{ "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 OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in 2 of 4 scored categories. OTel-2.0-LLM-31B-IT leads on documentation and upkeep.", "b": { "slug": "otel-2-0-llm-31b-it", "name": "OTel-2.0-LLM-31B-IT", "vendor": "AT\u0026T (Open Telco AI)", "vendorUrl": "https://github.com/farbodtavakkoli/OTel", "kind": "weights", "category": "open-weights", "summary": "OTel-2.0-LLM-31B-IT is AT\u0026T's open-weight telecoms model, Gemma 4 31B IT post-trained on about 440 billion telecom tokens from GSMA's corpus of standards, for RAG and question answering over 3GPP, ETSI and other specifications, published in BF16 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/otel-2-0-llm-31b-it", "markdownUrl": "https://www.anchorterminal.com/tools/otel-2-0-llm-31b-it.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/otel-2-0-llm-31b-it.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/otel-2-0-llm-31b-it.json", "dns": "otel-2-0-llm-31b-it.grade.anchorterminal.com", "repo": "https://huggingface.co/farbodtavakkoli/OTel-2.0-LLM-31B-IT", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "farbodtavakkoli/OTel-2.0-LLM-31B-IT" } ], "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 as the card's metadata gives it, with Google's Gemma 4 licence page as the link. Running it yourself costs the hardware, 62.6 GB in BF16, which the card puts on two 80 GB GPUs or one of 192 GB (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/farbodtavakkoli/OTel-2.0-LLM-31B-IT", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "fine-tune", "telecom", "domain-specific", "rag", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-11-01", "graded": true, "anchor": { "graded": true, "score": 56.8, "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": 83, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 60, "reach": 50, "risk": 55, "upkeep": 74 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A real telecoms post-training of Gemma 4 31B IT with an unusually candid card on what was and wasn't trained. It fits a 128 GB machine in BF16 only, with no quantised build, sits in a personal account, and its weights have been replaced in place. Tool-call format, structured output and safety results aren't documented.", "bestFor": "Telecoms teams that want an open model for standards questions and RAG on their own hardware", "strengths": [ "The card states what the training covered and left out, and what hasn't been validated", "Training sources and sizes are named, with GSMA as the provider of the corpus", "Apache 2.0, ungated, safetensors only, no code to run", "A full vLLM serving guide with sizing, stop tokens and troubleshooting", "The owner answers questions on the repository" ], "weaknesses": [ "BF16 only at 62.6 GB, with no GGUF or other quantised build from anyone we checked", "The files are in a personal account, not an AT\u0026T or GSMA organisation", "Weights replaced in place three times, and an old shard set left in the repository", "No tool-call format, structured output or thinking", "Post-trained at 4,096 tokens, so long-context use is unproven", "No safety results and no security contact" ], "agentNotes": [ "Download the 15 shards the index names, not the whole repository, which is twice the size", "Pin a revision. The card says the weights may change under the same name", "Serve it with vLLM 0.19.0 or later and the repository's own chat template, not Gemma 4's", "Send no thinking flags. It answers directly and wasn't trained on Gemma's thought channel", "Use it as the generation stage of a RAG pipeline over telecom standards, with source attribution and human review", "Don't use it to run network tools unattended. The card says that hasn't been evaluated" ], "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": 56.8 } ], "editorialScores": { "interface": 60, "reach": 50, "risk": 55, "upkeep": 74 }, "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": "# with vLLM 0.19.0 or later, on two 80 GB GPUs, per the card:\nvllm serve farbodtavakkoli/OTel-2.0-LLM-31B-IT --served-model-name OTel-2.0-LLM-31B-IT \\\n --tensor-parallel-size 2 --max-model-len 8192 --dtype bfloat16 --port 8000", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"OTel-2.0-LLM-31B-IT\", \"messages\": [{\"role\": \"user\", \"content\": \"What model is this?\"}], \"temperature\": 0}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/otel-2-0-llm-31b-it" }, "weights": { "trainedBy": "AT\u0026T Chief Data Office, with GSMA, Red Hat, Microsoft, AMD, Dell, MLCommons and Pleias named as contributors", "builtOn": "google/gemma-4-31b-it (post-trained with OSFT; named on the card and in its metadata)", "params": "31B", "context": 262144, "goodFor": "Telecoms question answering and RAG over 3GPP, ETSI, GSMA, CAMARA, ITU, O-RAN and TM Forum material, in English, as the generation stage with retrieval and human review around it.", "runtimes": [ "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "OTel-2.0-LLM-31B-IT", "url": "https://huggingface.co/farbodtavakkoli/OTel-2.0-LLM-31B-IT", "who": "publisher", "precision": "bf16", "sizeGB": 62.55, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ], "note": "The 15 shards the index names. The repository also holds an earlier 14-shard set of 64.21 GB, so download by the index and not the whole repository. No quantised build from the publisher or the usual converters" } ], "run": "vllm serve farbodtavakkoli/OTel-2.0-LLM-31B-IT --tensor-parallel-size 2 --max-model-len 8192 --dtype bfloat16", "runBuild": "OTel-2.0-LLM-31B-IT", "checked": "2026-11-10", "minMemoryGB": 96 }, "family": { "lab": "AT\u0026T (Open Telco AI)", "family": "OTel 2.0", "url": "https://www.anchorterminal.com/open-weights/#att-open-telco", "others": [ { "name": "OTel-2.0-LLM-31B-IT-QLoRA", "url": "https://huggingface.co/farbodtavakkoli/OTel-2.0-LLM-31B-IT-QLoRA", "params": "31B" }, { "name": "OTel-2.0-Decision-26B-A4B-4bit", "url": "https://huggingface.co/farbodtavakkoli/OTel-2.0-Decision-26B-A4B-4bit", "params": "26B total, 4B active (per name; card not read)" } ] }, "area": "models", "provenance": { "legalEntity": "AT\u0026T (the card and GSMA's newsroom name AT\u0026T as the releaser, and the model answers that AT\u0026T Chief Data Office trained it). Published from a contributor's personal accounts on Hugging Face and GitHub", "domain": "", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/farbodtavakkoli/OTel-2.0-LLM-31B-IT/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "No vendor domain is recorded. The project's pages are a personal GitHub repository and Hugging Face account, and open-telco.ai, the site GSMA's article links, answered 403 on 10 November 2026.", "GSMA's newsroom article of 24 July 2026 names AT\u0026T as the releaser and GSMA as the source of the corpus. It doesn't link the Hugging Face repository.", "The Hub lists the publishing account as a member of the AT\u0026T and GSMA organisations.", "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored.", "The GitHub repository has no SECURITY.md (404 on 10 November 2026)." ], "score": 61 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/otel-2-0-llm-31b-it.json" }, "edges": [ { "by": 30, "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, the publisher's only build is the BF16 safetensors, 62.55 GB for the 15 shards the index names, and 62.55 GB × 1.2 = 75.1 GB, above 64 GB and inside the reference machine's GPU share. Runtimes 15, the architecture is Gemma4ForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors files, three at 5 each. Builds 0, the publisher ships the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata, an OSI licence, with nothing added on the card. Download 10, gated is false on 10 November 2026, so the files download with no account or terms. Hosted 0, Hugging Face maps one inference provider, Featherless AI (live), short of the two the line needs. 10 + 15 + 0 + 15 + 10 + 0 = 50." }, "scores": { "a": 80, "b": 50 } }, { "by": 13, "category": "upkeep", "leads": "otel-2-0-llm-31b-it", "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 14, Intended use, a long limitations section, the architecture and training method, the parameters (31B) and the language (English) are on the card. Training data 15, the card names the sources (3GPP, ETSI, ITU, GSMA, CAMARA, O-RAN and TM Forum material provided by GSMA), the overall sizes (15 billion raw tokens, over 1 trillion processed, about 440 billion used), the mix of example types and the processing tool, and lists what was left out. Last change 20, the last commit, on 1 November 2026, updated the card's links, resources, citation and contacts, nine days before this check. Responsiveness 20, Of the 3 discussions that aren't pull requests (7 August to 28 October 2026), 2 have a reply from the repository's owner, which is most. Version policy 0, the weights were replaced in place: uploads on 27 July, 3 August and 1 September 2026 under one name, with a standing notice on the card that they may change and that users should pin a revision, and no dated note of what changed. File health 5, the card describes 15 shards of about 62.6 GB while the repository still holds an earlier 14-shard set beside them, 126.8 GB in all. 14 + 15 + 20 + 20 + 0 + 5 = 74." }, "scores": { "a": 61, "b": 74 } }, { "by": 10, "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, 19,300 characters, the publisher's own. Tool-calling format 10, only the template says how tools are declared and how calls come back. Trained tool calling 20, the template loops over tool_calls, so it renders several calls in one turn, and the card says the training mix included general-purpose agentic tool-calling examples, while none were specific to telecoms. Structured output 0, the card doesn't document JSON or JSON Schema output. Reasoning control 0, the card says the model wasn't trained to produce or consume Gemma's thought channel and answers directly, so there is no thinking to switch or budget. Context 10, 262,144 positions in the text config, the base's native context. 20 + 10 + 20 + 0 + 0 + 10 = 60." }, "scores": { "a": 70, "b": 60 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "FINAL-Bench (VIDRAFT)", "b": "AT\u0026T (Open Telco AI)", "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": "2026-11-01", "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 OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in 2 of 4 scored categories. OTel-2.0-LLM-31B-IT leads on documentation and upkeep.", "question": "Which is better for AI agents, Darwin-36B-Opus or OTel-2.0-LLM-31B-IT?" } ], "goodFor": [ { "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" }, { "aheadOn": [ "Documentation and upkeep, 74 against 61" ], "also": null, "goodFor": "Telecoms teams that want an open model for standards questions and RAG on their own hardware", "slug": "otel-2-0-llm-31b-it", "watchFor": "BF16 only at 62.6 GB, with no GGUF or other quantised build from anyone we checked" } ], "history": { "darwin-36b-opus": [ { "causes": [ "correction" ], "date": "2026-11-03", "delta": 0, "from": 61, "fromGrade": "C", "to": 61, "toGrade": "C" } ], 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OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-otel-2-0-llm-31b-it.json", "title": "MiniCPM-o 4.5 vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-otel-2-0-llm-31b-it.json", "title": "MiniCPM-V 4.7 1B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-otel-2-0-llm-31b-it.json", "title": "MiniCPM-V 4.7 35B-A3B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-otel-2-0-llm-31b-it.json", "title": "MiniCPM5-2B vs OTel-2.0-LLM-31B-IT", "url": 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"https://www.anchorterminal.com/compare/mistral-large-4-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-otel-2-0-llm-31b-it.json", "title": "Muse Glimmer 30B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-otel-2-0-llm-31b-it.json", "title": "Nanbeige4.2-3B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-otel-2-0-llm-31b-it.json", "title": "Nanbeige4.3-33B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-otel-2-0-llm-31b-it.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-otel-2-0-llm-31b-it.json", "title": "Nemotron 3.5 Lightning 30B A3B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-otel-2-0-llm-31b-it.json", "title": "Nemotron 3 Nano 30B A3B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-otel-2-0-llm-31b-it.json", "title": "Nemotron 3 Nano 4B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-otel-2-0-llm-31b-it.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-otel-2-0-llm-31b-it.json", "title": "Nemotron 3 Super 120B A12B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-otel-2-0-llm-31b-it.json", "title": "Nemotron 3 Ultra 550B A55B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-otel-2-0-llm-31b-it.json", "title": "North Mini Code 1.0 vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-otel-2-0-llm-31b-it.json", "title": "occamy-1.0 vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-otel-2-0-llm-31b-it.json", "title": "Olmo 3 7B Think vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-otel-2-0-llm-31b-it.json", "title": "Ornith-1.5-122B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-otel-2-0-llm-31b-it.json", "title": "Ornith-1.5-35B-A3B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-otel-2-0-llm-31b-it.json", "title": "Ornith-1.5-397B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-otel-2-0-llm-31b-it.json", "title": "Ornith-1.5-9B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-otel-2-0-llm-31b-it" }, { "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/otel-2-0-llm-31b-it-vs-qwen3-30b-a3b-instruct-2507.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-4b-instruct-2507.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-0-8b.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-122b-a10b.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-2b.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-397b-a17b.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-4b.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-9b.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-6-27b.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-6-35b-a3b.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-8-27b.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-8-flash-next.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-coder-30b-a3b-instruct.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-coder-next.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-omni-30b-a3b-instruct.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-2b-instruct.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-30b-a3b-instruct.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-32b-instruct.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-4b-instruct.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-8b-instruct.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwopus3-8-27b-flash-v2.json", "title": "OTel-2.0-LLM-31B-IT vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-recursive-flint.json", "title": "OTel-2.0-LLM-31B-IT vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-ripple-65b-preview.json", "title": "OTel-2.0-LLM-31B-IT vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-sensenova-loop-9b-preview.json", "title": "OTel-2.0-LLM-31B-IT vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-smollm3-3b.json", "title": "OTel-2.0-LLM-31B-IT vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-spark-x2-6-4b.json", "title": "OTel-2.0-LLM-31B-IT vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-step-3-7-flash.json", "title": "OTel-2.0-LLM-31B-IT vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-swift-1-5-qwen3-8-27b.json", "title": "OTel-2.0-LLM-31B-IT vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-swift-1-5-qwen3-8-flash-next.json", "title": "OTel-2.0-LLM-31B-IT vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-thinkingcap-qwen3-8-27b.json", "title": "OTel-2.0-LLM-31B-IT vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-unlimited-ocr-2.json", "title": "OTel-2.0-LLM-31B-IT vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-2b.json", "title": "OTel-2.0-LLM-31B-IT vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-4b.json", "title": "OTel-2.0-LLM-31B-IT vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-zaya1-8b.json", "title": "OTel-2.0-LLM-31B-IT vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 30, "darwin-36b-opus": 80, "edge": "darwin-36b-opus", "key": "reach", "name": "Reach", "otel-2-0-llm-31b-it": 50, "weight": 25 }, { "by": 0, "darwin-36b-opus": 55, "edge": "", "key": "risk", "name": "Risk", "otel-2-0-llm-31b-it": 55, "weight": 20 }, { "by": 10, "darwin-36b-opus": 70, "edge": "darwin-36b-opus", "key": "interface", "name": "Agent interface", "otel-2-0-llm-31b-it": 60, "weight": 10 }, { "by": 13, "darwin-36b-opus": 61, "edge": "otel-2-0-llm-31b-it", "key": "upkeep", "name": "Documentation and upkeep", "otel-2-0-llm-31b-it": 74, "weight": 10 } ], "summary": "Darwin-36B-Opus scores 61 (C) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in 2 of 4 scored categories. OTel-2.0-LLM-31B-IT leads on documentation and upkeep. 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.", "otel-2-0-llm-31b-it": "A real telecoms post-training of Gemma 4 31B IT with an unusually candid card on what was and wasn't trained. It fits a 128 GB machine in BF16 only, with no quantised build, sits in a personal account, and its weights have been replaced in place. Tool-call format, structured output and safety results aren't documented." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-otel-2-0-llm-31b-it", "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-otel-2-0-llm-31b-it.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-otel-2-0-llm-31b-it.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-otel-2-0-llm-31b-it.min.md" }, "markdown": "Darwin-36B-Opus scores 61 (C) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in 2 of 4 scored categories. OTel-2.0-LLM-31B-IT leads on documentation and upkeep. 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- OTel-2.0-LLM-31B-IT: grade C, 56.8/100, rank #111 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/otel-2-0-llm-31b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/otel-2-0-llm-31b-it.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 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### OTel-2.0-LLM-31B-IT (C)\n\nGood for: Telecoms teams that want an open model for standards questions and RAG on their own hardware\n\nAhead on:\n- Documentation and upkeep, 74 against 61\n\nWatch for: BF16 only at 62.6 GB, with no GGUF or other quantised build from anyone we checked\n\n\n## Why the edge\n\n### Reach: Darwin-36B-Opus +30\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- OTel-2.0-LLM-31B-IT (50): Memory 10, the publisher's only build is the BF16 safetensors, 62.55 GB for the 15 shards the index names, and 62.55 GB × 1.2 = 75.1 GB, above 64 GB and inside the reference machine's GPU share. Runtimes 15, the architecture is Gemma4ForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors files, three at 5 each. Builds 0, the publisher ships the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata, an OSI licence, with nothing added on the card. Download 10, gated is false on 10 November 2026, so the files download with no account or terms. Hosted 0, Hugging Face maps one inference provider, Featherless AI (live), short of the two the line needs. 10 + 15 + 0 + 15 + 10 + 0 = 50.\n\n### Documentation and upkeep: OTel-2.0-LLM-31B-IT +13\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- OTel-2.0-LLM-31B-IT (74): Model card 14, Intended use, a long limitations section, the architecture and training method, the parameters (31B) and the language (English) are on the card. Training data 15, the card names the sources (3GPP, ETSI, ITU, GSMA, CAMARA, O-RAN and TM Forum material provided by GSMA), the overall sizes (15 billion raw tokens, over 1 trillion processed, about 440 billion used), the mix of example types and the processing tool, and lists what was left out. Last change 20, the last commit, on 1 November 2026, updated the card's links, resources, citation and contacts, nine days before this check. Responsiveness 20, Of the 3 discussions that aren't pull requests (7 August to 28 October 2026), 2 have a reply from the repository's owner, which is most. Version policy 0, the weights were replaced in place: uploads on 27 July, 3 August and 1 September 2026 under one name, with a standing notice on the card that they may change and that users should pin a revision, and no dated note of what changed. File health 5, the card describes 15 shards of about 62.6 GB while the repository still holds an earlier 14-shard set beside them, 126.8 GB in all. 14 + 15 + 20 + 20 + 0 + 5 = 74.\n\n### Agent interface: Darwin-36B-Opus +10\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- OTel-2.0-LLM-31B-IT (60): Chat template 20, chat_template.jinja in the repository, 19,300 characters, the publisher's own. Tool-calling format 10, only the template says how tools are declared and how calls come back. Trained tool calling 20, the template loops over tool_calls, so it renders several calls in one turn, and the card says the training mix included general-purpose agentic tool-calling examples, while none were specific to telecoms. Structured output 0, the card doesn't document JSON or JSON Schema output. Reasoning control 0, the card says the model wasn't trained to produce or consume Gemma's thought channel and answers directly, so there is no thinking to switch or budget. Context 10, 262,144 positions in the text config, the base's native context. 20 + 10 + 20 + 0 + 0 + 10 = 60.\n\n## Score by category\n\n| Category | Weight | Darwin-36B-Opus | OTel-2.0-LLM-31B-IT | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 80 | 50 | Darwin-36B-Opus +30 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 70 | 60 | Darwin-36B-Opus +10 |\n| Documentation and upkeep | 10% (10 this run) | 61 | 74 | OTel-2.0-LLM-31B-IT +13 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **56.8 · C** | |\n\n## Facts side by side\n\n| Fact | Darwin-36B-Opus | OTel-2.0-LLM-31B-IT |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | FINAL-Bench (VIDRAFT) | AT\u0026T (Open Telco AI) |\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 | 2026-11-01 |\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### OTel-2.0-LLM-31B-IT\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**OTel-2.0-LLM-31B-IT.** A real telecoms post-training of Gemma 4 31B IT with an unusually candid card on what was and wasn't trained. It fits a 128 GB machine in BF16 only, with no quantised build, sits in a personal account, and its weights have been replaced in place. Tool-call format, structured output and safety results aren't documented.\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### OTel-2.0-LLM-31B-IT\n\n1. Download the 15 shards the index names, not the whole repository, which is twice the size\n2. Pin a revision. The card says the weights may change under the same name\n3. Serve it with vLLM 0.19.0 or later and the repository's own chat template, not Gemma 4's\n4. Send no thinking flags. It answers directly and wasn't trained on Gemma's thought channel\n5. Use it as the generation stage of a RAG pipeline over telecom standards, with source attribution and human review\n6. Don't use it to run network tools unattended. The card says that hasn't been evaluated\n\n## Questions\n\n### Which is better for AI agents, Darwin-36B-Opus or OTel-2.0-LLM-31B-IT?\n\nDarwin-36B-Opus scores 61 (C) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in 2 of 4 scored categories. OTel-2.0-LLM-31B-IT leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/darwin-36b-opus-vs-otel-2-0-llm-31b-it.json, and with the fewest tokens: https://www.anchorterminal.com/compare/darwin-36b-opus-vs-otel-2-0-llm-31b-it.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"darwin-36b-opus\", \"b\": \"otel-2-0-llm-31b-it\"}`. From a terminal: `anchor compare darwin-36b-opus otel-2-0-llm-31b-it`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/darwin-36b-opus.json and https://www.anchorterminal.com/api/v1/tools/otel-2-0-llm-31b-it.json\n\n## Other comparisons with Darwin-36B-Opus or OTel-2.0-LLM-31B-IT\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 OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/agents-a1-4b-vs-otel-2-0-llm-31b-it.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 OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-otel-2-0-llm-31b-it.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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