{ "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", "dns": "darwin-36b-opus.grade.anchorterminal.com", "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 held an Apache 2.0 LICENSE file since 1 November 2026. 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 2026-11-03).", "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": 1518, "categoryRank": 33, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 70, "reach": 80, "risk": 55, "upkeep": 61 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-03" }, "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 a LICENSE file since 1 November 2026 and 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 and base disagree with the files", "Tool calling and structured output aren't documented, and the card names no parser", "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": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 70, "reach": 80, "risk": 55, "upkeep": 61 }, "provenanceScore": 44, "checked": { "categories": { "interface": "2026-11-03", "reach": "2026-11-03", "risk": "2026-11-03", "upkeep": "2026-11-03" }, "last": "2026-11-03", "lastMoved": "2026-11-03", "oldest": "2026-11-03", "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": "2026-11-03", "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 and, since 1 November 2026, in a LICENSE 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": 44 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/darwin-36b-opus.json", "live": { "slug": "darwin-36b-opus", "securityTxt": { "url": "https://vidraft.net/.well-known/security.txt", "state": "valid", "expires": "2027-11-01T00:00:00.000Z", "checkedAt": "2026-11-10T16:09:31.979229229Z" }, "domain": { "domain": "vidraft.net", "registered": "2025-11-27", "source": "https://rdap.verisign.com/net/v1/domain/vidraft.net", "checkedAt": "2026-11-08T15:12:31.077394527Z" }, "updatedAt": "2026-11-10T16:09:31.979229229Z" } }, "answer": "Darwin-36B-Opus scores 61 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 3 of 4 scored categories.", "b": { "slug": "qwen-agentworld-35b-a3b", "name": "Qwen-AgentWorld-35B-A3B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen-AgentWorld-35B-A3B is Alibaba's open-weight language world model, a 35B mixture of experts with 3B active that simulates agent environments by predicting the next observation across seven domains, published in BF16 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen-agentworld-35b-a3b.json", "dns": "qwen-agentworld-35b-a3b.grade.anchorterminal.com", "repo": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen-AgentWorld-35B-A3B" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, with a LICENSE file in the repository. Running it yourself costs the hardware, 69.3 GB for the BF16 files, or 22.1 GB for Unsloth's 4-bit 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/Qwen/Qwen-AgentWorld-35B-A3B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "world-model", "agent-simulation", "reasoning", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-06-25", "graded": true, "anchor": { "graded": true, "score": 52.1, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 104, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 50, "reach": 55, "risk": 55, "upkeep": 41 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A specialist, not a chat model. It plays the environment an agent acts in, across seven domains, so you can train or test an agent without the real thing. Alibaba ships BF16 only, which fits a 128 GB machine, and hasn't answered a discussion on it or changed the files since June.", "bestFor": "Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine", "strengths": [ "Simulates seven kinds of agent environment in one model, with the system prompts for each published", "Apache 2.0, ungated, safetensors only", "Runs in vLLM, SGLang, Transformers and llama.cpp from their releases", "Fits a 128 GB machine in BF16, and 32 GB from Unsloth's 4-bit GGUF", "A 262,144-token context for long interaction histories" ], "weaknesses": [ "Not a general assistant, and the card doesn't describe using it to call tools", "No quantised build from Alibaba, so the GGUF route depends on a third party", "No reply from Alibaba on any of 10 discussions, and no change to the files since 25 June 2026", "No limitations, safety results or security contact on the card", "Its results are on Alibaba's own benchmark" ], "agentNotes": [ "Use it as the environment, not the agent. Give it the domain's system prompt from Alibaba's GitHub repository, the history and the agent's action, and it returns the predicted observation", "With vLLM pass `--language-model-only`, or startup fails on the missing vision weights", "Use temperature 0.6, top_p 0.95 and top_k 20, and allow up to 32,768 output tokens, as the card recommends", "Keep at least 128K of context, since the simulation depends on the history", "Thinking is on by default and comes back through the qwen3 reasoning parser", "A simulated observation is a prediction. Check anything that matters against the real environment" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 52.1 } ], "editorialScores": { "interface": 50, "reach": 55, "risk": 55, "upkeep": 41 }, "provenanceScore": 66, "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": "# vLLM, as the card gives it (four GPUs)\nvllm serve Qwen/Qwen-AgentWorld-35B-A3B --port 8000 --tensor-parallel-size 4 \\\n --max-model-len 262144 --reasoning-parser qwen3 --language-model-only --trust-remote-code\n# or, with llama.cpp and Unsloth's GGUF, a third party's conversion:\nllama-server -hf unsloth/Qwen-AgentWorld-35B-A3B-GGUF:UD-Q4_K_M --jinja", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"Qwen/Qwen-AgentWorld-35B-A3B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"temperature\": 0.6}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen-agentworld-35b-a3b" }, "sameCompany": [ "qwen3-embedding-0-6b", "qwen3-embedding-4b", "qwen3-embedding-8b", "qwen3-vl-embedding-2b", "qwen3-vl-embedding-8b", "qwen3-asr-0-6b", "qwen3-asr-1-7b", "qwen3-tts-1-7b-customvoice", "qwen3-30b-a3b-instruct-2507", "qwen3-4b-instruct-2507", "qwen3-coder-30b-a3b-instruct", "qwen3-coder-next", "qwen3-omni-30b-a3b-instruct", "qwen3-vl-2b-instruct", "qwen3-vl-30b-a3b-instruct", "qwen3-vl-32b-instruct", "qwen3-vl-4b-instruct", "qwen3-vl-8b-instruct", "qwen3-5-0-8b", "qwen3-5-122b-a10b", "qwen3-5-2b", "qwen3-5-397b-a17b", "qwen3-5-4b", "qwen3-5-9b", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen)", "builtOn": "Qwen3.5-35B-A3B-Base, named on the card and in its metadata", "params": "34.7B", "active": "3B", "context": 262144, "goodFor": "A simulator of agent environments (MCP tools, search, terminal, software engineering, Android, web and OS) that predicts the next observation, for training and testing agents without the real environment.", "runtimes": [ "vLLM", "SGLang", "Transformers", "llama.cpp" ], "builds": [ { "name": "unsloth Qwen-AgentWorld-35B-A3B-GGUF (UD-Q4_K_M)", "url": "https://huggingface.co/unsloth/Qwen-AgentWorld-35B-A3B-GGUF", "who": "unsloth", "precision": "q4_k_m", "sizeGB": 22.13, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From Unsloth, a third party's conversion and not Alibaba's. 22.13 GB of UD-Q4_K_M weights, from the Hub's model information on 10 November 2026. Reach doesn't count it" }, { "name": "unsloth Qwen-AgentWorld-35B-A3B-GGUF (Q8_0)", "url": "https://huggingface.co/unsloth/Qwen-AgentWorld-35B-A3B-GGUF", "who": "unsloth", "precision": "q8_0", "sizeGB": 36.9, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From Unsloth, a third party's conversion and not Alibaba's. 36.90 GB of Q8_0 weights, from the Hub's model information on 10 November 2026. Reach doesn't count it" }, { "name": "Qwen-AgentWorld-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B", "who": "publisher", "precision": "bf16", "sizeGB": 69.32, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ], "note": "The only build Alibaba publishes. Language weights only" } ], "run": "llama-server -hf unsloth/Qwen-AgentWorld-35B-A3B-GGUF:UD-Q4_K_M --jinja", "runBuild": "unsloth Qwen-AgentWorld-35B-A3B-GGUF (UD-Q4_K_M)", "checked": "2026-11-10", "minMemoryGB": 32 }, "family": { "lab": "Alibaba (Qwen)", "family": "Qwen-AgentWorld", "url": "https://www.anchorterminal.com/open-weights/#qwen", "olderCount": 109 }, "area": "models", "provenance": { "legalEntity": "Alibaba Cloud (named in the licence's copyright line)", "domain": "qwen.ai", "domainRegistered": "2023-08-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The LICENSE file names Alibaba Cloud, as the Qwen3.6-35B-A3B listing records for the organisation.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, nine commits between 22 and 25 June 2026, the upload and updates to the card.", "The Qwen3.6-35B-A3B listing found no security.txt on qwen.ai. unchecked on 10 November." ], "score": 66 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.json" }, "edges": [ { "by": 25, "category": "reach", "leads": "darwin-36b-opus", "name": "Reach", "reasons": { "a": "Memory 20, the publisher's Q4_K_M GGUF is 21.2 GB and needs about 25.4 GB with the fifth added, so 32 GB. Runtimes 20, the text-only Qwen3.5 MoE class runs in released vLLM, SGLang, Transformers and llama.cpp, while Ollama's and LM Studio's entries are the base's, not this model's. Builds 15, FINAL-Bench's POCKET-35B-GGUF has it at four precisions. Terms 15, Apache 2.0 with nothing added, with a LICENSE file since 1 November 2026, and Apache 2.0 on the base Qwen3.6-35B-A3B too. Download 10, nothing is gated. Hosted 0, no provider or API serves it.", "b": "Memory 10, Alibaba's only build is the BF16 safetensors, 69.32 GB, and 69.32 GB × 1.2 = 83.2 GB, above 64 GB and inside the reference machine's GPU share. Runtimes 20, Four released runtimes run the architecture, vLLM 0.33.0 (Qwen3_5MoeForConditionalGeneration), SGLang 0.5.23, Transformers 5.22.2 (qwen3_5_moe) and llama.cpp b12242 (its Qwen conversion code). Builds 0, Alibaba publishes the BF16 weights only. Terms 15, Apache 2.0, an OSI licence, in the metadata and as a LICENSE file, with no use policy or other terms added. Download 10, the repository isn't gated, so the files download with no account and no terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026). 10 + 20 + 0 + 15 + 10 + 0 = 55." }, "scores": { "a": 80, "b": 55 } }, { "by": 20, "category": "interface", "leads": "darwin-36b-opus", "name": "Agent interface", "reasons": { "a": "Chat template 20, Qwen3.6's Jinja template in the repository. Tool-calling format 10, the format is only in the template and the card says nothing about tools. Trained tool calling 20, inherited from the base that was trained for it, with several calls in one turn in the template, though the publisher neither claims nor tests it. Structured output 0. Reasoning control 10, the template turns thinking off per request and the card shows where the reasoning comes back. Context 10, 262,144 tokens.", "b": "Chat template 20, chat_template.jinja in the repository, the same as the template in tokenizer_config.json. Tool-calling format 10, only the template says how tools are declared and how calls come back. Trained tool calling 10, the template renders several tool_call blocks in one turn, but the card presents the model as a simulator of what tools and environments return, not as a tool caller. Structured output 0, no JSON or JSON Schema output documented for the model. Reasoning control 0, the card says thinking is on by default and names the qwen3 reasoning parser, but doesn't say how to turn thinking off or budget it. Context 10, 262,144 tokens native per the card and the text config. 20 + 10 + 10 + 0 + 0 + 10 = 50." }, "scores": { "a": 70, "b": 50 } }, { "by": 20, "category": "upkeep", "leads": "darwin-36b-opus", "name": "Documentation and upkeep", "reasons": { "a": "Model card 11, it covers architecture, parameters, context and languages but not intended use, limitations or a knowledge cut-off. Training data 5, the reasoning parent's fine-tune is described without datasets or a cut-off. Last change 20, on 1 November 2026 the publisher added a LICENSE file and a corrected eval_results note to the main repository and edited the POCKET-35B-GGUF card, read as this run reads card edits on other open-weight listings (Muse Glimmer 30B, MiMo-V2.6 Distill Qwen 9B, Apertus v1.5 8B). The weights date from 23 April and the GGUF build from 22 July. Responsiveness 20, the publisher answered 8 of the 13 discussions, and two of the rest are its own posts. Version policy 5, a first release whose weights were replaced twice in its first day with commit titles but no card note. File health 0, the card's head counts and base still disagree with the files, and the generation config drops the base's end-of-turn stop token. 11 + 5 + 20 + 20 + 5 + 0 = 61.", "b": "Model card 11, the card gives intended use (simulating agent environments), architecture, parameters and context length. Training data 5, A general description without a cut-off, the three training stages and a statement that no outputs from external API services were included. Last change 10, the last commit was on 25 June 2026, an update to the card, 138 days before this check, past 90 days and within 180. Responsiveness 0, 10 discussions that aren't pull requests, opened between 24 June and 5 July 2026. Version policy 5, A first release, with nothing replaced. File health 10, Config, both copies of the template and the card agree on the architecture and the context. 11 + 5 + 10 + 0 + 5 + 10 = 41." }, "scores": { "a": 61, "b": 41 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "FINAL-Bench (VIDRAFT)", "b": "Alibaba (Qwen team)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-04-23", "b": "2026-06-25", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "2.5/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Darwin-36B-Opus scores 61 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 3 of 4 scored categories.", "question": "Which is better for AI agents, Darwin-36B-Opus or Qwen-AgentWorld-35B-A3B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 80 against 55", "Agent interface, 70 against 50", "Documentation and upkeep, 61 against 41" ], "also": null, "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": null, "also": null, "goodFor": "Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine", "slug": "qwen-agentworld-35b-a3b", "watchFor": "Not a general assistant, and the card doesn't describe using it to call tools" } ], "history": { "darwin-36b-opus": [ { "causes": [ "correction" ], "date": "2026-11-03", "delta": 0, "from": 61, "fromGrade": "C", "to": 61, "toGrade": "C" } ], "qwen-agentworld-35b-a3b": null }, "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-qwen-agentworld-35b-a3b.json", "title": "Agents-A1-4B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen-agentworld-35b-a3b" }, { "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": 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Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v3-2.json", "title": "Darwin-36B-Opus vs DeepSeek-V3.2", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v3-2" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v4-1-flash.json", "title": "Darwin-36B-Opus vs DeepSeek V4.1 Flash", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v4-1-flash" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v4-flash-0731.json", "title": "Darwin-36B-Opus vs DeepSeek-V4-Flash-0731", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v4-flash-0731" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-devstral-small-2.json", "title": "Darwin-36B-Opus vs Devstral Small 2", "url": 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Nemotron 3.5 Bolt E4B 4GB vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen-agentworld-35b-a3b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen-agentworld-35b-a3b.json", "title": "Nemotron 3 Nano 30B A3B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen-agentworld-35b-a3b.json", "title": "Nemotron 3 Nano 4B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen-agentworld-35b-a3b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen-agentworld-35b-a3b.json", "title": "Nemotron 3 Super 120B A12B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen-agentworld-35b-a3b.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen-agentworld-35b-a3b.json", "title": "North Mini Code 1.0 vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen-agentworld-35b-a3b.json", "title": "occamy-1.0 vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen-agentworld-35b-a3b.json", "title": "Olmo 3 7B Think vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen-agentworld-35b-a3b" }, { "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-1-5-35b-a3b-vs-qwen-agentworld-35b-a3b.json", "title": "Ornith-1.5-35B-A3B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen-agentworld-35b-a3b.json", "title": "Ornith-1.5-397B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen-agentworld-35b-a3b.json", "title": "Ornith-1.5-9B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen-agentworld-35b-a3b.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-4b-instruct-2507.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-0-8b.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-122b-a10b.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-122b-a10b" }, { "json": 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"https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-6-27b.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-6-35b-a3b.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-8-27b.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-8-flash-next.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-coder-next.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-2b-instruct.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-32b-instruct.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-8b-instruct.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-recursive-flint.json", "title": "Qwen-AgentWorld-35B-A3B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-ripple-65b-preview.json", "title": "Qwen-AgentWorld-35B-A3B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-sensenova-loop-9b-preview.json", "title": "Qwen-AgentWorld-35B-A3B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-smollm3-3b.json", "title": "Qwen-AgentWorld-35B-A3B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-spark-x2-6-4b.json", "title": "Qwen-AgentWorld-35B-A3B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-step-3-7-flash.json", "title": "Qwen-AgentWorld-35B-A3B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen-AgentWorld-35B-A3B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen-AgentWorld-35B-A3B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen-AgentWorld-35B-A3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-unlimited-ocr-2.json", "title": "Qwen-AgentWorld-35B-A3B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-2b.json", "title": "Qwen-AgentWorld-35B-A3B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-4b.json", "title": "Qwen-AgentWorld-35B-A3B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-zaya1-8b.json", "title": "Qwen-AgentWorld-35B-A3B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 25, "darwin-36b-opus": 80, "edge": "darwin-36b-opus", "key": "reach", "name": "Reach", "qwen-agentworld-35b-a3b": 55, "weight": 25 }, { "by": 0, "darwin-36b-opus": 55, "edge": "", "key": "risk", "name": "Risk", "qwen-agentworld-35b-a3b": 55, "weight": 20 }, { "by": 20, "darwin-36b-opus": 70, "edge": "darwin-36b-opus", "key": "interface", "name": "Agent interface", "qwen-agentworld-35b-a3b": 50, "weight": 10 }, { "by": 20, "darwin-36b-opus": 61, "edge": "darwin-36b-opus", "key": "upkeep", "name": "Documentation and upkeep", "qwen-agentworld-35b-a3b": 41, "weight": 10 } ], "summary": "Darwin-36B-Opus scores 61 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.", "tasks": null, "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.", "qwen-agentworld-35b-a3b": "A specialist, not a chat model. It plays the environment an agent acts in, across seven domains, so you can train or test an agent without the real thing. Alibaba ships BF16 only, which fits a 128 GB machine, and hasn't answered a discussion on it or changed the files since June." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen-agentworld-35b-a3b", "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen-agentworld-35b-a3b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen-agentworld-35b-a3b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen-agentworld-35b-a3b.min.md" }, "markdown": "Darwin-36B-Opus scores 61 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.\n\n- Darwin-36B-Opus: grade C, 61/100, rank #43 of 160 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- Qwen-AgentWorld-35B-A3B: grade D, 52.1/100, rank #141 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen-agentworld-35b-a3b.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### 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 55\n- Agent interface, 70 against 50\n- Documentation and upkeep, 61 against 41\n\nWatch for: The merge method is proprietary and the current weights include an undescribed targeted refinement\n\n### Qwen-AgentWorld-35B-A3B (D)\n\nGood for: Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine\n\nWatch for: Not a general assistant, and the card doesn't describe using it to call tools\n\n\n## Why the edge\n\n### Reach: Darwin-36B-Opus +25\n\n- Darwin-36B-Opus (80): Memory 20, the publisher's Q4_K_M GGUF is 21.2 GB and needs about 25.4 GB with the fifth added, so 32 GB. Runtimes 20, the text-only Qwen3.5 MoE class runs in released vLLM, SGLang, Transformers and llama.cpp, while Ollama's and LM Studio's entries are the base's, not this model's. Builds 15, FINAL-Bench's POCKET-35B-GGUF has it at four precisions. Terms 15, Apache 2.0 with nothing added, with a LICENSE file since 1 November 2026, and Apache 2.0 on the base Qwen3.6-35B-A3B too. Download 10, nothing is gated. Hosted 0, no provider or API serves it.\n- Qwen-AgentWorld-35B-A3B (55): Memory 10, Alibaba's only build is the BF16 safetensors, 69.32 GB, and 69.32 GB × 1.2 = 83.2 GB, above 64 GB and inside the reference machine's GPU share. Runtimes 20, Four released runtimes run the architecture, vLLM 0.33.0 (Qwen3_5MoeForConditionalGeneration), SGLang 0.5.23, Transformers 5.22.2 (qwen3_5_moe) and llama.cpp b12242 (its Qwen conversion code). Builds 0, Alibaba publishes the BF16 weights only. Terms 15, Apache 2.0, an OSI licence, in the metadata and as a LICENSE file, with no use policy or other terms added. Download 10, the repository isn't gated, so the files download with no account and no terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026). 10 + 20 + 0 + 15 + 10 + 0 = 55.\n\n### Agent interface: Darwin-36B-Opus +20\n\n- Darwin-36B-Opus (70): Chat template 20, Qwen3.6's Jinja template in the repository. Tool-calling format 10, the format is only in the template and the card says nothing about tools. Trained tool calling 20, inherited from the base that was trained for it, with several calls in one turn in the template, though the publisher neither claims nor tests it. Structured output 0. Reasoning control 10, the template turns thinking off per request and the card shows where the reasoning comes back. Context 10, 262,144 tokens.\n- Qwen-AgentWorld-35B-A3B (50): Chat template 20, chat_template.jinja in the repository, the same as the template in tokenizer_config.json. Tool-calling format 10, only the template says how tools are declared and how calls come back. Trained tool calling 10, the template renders several tool_call blocks in one turn, but the card presents the model as a simulator of what tools and environments return, not as a tool caller. Structured output 0, no JSON or JSON Schema output documented for the model. Reasoning control 0, the card says thinking is on by default and names the qwen3 reasoning parser, but doesn't say how to turn thinking off or budget it. Context 10, 262,144 tokens native per the card and the text config. 20 + 10 + 10 + 0 + 0 + 10 = 50.\n\n### Documentation and upkeep: Darwin-36B-Opus +20\n\n- Darwin-36B-Opus (61): Model card 11, it covers architecture, parameters, context and languages but not intended use, limitations or a knowledge cut-off. Training data 5, the reasoning parent's fine-tune is described without datasets or a cut-off. Last change 20, on 1 November 2026 the publisher added a LICENSE file and a corrected eval_results note to the main repository and edited the POCKET-35B-GGUF card, read as this run reads card edits on other open-weight listings (Muse Glimmer 30B, MiMo-V2.6 Distill Qwen 9B, Apertus v1.5 8B). The weights date from 23 April and the GGUF build from 22 July. Responsiveness 20, the publisher answered 8 of the 13 discussions, and two of the rest are its own posts. Version policy 5, a first release whose weights were replaced twice in its first day with commit titles but no card note. File health 0, the card's head counts and base still disagree with the files, and the generation config drops the base's end-of-turn stop token. 11 + 5 + 20 + 20 + 5 + 0 = 61.\n- Qwen-AgentWorld-35B-A3B (41): Model card 11, the card gives intended use (simulating agent environments), architecture, parameters and context length. Training data 5, A general description without a cut-off, the three training stages and a statement that no outputs from external API services were included. Last change 10, the last commit was on 25 June 2026, an update to the card, 138 days before this check, past 90 days and within 180. Responsiveness 0, 10 discussions that aren't pull requests, opened between 24 June and 5 July 2026. Version policy 5, A first release, with nothing replaced. File health 10, Config, both copies of the template and the card agree on the architecture and the context. 11 + 5 + 10 + 0 + 5 + 10 = 41.\n\n## Score by category\n\n| Category | Weight | Darwin-36B-Opus | Qwen-AgentWorld-35B-A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 80 | 55 | Darwin-36B-Opus +25 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 70 | 50 | Darwin-36B-Opus +20 |\n| Documentation and upkeep | 10% (10 this run) | 61 | 41 | Darwin-36B-Opus +20 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **52.1 · D** | |\n\n## Facts side by side\n\n| Fact | Darwin-36B-Opus | Qwen-AgentWorld-35B-A3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | FINAL-Bench (VIDRAFT) | Alibaba (Qwen team) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-04-23 | 2026-06-25 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 2.5/5 (4) | none |\n\n## How the scores have moved\n\n### Darwin-36B-Opus\n\n- no change on 3 November 2026: correction\n\n### Qwen-AgentWorld-35B-A3B\n\nNo score change since it was listed.\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**Qwen-AgentWorld-35B-A3B.** A specialist, not a chat model. It plays the environment an agent acts in, across seven domains, so you can train or test an agent without the real thing. Alibaba ships BF16 only, which fits a 128 GB machine, and hasn't answered a discussion on it or changed the files since June.\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### Qwen-AgentWorld-35B-A3B\n\n1. Use it as the environment, not the agent. Give it the domain's system prompt from Alibaba's GitHub repository, the history and the agent's action, and it returns the predicted observation\n2. With vLLM pass `--language-model-only`, or startup fails on the missing vision weights\n3. Use temperature 0.6, top_p 0.95 and top_k 20, and allow up to 32,768 output tokens, as the card recommends\n4. Keep at least 128K of context, since the simulation depends on the history\n5. Thinking is on by default and comes back through the qwen3 reasoning parser\n6. A simulated observation is a prediction. Check anything that matters against the real environment\n\n## Questions\n\n### Which is better for AI agents, Darwin-36B-Opus or Qwen-AgentWorld-35B-A3B?\n\nDarwin-36B-Opus scores 61 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 3 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen-agentworld-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen-agentworld-35b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"darwin-36b-opus\", \"b\": \"qwen-agentworld-35b-a3b\"}`. From a terminal: `anchor compare darwin-36b-opus qwen-agentworld-35b-a3b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/darwin-36b-opus.json and https://www.anchorterminal.com/api/v1/tools/qwen-agentworld-35b-a3b.json\n\n## Other comparisons with Darwin-36B-Opus or Qwen-AgentWorld-35B-A3B\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 Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen-agentworld-35b-a3b.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 Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen-agentworld-35b-a3b.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 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