{ "data": { "a": { "slug": "arete-27b", "name": "Arete-27B", "vendor": "Nous Research", "vendorUrl": "https://nousresearch.com", "kind": "weights", "category": "open-weights", "summary": "Arete-27B is Nous Research's fine-tune of Qwen3.8-27B, trained on 28,886 published examples so that its answers on politically sensitive topics stop following the Chinese state's account, with the base's tools, images and 262K context, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/arete-27b", "markdownUrl": "https://www.anchorterminal.com/tools/arete-27b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/arete-27b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/arete-27b.json", "dns": "arete-27b.grade.anchorterminal.com", "repo": "https://huggingface.co/NousResearch/Arete-27B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "NousResearch/Arete-27B" } ], "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, in the card's metadata and as a LICENSE file in the repository. Running it yourself costs the hardware, 54.7 GB for the BF16 files, or 18.1 GB for bartowski's Q4_K_M GGUF with its projector (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/NousResearch/Arete-27B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "fine-tune", "qwen", "multimodal", "vision", "reasoning", "tool-calling", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-11-06", "graded": true, "anchor": { "graded": true, "score": 55.1, "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": 92, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 60, "reach": 50, "risk": 55, "upkeep": 63 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.", "bestFor": "Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine", "strengths": [ "The training pairs and the evaluation set are both published", "Apache 2.0, ungated, safetensors only, no remote code", "The base's architecture, so vLLM, SGLang and Transformers run it from their releases", "Fits a 128 GB machine in BF16, and 24 GB from bartowski's 4-bit GGUF", "Nous Research reports no meaningful loss against the base on its agent benchmarks" ], "weaknesses": [ "No quantised build from Nous Research, so the GGUF route depends on a third party", "The card doesn't document tool calling, structured output or how to switch thinking", "No limitations section, safety results or security contact", "None of the 13 discussions has a reply from Nous Research", "The results are Nous Research's own evaluation and nobody else has rerun it yet" ], "agentNotes": [ "Start it with `llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja`, a third party's conversion, or serve the BF16 files with vLLM", "Use the base's parsers in vLLM, `--reasoning-parser qwen3 --tool-call-parser qwen3_coder`, and check tool calls first, since the template differs slightly from Qwen3.8-27B's", "Thinking switches with `enable_thinking` in the template. The card's own evaluation ran with it off", "Expect answers on politically sensitive topics to differ from Qwen3.8-27B's. That is what the fine-tune is for", "Validate JSON output yourself, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 55.1 } ], "editorialScores": { "interface": 60, "reach": 50, "risk": 55, "upkeep": 63 }, "provenanceScore": 61, "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 serve NousResearch/Arete-27B --max-model-len 262144 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or, with llama.cpp and bartowski's GGUF, a third party's conversion:\nllama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"NousResearch/Arete-27B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/arete-27b" }, "sameCompany": [ "hermes-agent" ], "weights": { "trainedBy": "Nous Research", "builtOn": "Qwen3.8-27B, declared as base_model in the card's metadata. Supervised fine-tuning with Inkling as the teacher", "params": "27.4B", "context": 262144, "goodFor": "Qwen3.8-27B for coding and agent work, text, images and video in, with its answers on politically sensitive topics retrained away from the Chinese state's account.", "runtimes": [ "vLLM", "SGLang", "Transformers", "llama.cpp" ], "builds": [ { "name": "bartowski NousResearch_Arete-27B-GGUF (Q4_K_M)", "url": "https://huggingface.co/bartowski/NousResearch_Arete-27B-GGUF", "who": "bartowski", "precision": "q4_k_m", "sizeGB": 18.13, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not Nous Research's. 17.20 GB of Q4_K_M weights plus the 0.93 GB F16 projector, from the Hub's model information on 10 November 2026. Reach doesn't count it" }, { "name": "bartowski NousResearch_Arete-27B-GGUF (Q8_0)", "url": "https://huggingface.co/bartowski/NousResearch_Arete-27B-GGUF", "who": "bartowski", "precision": "q8_0", "sizeGB": 29.6, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not Nous Research's. 28.67 GB of Q8_0 weights plus the 0.93 GB F16 projector, from the Hub's model information on 10 November 2026. Reach doesn't count it" }, { "name": "Arete-27B", "url": "https://huggingface.co/NousResearch/Arete-27B", "who": "publisher", "precision": "bf16", "sizeGB": 54.7, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ], "note": "The only build Nous Research publishes" } ], "run": "llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja", "runBuild": "bartowski NousResearch_Arete-27B-GGUF (Q4_K_M)", "checked": "2026-11-10", "minMemoryGB": 24 }, "family": { "lab": "Nous Research (NousResearch)", "family": "Arete", "url": "https://www.anchorterminal.com/open-weights/#nous-research" }, "area": "models", "provenance": { "legalEntity": "Nous Research, Inc. (named in the footer of nousresearch.com)", "domain": "nousresearch.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/NousResearch/Arete-27B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "Nous Research, Inc. is the name in the site's footer, as the Hermes Agent listing records.", "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, eight commits on 6 November 2026, the upload and updates to the card.", "The Hermes Agent listing found no security.txt on nousresearch.com. unchecked on 10 November.", "unchecked: the domain's registration date." ], "score": 61 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/arete-27b.json" }, "answer": "Darwin-36B-Opus scores 61 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 2 of 4 scored categories.", "b": { "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" } }, "edges": [ { "by": 30, "category": "reach", "leads": "darwin-36b-opus", "name": "Reach", "reasons": { "a": "Memory 10, the only build from Nous Research is the 54.7 GB BF16 release, which needs about 66 GB and fits the reference machine. Runtimes 15, vLLM, SGLang and Transformers run the base's architecture from those files, and llama.cpp isn't credited because Nous Research publishes no GGUF. Builds 0, no quantised build of its own. Terms 15, Apache 2.0. Download 10, not gated. Hosted 0, no provider. 10 + 15 + 0 + 15 + 10 + 0 = 50.", "b": "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." }, "scores": { "a": 50, "b": 80 } }, { "by": 10, "category": "interface", "leads": "darwin-36b-opus", "name": "Agent interface", "reasons": { "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 10, only the template says it. Trained tool calling 20, several calls a turn and agentic work as an intended use. Structured output 0 and reasoning control 0, neither is documented on the card. Context 10, 262,144 tokens native. 20 + 10 + 20 + 0 + 0 + 10 = 60.", "b": "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." }, "scores": { "a": 60, "b": 70 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Nous Research", "b": "FINAL-Bench (VIDRAFT)", "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": "2026-11-06", "b": "2026-04-23", "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": "none", "b": "2.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Darwin-36B-Opus scores 61 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 2 of 4 scored categories.", "question": "Which is better for AI agents, Arete-27B or Darwin-36B-Opus?" } ], "goodFor": [ { "aheadOn": null, "also": null, "goodFor": "Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine", "slug": "arete-27b", "watchFor": "No quantised build from Nous Research, so the GGUF route depends on a third party" }, { "aheadOn": [ "Reach, 80 against 50", "Agent interface, 70 against 60" ], "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" } ], "history": { "arete-27b": null, "darwin-36b-opus": [ { "causes": [ "correction" ], "date": "2026-11-03", "delta": 0, "from": 61, "fromGrade": "C", "to": 61, "toGrade": "C" } ] }, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-arete-27b.json", "title": "Agents-A1-4B vs Arete-27B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-arete-27b" }, { "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/apertus-v1-5-8b-vs-arete-27b.json", "title": "Apertus v1.5 8B vs Arete-27B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-arete-27b" }, { "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/apodex-1-1-mini-vs-arete-27b.json", "title": "Apodex-1.1-mini vs Arete-27B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-arete-27b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-darwin-36b-opus.json", "title": "Apodex-1.1-mini vs Darwin-36B-Opus", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-darwin-36b-opus" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-bigbang-v1.json", "title": "Arete-27B vs BigBang-v1", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-bigbang-v1" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-carbon-x.json", "title": "Arete-27B vs Carbon-X", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-carbon-x" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-cola2.json", "title": "Arete-27B vs Cola2", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-cola2" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-cosmos-reason2-2b.json", "title": "Arete-27B vs Cosmos-Reason2-2B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-cosmos-reason2-2b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v3-2.json", "title": "Arete-27B vs DeepSeek-V3.2", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v3-2" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v4-1-flash.json", "title": "Arete-27B vs DeepSeek V4.1 Flash", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v4-1-flash" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v4-flash-0731.json", "title": "Arete-27B vs DeepSeek-V4-Flash-0731", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v4-flash-0731" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-devstral-small-2.json", "title": "Arete-27B vs Devstral Small 2", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-devstral-small-2" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-diffusiongemma-26b-a4b-it.json", "title": "Arete-27B vs DiffusionGemma 26B A4B IT", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-diffusiongemma-26b-a4b-it" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-exaone-4-5-33b.json", "title": "Arete-27B vs EXAONE 4.5 33B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-exaone-4-5-33b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-12b-it.json", "title": "Arete-27B vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-12b-it" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-26b-a4b-it.json", "title": "Arete-27B vs Gemma 4 26B A4B IT", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-26b-a4b-it" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-31b-it.json", "title": "Arete-27B vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e2b-it.json", "title": "Arete-27B vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e4b-it.json", "title": "Arete-27B vs Gemma 4 E4B IT", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e4b-it" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-4-5-air.json", "title": "Arete-27B vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-4-7-flash.json", "title": "Arete-27B vs GLM-4.7-Flash", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-4-7-flash" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-5-3.json", "title": "Arete-27B vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-5-3" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-5-3-flash.json", "title": "Arete-27B vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-gpt-oss-120b.json", "title": "Arete-27B vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-gpt-oss-120b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-gpt-oss-20b.json", "title": "Arete-27B vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-gpt-oss-20b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-30b.json", "title": "Arete-27B vs Granite 4.2 30B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-30b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-3b.json", "title": "Arete-27B vs Granite 4.2 3B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-3b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-8b.json", "title": "Arete-27B vs Granite 4.2 8B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-8b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-hy-memory-30b-a3b.json", "title": "Arete-27B vs Hy-Memory-30B-A3B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-hy-memory-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-hy3.json", "title": "Arete-27B vs Hy3", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-inkling.json", "title": "Arete-27B vs Inkling", "url": 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"title": "Arete-27B vs Kolibri-1", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-kolibri-1" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-kronos.json", "title": "Arete-27B vs Kronos", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-laguna-m-1.json", "title": "Arete-27B vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-laguna-s-2-1.json", "title": "Arete-27B vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-laguna-xs-2-1.json", "title": "Arete-27B vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-lbs-27b.json", "title": "Arete-27B vs LBS-27B", "url": 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"json": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-6-27b.json", "title": "Arete-27B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-6-35b-a3b.json", "title": "Arete-27B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-8-27b.json", "title": "Arete-27B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-8-flash-next.json", "title": "Arete-27B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Arete-27B vs Qwen3-Coder-30B-A3B-Instruct", "url": 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"json": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-32b-instruct.json", "title": "Arete-27B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-4b-instruct.json", "title": "Arete-27B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-8b-instruct.json", "title": "Arete-27B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-qwopus3-8-27b-flash-v2.json", "title": "Arete-27B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-recursive-flint.json", "title": "Arete-27B 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"https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Darwin-36B-Opus vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-4b-instruct-2507.json", "title": "Darwin-36B-Opus vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-0-8b.json", "title": "Darwin-36B-Opus vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-122b-a10b.json", "title": "Darwin-36B-Opus vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-2b.json", "title": "Darwin-36B-Opus vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-397b-a17b.json", "title": "Darwin-36B-Opus vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-4b.json", "title": "Darwin-36B-Opus vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-9b.json", "title": "Darwin-36B-Opus vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-6-27b.json", "title": "Darwin-36B-Opus vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-6-35b-a3b.json", "title": "Darwin-36B-Opus vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-8-27b.json", "title": "Darwin-36B-Opus vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-8-flash-next.json", "title": "Darwin-36B-Opus vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Darwin-36B-Opus vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-coder-next.json", "title": "Darwin-36B-Opus vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Darwin-36B-Opus vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-vl-2b-instruct.json", "title": "Darwin-36B-Opus vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Darwin-36B-Opus vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-vl-32b-instruct.json", "title": "Darwin-36B-Opus vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-vl-4b-instruct.json", "title": "Darwin-36B-Opus vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-vl-8b-instruct.json", "title": "Darwin-36B-Opus vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwopus3-8-27b-flash-v2.json", "title": "Darwin-36B-Opus vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-recursive-flint.json", "title": "Darwin-36B-Opus vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ripple-65b-preview.json", "title": "Darwin-36B-Opus vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-sensenova-loop-9b-preview.json", "title": "Darwin-36B-Opus vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-smollm3-3b.json", "title": "Darwin-36B-Opus vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-spark-x2-6-4b.json", "title": "Darwin-36B-Opus vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-step-3-7-flash.json", "title": "Darwin-36B-Opus vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-swift-1-5-qwen3-8-27b.json", "title": "Darwin-36B-Opus vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Darwin-36B-Opus vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-thinkingcap-qwen3-8-27b.json", "title": "Darwin-36B-Opus vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-unlimited-ocr-2.json", "title": "Darwin-36B-Opus vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-wevisone-vl-2b.json", "title": "Darwin-36B-Opus vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-wevisone-vl-4b.json", "title": "Darwin-36B-Opus vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-zaya1-8b.json", "title": "Darwin-36B-Opus vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "arete-27b": 50, "by": 30, "darwin-36b-opus": 80, "edge": "darwin-36b-opus", "key": "reach", "name": "Reach", "weight": 25 }, { "arete-27b": 55, "by": 0, "darwin-36b-opus": 55, "edge": "", "key": "risk", "name": "Risk", "weight": 20 }, { "arete-27b": 60, "by": 10, "darwin-36b-opus": 70, "edge": "darwin-36b-opus", "key": "interface", "name": "Agent interface", "weight": 10 }, { "arete-27b": 63, "by": 2, "darwin-36b-opus": 61, "edge": "arete-27b", "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Darwin-36B-Opus scores 61 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 2 of 4 scored categories. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "arete-27b": "Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/arete-27b-vs-darwin-36b-opus", "json": "https://www.anchorterminal.com/compare/arete-27b-vs-darwin-36b-opus.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/arete-27b-vs-darwin-36b-opus.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/arete-27b-vs-darwin-36b-opus.min.md" }, "markdown": "Darwin-36B-Opus scores 61 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 2 of 4 scored categories. Both do open-weight models you run yourself.\n\n- Arete-27B: grade C, 55.1/100, rank #123 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/arete-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/arete-27b.json\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- 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### Arete-27B (C)\n\nGood for: Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine\n\nWatch for: No quantised build from Nous Research, so the GGUF route depends on a third party\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 50\n- Agent interface, 70 against 60\n\nWatch for: The merge method is proprietary and the current weights include an undescribed targeted refinement\n\n\n## Why the edge\n\n### Reach: Darwin-36B-Opus +30\n\n- Arete-27B (50): Memory 10, the only build from Nous Research is the 54.7 GB BF16 release, which needs about 66 GB and fits the reference machine. Runtimes 15, vLLM, SGLang and Transformers run the base's architecture from those files, and llama.cpp isn't credited because Nous Research publishes no GGUF. Builds 0, no quantised build of its own. Terms 15, Apache 2.0. Download 10, not gated. Hosted 0, no provider. 10 + 15 + 0 + 15 + 10 + 0 = 50.\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\n### Agent interface: Darwin-36B-Opus +10\n\n- Arete-27B (60): Chat template 20, a Jinja file in the repository. Tool-calling format 10, only the template says it. Trained tool calling 20, several calls a turn and agentic work as an intended use. Structured output 0 and reasoning control 0, neither is documented on the card. Context 10, 262,144 tokens native. 20 + 10 + 20 + 0 + 0 + 10 = 60.\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\n## Score by category\n\n| Category | Weight | Arete-27B | Darwin-36B-Opus | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 50 | 80 | Darwin-36B-Opus +30 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 60 | 70 | Darwin-36B-Opus +10 |\n| Documentation and upkeep | 10% (10 this run) | 63 | 61 | Arete-27B +2 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **55.1 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Arete-27B | Darwin-36B-Opus |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Nous Research | FINAL-Bench (VIDRAFT) |\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 | 2026-11-06 | 2026-04-23 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 2.5/5 (4) |\n\n## How the scores have moved\n\n### Arete-27B\n\nNo score change since it was listed.\n\n### Darwin-36B-Opus\n\n- no change on 3 November 2026: correction\n\n## Verdicts\n\n**Arete-27B.** Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.\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## Before you call either\n\n### Arete-27B\n\n1. Start it with `llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja`, a third party's conversion, or serve the BF16 files with vLLM\n2. Use the base's parsers in vLLM, `--reasoning-parser qwen3 --tool-call-parser qwen3_coder`, and check tool calls first, since the template differs slightly from Qwen3.8-27B's\n3. Thinking switches with `enable_thinking` in the template. The card's own evaluation ran with it off\n4. Expect answers on politically sensitive topics to differ from Qwen3.8-27B's. That is what the fine-tune is for\n5. Validate JSON output yourself, since structured output isn't documented\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## Questions\n\n### Which is better for AI agents, Arete-27B or Darwin-36B-Opus?\n\nDarwin-36B-Opus scores 61 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 2 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/arete-27b-vs-darwin-36b-opus.json, and with the fewest tokens: https://www.anchorterminal.com/compare/arete-27b-vs-darwin-36b-opus.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"arete-27b\", \"b\": \"darwin-36b-opus\"}`. From a terminal: `anchor compare arete-27b darwin-36b-opus`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/arete-27b.json and https://www.anchorterminal.com/api/v1/tools/darwin-36b-opus.json\n\n## Other comparisons with Arete-27B or Darwin-36B-Opus\n\n- [Agents-A1-4B vs Arete-27B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-arete-27b.md)\n- [Agents-A1-4B vs Darwin-36B-Opus](https://www.anchorterminal.com/compare/agents-a1-4b-vs-darwin-36b-opus.md)\n- [Apertus v1.5 8B vs Arete-27B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-arete-27b.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- [Apodex-1.1-mini vs Arete-27B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-arete-27b.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- [Arete-27B vs BigBang-v1](https://www.anchorterminal.com/compare/arete-27b-vs-bigbang-v1.md)\n- [Arete-27B vs Carbon-X](https://www.anchorterminal.com/compare/arete-27b-vs-carbon-x.md)\n- [Arete-27B vs Cola2](https://www.anchorterminal.com/compare/arete-27b-vs-cola2.md)\n- [Arete-27B vs Cosmos-Reason2-2B](https://www.anchorterminal.com/compare/arete-27b-vs-cosmos-reason2-2b.md)\n- [Arete-27B vs DeepSeek-V3.2](https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v3-2.md)\n- [Arete-27B vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v4-1-flash.md)\n- [Arete-27B vs DeepSeek-V4-Flash-0731](https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v4-flash-0731.md)\n- [Arete-27B vs Devstral Small 2](https://www.anchorterminal.com/compare/arete-27b-vs-devstral-small-2.md)\n- [Arete-27B vs DiffusionGemma 26B A4B IT](https://www.anchorterminal.com/compare/arete-27b-vs-diffusiongemma-26b-a4b-it.md)\n- [Arete-27B vs EXAONE 4.5 33B](https://www.anchorterminal.com/compare/arete-27b-vs-exaone-4-5-33b.md)\n- [Arete-27B vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-12b-it.md)\n- [Arete-27B vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-26b-a4b-it.md)\n- [Arete-27B vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-31b-it.md)\n- [Arete-27B vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e2b-it.md)\n- [Arete-27B vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e4b-it.md)\n- [Arete-27B vs GLM-4.5-Air](https://www.anchorterminal.com/compare/arete-27b-vs-glm-4-5-air.md)\n- [Arete-27B vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/arete-27b-vs-glm-4-7-flash.md)\n- [Arete-27B vs GLM-5.3](https://www.anchorterminal.com/compare/arete-27b-vs-glm-5-3.md)\n- [Arete-27B vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/arete-27b-vs-glm-5-3-flash.md)\n- [Arete-27B vs gpt-oss-120b](https://www.anchorterminal.com/compare/arete-27b-vs-gpt-oss-120b.md)\n- [Arete-27B vs gpt-oss-20b](https://www.anchorterminal.com/compare/arete-27b-vs-gpt-oss-20b.md)\n- [Arete-27B vs Granite 4.2 30B](https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-30b.md)\n- [Arete-27B vs Granite 4.2 3B](https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-3b.md)\n- [Arete-27B vs Granite 4.2 8B](https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-8b.md)\n- [Arete-27B vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/arete-27b-vs-hy-memory-30b-a3b.md)\n- [Arete-27B vs Hy3](https://www.anchorterminal.com/compare/arete-27b-vs-hy3.md)\n- [Arete-27B vs Inkling](https://www.anchorterminal.com/compare/arete-27b-vs-inkling.md)\n- [Arete-27B vs Inkling-Small](https://www.anchorterminal.com/compare/arete-27b-vs-inkling-small.md)\n- [Arete-27B vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/arete-27b-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Arete-27B vs Kimi K3](https://www.anchorterminal.com/compare/arete-27b-vs-kimi-k3.md)\n- [Arete-27B vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/arete-27b-vs-kimi-k3-1-pro.md)\n- [Arete-27B vs Kolibri-1](https://www.anchorterminal.com/compare/arete-27b-vs-kolibri-1.md)\n- [Arete-27B vs Kronos](https://www.anchorterminal.com/compare/arete-27b-vs-kronos.md)\n- [Arete-27B vs Laguna M.1](https://www.anchorterminal.com/compare/arete-27b-vs-laguna-m-1.md)\n- [Arete-27B vs Laguna S 2.1](https://www.anchorterminal.com/compare/arete-27b-vs-laguna-s-2-1.md)\n- [Arete-27B vs Laguna XS 2.1](https://www.anchorterminal.com/compare/arete-27b-vs-laguna-xs-2-1.md)\n- [Arete-27B vs LBS-27B](https://www.anchorterminal.com/compare/arete-27b-vs-lbs-27b.md)\n- [Arete-27B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/arete-27b-vs-lfm2-5-2-6b.md)\n- [Arete-27B vs LFM3-1.7B](https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-1-7b.md)\n- [Arete-27B vs LFM3-Audio-2B](https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-audio-2b.md)\n- [Arete-27B vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-omni-2b.md)\n- [Arete-27B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-vl-2b.md)\n- [Arete-27B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/arete-27b-vs-ling-3-0-tiny.md)\n- [Arete-27B vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/arete-27b-vs-ling-3-1-flash-1019.md)\n- [Arete-27B vs LongCat-2.5](https://www.anchorterminal.com/compare/arete-27b-vs-longcat-2-5.md)\n- [Arete-27B vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/arete-27b-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Arete-27B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/arete-27b-vs-mimo-v2-6-flash-rl.md)\n- [Arete-27B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-o-4-5.md)\n- [Arete-27B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-v-4-7-1b.md)\n- [Arete-27B vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-v-4-7-35b-a3b.md)\n- [Arete-27B vs MiniCPM5-2B](https://www.anchorterminal.com/compare/arete-27b-vs-minicpm5-2b.md)\n- [Arete-27B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/arete-27b-vs-minimax-m2-5.md)\n- [Arete-27B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/arete-27b-vs-minimax-m2-7.md)\n- [Arete-27B vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/arete-27b-vs-minimax-m3-1-flash.md)\n- [Arete-27B vs Mistral Large 4](https://www.anchorterminal.com/compare/arete-27b-vs-mistral-large-4.md)\n- [Arete-27B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/arete-27b-vs-muse-glimmer-30b.md)\n- [Arete-27B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/arete-27b-vs-nanbeige-4-2-3b.md)\n- [Arete-27B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/arete-27b-vs-nanbeige-4-3-33b.md)\n- [Arete-27B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Arete-27B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Arete-27B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-nano-30b-a3b.md)\n- [Arete-27B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-nano-4b.md)\n- [Arete-27B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Arete-27B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-super-120b-a12b.md)\n- [Arete-27B vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-ultra-550b-a55b.md)\n- [Arete-27B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/arete-27b-vs-north-mini-code-1-0.md)\n- [Arete-27B vs occamy-1.0](https://www.anchorterminal.com/compare/arete-27b-vs-occamy-1-0.md)\n- [Arete-27B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/arete-27b-vs-olmo-3-7b-think.md)\n- [Arete-27B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-122b.md)\n- [Arete-27B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-35b-a3b.md)\n- [Arete-27B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-397b.md)\n- [Arete-27B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-9b.md)\n- [Arete-27B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/arete-27b-vs-otel-2-0-llm-31b-it.md)\n- [Arete-27B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen-agentworld-35b-a3b.md)\n- [Arete-27B vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Arete-27B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-4b-instruct-2507.md)\n- [Arete-27B vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-5-0-8b.md)\n- [Arete-27B vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-5-122b-a10b.md)\n- [Arete-27B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-5-2b.md)\n- [Arete-27B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-5-397b-a17b.md)\n- [Arete-27B vs Qwen3.5-4B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-5-4b.md)\n- [Arete-27B vs Qwen3.5-9B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-5-9b.md)\n- [Arete-27B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-6-27b.md)\n- [Arete-27B vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-6-35b-a3b.md)\n- [Arete-27B vs Qwen3.8-27B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-8-27b.md)\n- [Arete-27B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-8-flash-next.md)\n- [Arete-27B vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Arete-27B vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-coder-next.md)\n- [Arete-27B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Arete-27B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-2b-instruct.md)\n- [Arete-27B vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-30b-a3b-instruct.md)\n- [Arete-27B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-32b-instruct.md)\n- [Arete-27B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-4b-instruct.md)\n- [Arete-27B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-8b-instruct.md)\n- [Arete-27B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/arete-27b-vs-qwopus3-8-27b-flash-v2.md)\n- [Arete-27B vs Recursive Flint](https://www.anchorterminal.com/compare/arete-27b-vs-recursive-flint.md)\n- [Arete-27B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/arete-27b-vs-ripple-65b-preview.md)\n- [Arete-27B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/arete-27b-vs-sensenova-loop-9b-preview.md)\n- [Arete-27B vs SmolLM3-3B](https://www.anchorterminal.com/compare/arete-27b-vs-smollm3-3b.md)\n- [Arete-27B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/arete-27b-vs-spark-x2-6-4b.md)\n- [Arete-27B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/arete-27b-vs-step-3-7-flash.md)\n- [Arete-27B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/arete-27b-vs-swift-1-5-qwen3-8-27b.md)\n- [Arete-27B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/arete-27b-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Arete-27B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/arete-27b-vs-thinkingcap-qwen3-8-27b.md)\n- [Arete-27B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/arete-27b-vs-unlimited-ocr-2.md)\n- [Arete-27B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-2b.md)\n- [Arete-27B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-4b.md)\n- [Arete-27B vs ZAYA1-8B](https://www.anchorterminal.com/compare/arete-27b-vs-zaya1-8b.md)\n- [BigBang-v1 vs Darwin-36B-Opus](https://www.anchorterminal.com/compare/bigbang-v1-vs-darwin-36b-opus.md)\n- [Carbon-X vs Darwin-36B-Opus](https://www.anchorterminal.com/compare/carbon-x-vs-darwin-36b-opus.md)\n- [Cola2 vs Darwin-36B-Opus](https://www.anchorterminal.com/compare/cola2-vs-darwin-36b-opus.md)\n- [Cosmos-Reason2-2B vs Darwin-36B-Opus](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-darwin-36b-opus.md)\n- [Darwin-36B-Opus vs DeepSeek-V3.2](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v3-2.md)\n- [Darwin-36B-Opus vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v4-1-flash.md)\n- [Darwin-36B-Opus vs DeepSeek-V4-Flash-0731](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v4-flash-0731.md)\n- [Darwin-36B-Opus vs Devstral Small 2](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-devstral-small-2.md)\n- [Darwin-36B-Opus vs DiffusionGemma 26B A4B IT](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-diffusiongemma-26b-a4b-it.md)\n- [Darwin-36B-Opus vs EXAONE 4.5 33B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-exaone-4-5-33b.md)\n- [Darwin-36B-Opus vs Gemma 4 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 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