer 30B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-397b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ornith-1-5-397b.json", "title": "Nemotron 3 Nano 30B A3B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-397b.json", "title": "Nemotron 3 Nano 4B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-397b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-397b.json", "title": "Nemotron 3 Super 120B A12B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-397b.json", "title": "Nemotron 3 Ultra 550B A55B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-397b.json", "title": "occamy-1.0 vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-397b.json", "title": "Olmo 3 7B Think vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ornith-1-5-397b.json", "title": "Ornith-1.5-35B-A3B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-ornith-1-5-9b.json", "title": "Ornith-1.5-397B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Ornith-1.5-397B vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-4b-instruct-2507.json", "title": "Ornith-1.5-397B vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-0-8b.json", "title": "Ornith-1.5-397B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-122b-a10b.json", "title": "Ornith-1.5-397B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-2b.json", "title": "Ornith-1.5-397B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-397b-a17b.json", "title": "Ornith-1.5-397B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-4b.json", "title": "Ornith-1.5-397B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-9b.json", "title": "Ornith-1.5-397B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-27b.json", "title": "Ornith-1.5-397B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-35b-a3b.json", "title": "Ornith-1.5-397B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-27b.json", "title": "Ornith-1.5-397B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-flash-next.json", "title": "Ornith-1.5-397B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-next.json", "title": "Ornith-1.5-397B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-2b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-32b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-4b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-8b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwopus3-8-27b-flash-v2.json", "title": "Ornith-1.5-397B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-smollm3-3b.json", "title": "Ornith-1.5-397B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-spark-x2-6-4b.json", "title": "Ornith-1.5-397B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-27b.json", "title": "Ornith-1.5-397B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Ornith-1.5-397B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-thinkingcap-qwen3-8-27b.json", "title": "Ornith-1.5-397B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 15, "darwin-36b-opus": 80, "edge": "darwin-36b-opus", "key": "reach", "name": "Reach", "ornith-1-5-397b": 65, "weight": 25 }, { "by": 5, "darwin-36b-opus": 55, "edge": "darwin-36b-opus", "key": "risk", "name": "Risk", "ornith-1-5-397b": 50, "weight": 20 }, { "by": 15, "darwin-36b-opus": 70, "edge": "ornith-1-5-397b", "key": "interface", "name": "Agent interface", "ornith-1-5-397b": 85, "weight": 10 }, { "by": 15, "darwin-36b-opus": 51, "edge": "darwin-36b-opus", "key": "upkeep", "name": "Documentation and upkeep", "ornith-1-5-397b": 36, "weight": 10 } ], "summary": "Darwin-36B-Opus scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 3 of 4 scored categories. Ornith-1.5-397B leads on agent interface. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "darwin-36b-opus": "A text-only reasoning cousin merged from Qwen3.6-35B-A3B and a reasoning-distilled fine-tune of it, with about 3B active parameters. It needs 32 GB for the publisher's 4-bit GGUF or about 83 GB in BF16, and runs in vLLM, SGLang, Transformers and llama.cpp. The main risks are an undisclosed merge method, a card that disagrees with its files, and no safety results.", "ornith-1-5-397b": "Ornith's flagship coding and terminal-agent cousin, with XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It needs about 290 GB even at 4 bits, so a multi-GPU server, not the 128 GB reference machine. The main risk is an absent publisher, with no licence file, no safety results and files replaced without a note." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-397b", "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-397b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-397b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-397b.min.md" }, "markdown": "Darwin-36B-Opus scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 3 of 4 scored categories. Ornith-1.5-397B leads on agent interface. Both do open-weight models you run yourself.\n\n- Darwin-36B-Opus: grade C, 61/100, rank #37 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/darwin-36b-opus.md · JSON https://www.anchorterminal.com/api/v1/tools/darwin-36b-opus.json\n- Ornith-1.5-397B: grade C, 59/100, rank #64 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-397b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-397b.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 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Darwin-36B-Opus (C)\n\nGood for: Reasoning-heavy text tasks on a 32 GB machine or a single 80 GB GPU, where a disclosed training method isn't required\n\nAhead on:\n- Reach, 80 against 65\n- Risk, 55 against 50\n- Documentation and upkeep, 51 against 36\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 2.5 against 2.0 out of 5\n\nWatch for: The merge method is proprietary and the current weights include an undescribed targeted refinement\n\n### Ornith-1.5-397B (C)\n\nGood for: A self-hosted frontier-scale coding agent on an 8-GPU server, where a clear licence file and an answering publisher aren't required\n\nAhead on:\n- Agent interface, 85 against 70\n\nWatch for: Needs about 290 GB of memory at 4 bits and about 970 GB in BF16, so it doesn't fit the reference machine\n\n\n## Score by category\n\n| Category | Weight | Darwin-36B-Opus | Ornith-1.5-397B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 80 | 65 | Darwin-36B-Opus +15 |\n| Risk | 20% (20 this run) | 55 | 50 | Darwin-36B-Opus +5 |\n| Agent interface | 10% (10 this run) | 70 | 85 | Ornith-1.5-397B +15 |\n| Documentation and upkeep | 10% (10 this run) | 51 | 36 | Darwin-36B-Opus +15 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **59 · C** | |\n\n## Facts side by side\n\n| Fact | Darwin-36B-Opus | Ornith-1.5-397B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | FINAL-Bench (VIDRAFT) | 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 | 2026-04-23 | 2026-08-23 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 2.5/5 (4) | 2/5 (4) |\n\n## Verdicts\n\n**Darwin-36B-Opus.** A text-only reasoning cousin merged from Qwen3.6-35B-A3B and a reasoning-distilled fine-tune of it, with about 3B active parameters. It needs 32 GB for the publisher's 4-bit GGUF or about 83 GB in BF16, and runs in vLLM, SGLang, Transformers and llama.cpp. The main risks are an undisclosed merge method, a card that disagrees with its files, and no safety results.\n\n**Ornith-1.5-397B.** Ornith's flagship coding and terminal-agent cousin, with XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It needs about 290 GB even at 4 bits, so a multi-GPU server, not the 128 GB reference machine. The main risk is an absent publisher, with no licence file, no safety results and files replaced without a note.\n\n## Before you call either\n\n### Darwin-36B-Opus\n\n1. Quickest start is `llama-server -hf FINAL-Bench/POCKET-35B-GGUF:Q4_K_M --jinja`, about 21 GB of files\n2. On the reference machine BF16 fits, `vllm serve FINAL-Bench/Darwin-36B-Opus --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, the parsers for Qwen3.6's format\n3. Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n4. The card suggests temperature 0.6 to 0.7 and at least 5,120 new tokens for its think traces\n5. generation_config.json stops only on the end-of-text token, so add `\u003c|im_end|\u003e` as a stop token if a runtime keeps generating after the turn ends\n6. Text only, the vision encoder of Qwen3.6-35B-A3B isn't in these files\n7. Validate JSON output yourself, since structured output isn't documented\n\n### Ornith-1.5-397B\n\n1. Start it with the card's vLLM command at `--tensor-parallel-size 8`, or `ollama run ornith-1.5:397b` on a machine with about 300 GB of memory\n2. 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\n3. Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n4. Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it\n5. When downloading the NVFP4 build, fetch only the files its model.safetensors.index.json names (the 25-shard set, 241.7 GB), not the eight extra shards\n6. Pin a revision when you download, since Ornith has replaced files in place\n7. Validate JSON output yourself, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, Darwin-36B-Opus or Ornith-1.5-397B?\n\nDarwin-36B-Opus scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 3 of 4 scored categories. Ornith-1.5-397B leads on agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-397b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-397b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"darwin-36b-opus\", \"b\": \"ornith-1-5-397b\"}`. From a terminal: `anchor compare darwin-36b-opus ornith-1-5-397b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/darwin-36b-opus.json and https://www.anchorterminal.com/api/v1/tools/ornith-1-5-397b.json\n\n## Other comparisons with Darwin-36B-Opus or Ornith-1.5-397B\n\n- [Agents-A1-4B vs Darwin-36B-Opus](https://www.anchorterminal.com/compare/agents-a1-4b-vs-darwin-36b-opus.md)\n- [Agents-A1-4B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-397b.md)\n- [Apertus v1.5 8B vs Darwin-36B-Opus](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-darwin-36b-opus.md)\n- [Apertus v1.5 8B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-397b.md)\n- [Apodex-1.1-mini vs Darwin-36B-Opus](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-darwin-36b-opus.md)\n- [Apodex-1.1-mini vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-397b.md)\n- 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12B IT](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-12b-it.md)\n- [Darwin-36B-Opus vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-26b-a4b-it.md)\n- [Darwin-36B-Opus vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-31b-it.md)\n- [Darwin-36B-Opus vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-e2b-it.md)\n- [Darwin-36B-Opus vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-e4b-it.md)\n- [Darwin-36B-Opus vs GLM-4.5-Air](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-4-5-air.md)\n- [Darwin-36B-Opus vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-4-7-flash.md)\n- [Darwin-36B-Opus vs GLM-5.3](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-5-3.md)\n- [Darwin-36B-Opus vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-5-3-flash.md)\n- 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Kolibri-1](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-kolibri-1.md)\n- [Darwin-36B-Opus vs Laguna M.1](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-laguna-m-1.md)\n- [Darwin-36B-Opus vs Laguna S 2.1](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-laguna-s-2-1.md)\n- [Darwin-36B-Opus vs Laguna XS 2.1](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-laguna-xs-2-1.md)\n- [Darwin-36B-Opus vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm2-5-2-6b.md)\n- [Darwin-36B-Opus vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ling-3-0-tiny.md)\n- [Darwin-36B-Opus vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Darwin-36B-Opus vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minicpm-o-4-5.md)\n- [Darwin-36B-Opus vs MiniCPM5-2B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minicpm5-2b.md)\n- [Darwin-36B-Opus vs MiniMax-M2.5](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minimax-m2-5.md)\n- [Darwin-36B-Opus vs MiniMax-M2.7](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minimax-m2-7.md)\n- [Darwin-36B-Opus vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-muse-glimmer-30b.md)\n- [Darwin-36B-Opus vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Darwin-36B-Opus vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-nano-30b-a3b.md)\n- [Darwin-36B-Opus vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-nano-4b.md)\n- [Darwin-36B-Opus vs Nemotron 3 Nano Omni 30B A3B 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[Ornith-1.5-397B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-thinkingcap-qwen3-8-27b.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": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-21", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Darwin-36B-Opus vs Ornith-1.5-397B", "url": "" } ], "description": "Darwin-36B-Opus scores 61 (C) to Ornith-1.5-397B's 59 (C) for open-weight models you run yourself. 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