{ "data": { "a": { "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" }, "answer": "Qwen3.5-397B-A17B scores 64.1 (B) 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": "qwen3-5-397b-a17b", "name": "Qwen3.5-397B-A17B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3.5-397B-A17B is Alibaba's largest open-weight Qwen3.5 model, a mixture-of-experts vision-language model of 397B parameters with 17B active, thinking on by default and a 262,144-token context, published on Hugging Face in BF16, FP8 and GPTQ 4-bit under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-5-397b-a17b.json", "dns": "qwen3-5-397b-a17b.grade.anchorterminal.com", "repo": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3.5-397B-A17B" }, { "registry": "huggingface", "name": "Qwen/Qwen3.5-397B-A17B-FP8" }, { "registry": "huggingface", "name": "Qwen/Qwen3.5-397B-A17B-GPTQ-Int4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the three repositories is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. The card says Qwen3.5-Plus on Alibaba Cloud Model Studio is the hosted version of this model, and several inference providers serve it per token. Running it yourself costs the hardware, 806.8 GB for the BF16 files, 406.2 GB in FP8 and 235.8 GB for Alibaba's GPTQ 4-bit build (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "large", "multimodal", "reasoning", "tool-calling", "apache-2.0", "safetensors", "fp8", "gptq", "self-hosted" ], "lastRelease": "2026-02-16", "graded": true, "anchor": { "graded": true, "score": 64.1, "grade": "B", "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": 17, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 72, "interface": 70, "reach": 70, "risk": 55, "upkeep": 34 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-02" }, "negative": 0, "verdict": "Alibaba's biggest open Qwen3.5, a cousin for hard coding, agent and vision work. Even at 4 bits it needs about 270 GB, so most agents will call it through Alibaba's Model Studio or one of the many providers that host it. The main risk is silence, with no safety results, no agent guidance and template reports without a reply.", "bestFor": "Agents with access to an 8-GPU server, or calling a hosted endpoint, that want the strongest open Qwen3.5 with vision and a plain licence.", "strengths": [ "Released support in llama.cpp, vLLM, SGLang, Transformers and LM Studio", "Alibaba's own FP8 and GPTQ 4-bit builds", "Plain Apache 2.0 with no gate, no account and no use policy", "Safetensors only, with a checksum on every file in the publisher's own organisation", "262,144 tokens native, several tool calls per turn and thinking that turns off per request", "Hosted by Alibaba as Qwen3.5-Plus and by at least five other providers" ], "weaknesses": [ "No build fits a 256 GB machine, and Ollama has no local build", "No safety results, agent-use guidance or reporting route for flaws", "No reply from Alibaba to any of the 40 most recent discussions, including template bug reports", "Tool format is only in the template and structured output isn't documented", "No limitations, knowledge cut-off or training data sources on the card" ], "agentNotes": [ "The card's server command is `vllm serve Qwen/Qwen3.5-397B-A17B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, and the FP8 build halves the memory", "It doesn't fit our 128 GB reference machine at any precision, so call a hosted endpoint instead (Model Studio's Qwen3.5-Plus, or a provider serving Qwen/Qwen3.5-397B-A17B)", "On Model Studio pass `\"enable_thinking\": false` directly, while vLLM and SGLang take `\"chat_template_kwargs\": {\"enable_thinking\": false}`", "Sampling per the card is temperature 0.6, top_p 0.95 and top_k 20 when thinking, and temperature 0.7, top_p 0.8, top_k 20 and presence_penalty 1.5 without", "For multi-token prediction in vLLM add `--speculative-config '{\"method\":\"qwen3_next_mtp\",\"num_speculative_tokens\":2}'`", "Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens", "Validate any JSON it returns, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 64.1 } ], "editorialScores": { "capability": 72, "interface": 70, "reach": 70, "risk": 55, "upkeep": 34 }, "provenanceScore": 54, "checked": { "categories": { "capability": "2026-11-02", "interface": "2026-11-02", "reach": "2026-11-02", "risk": "2026-11-02", "upkeep": "2026-11-02" }, "last": "2026-11-02", "lastMoved": "2026-11-02", "oldest": "2026-11-02", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# The card's vLLM command, tensor parallel on 8 GPUs:\nvllm serve Qwen/Qwen3.5-397B-A17B-FP8 --port 8000 --tensor-parallel-size 8 --max-model-len 262144 \\\n --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"Qwen/Qwen3.5-397B-A17B-FP8\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen3-5-397b-a17b" }, "sameCompany": [ "qwen3-embedding-0-6b", "qwen3-embedding-4b", "qwen3-embedding-8b", "qwen3-vl-embedding-2b", "qwen3-vl-embedding-8b", "qwen3-asr-0-6b", "qwen3-asr-1-7b", "qwen3-tts-1-7b-customvoice", "qwen-agentworld-35b-a3b", "qwen3-30b-a3b-instruct-2507", "qwen3-4b-instruct-2507", "qwen3-coder-30b-a3b-instruct", "qwen3-coder-next", "qwen3-omni-30b-a3b-instruct", "qwen3-vl-2b-instruct", "qwen3-vl-30b-a3b-instruct", "qwen3-vl-32b-instruct", "qwen3-vl-4b-instruct", "qwen3-vl-8b-instruct", "qwen3-5-0-8b", "qwen3-5-122b-a10b", "qwen3-5-2b", "qwen3-5-4b", "qwen3-5-9b", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "params": "397B total, 17B active", "context": 262144, "goodFor": "Alibaba's largest open Qwen3.5, a mixture-of-experts vision-language model for coding, agents and tool calling on a multi-GPU server, thinking on by default.", "runtimes": [ "llama.cpp", "LM Studio", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Qwen3.5-397B-A17B-FP8", "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 406.2, "memoryGB": 512, "format": "safetensors" }, { "name": "Qwen3.5-397B-A17B-GPTQ-Int4", "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B-GPTQ-Int4", "who": "publisher", "precision": "gptq-int4", "sizeGB": 235.8, "memoryGB": 512, "format": "safetensors", "note": "The card serves it with `--quantization moe_wna16` in vLLM and SGLang" }, { "name": "lmstudio-community Qwen3.5-397B-A17B-MLX-4bit", "url": "https://huggingface.co/lmstudio-community/Qwen3.5-397B-A17B-MLX-4bit", "who": "lmstudio", "precision": "mlx-4bit", "sizeGB": 223.9, "memoryGB": 512, "format": "mlx", "runsOn": [ "LM Studio (Apple silicon)" ], "note": "The smallest build at 4 bits or better from the publisher or a runtime's library, summed from the Hub's file list. LM Studio's catalogue entry states a minimum of 230 GB of system memory" }, { "name": "lmstudio-community Qwen3.5-397B-A17B-GGUF Q4_K_M", "url": "https://huggingface.co/lmstudio-community/Qwen3.5-397B-A17B-GGUF", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 241.5, "memoryGB": 512, "format": "gguf", "runsOn": [ "llama.cpp", "LM Studio" ], "note": "240.6 GB of Q4_K_M weights plus a 0.9 GB BF16 vision projector, summed from the Hub's file list" }, { "name": "Qwen3.5-397B-A17B", "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B", "who": "publisher", "precision": "bf16", "sizeGB": 806.8, "memoryGB": 1024, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve Qwen/Qwen3.5-397B-A17B-FP8 --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "Qwen3.5-397B-A17B-FP8", "checked": "2026-10-21", "minMemoryGB": 512 }, "family": { "lab": "Alibaba (Qwen)", "family": "Qwen3", "url": "https://www.anchorterminal.com/open-weights/#qwen", "others": [ { "name": "Qwen3-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-0.6B", "params": "0.6B" }, { "name": "Qwen3-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-1.7B", "params": "1.7B" }, { "name": "Qwen3-14B", "url": "https://huggingface.co/Qwen/Qwen3-14B", "params": "14B" }, { "name": "Qwen3-235B-A22B", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B", "params": "30B-A3B" }, { "name": "Qwen3-32B", "url": "https://huggingface.co/Qwen/Qwen3-32B", "params": "32B" }, { "name": "Qwen3-4B", "url": "https://huggingface.co/Qwen/Qwen3-4B", "params": "4B" }, { "name": "Qwen3-8B", "url": "https://huggingface.co/Qwen/Qwen3-8B", "params": "8B" }, { "name": "Qwen3-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-14B-Base", "url": "https://huggingface.co/Qwen/Qwen3-14B-Base", "params": "14B" }, { "name": "Qwen3-30B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Base", "params": "30B-A3B" }, { "name": "Qwen3-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3-4B-Base", "params": "4B" }, { "name": "Qwen3-8B-Base", "url": "https://huggingface.co/Qwen/Qwen3-8B-Base", "params": "8B" }, { "name": "Qwen3-Reranker-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-0.6B", "params": "0.6B" }, { "name": "Qwen3-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-8B", "params": "8B" }, { "name": "Qwen3-Embedding-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-0-6b" }, { "name": "Qwen3-Embedding-4B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-4b" }, { "name": "Qwen3-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-8b" }, { "name": "Qwen3-Reranker-4B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-4B", "params": "4B" }, { "name": "Qwen3-235B-A22B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507", "params": "235B-A22B" }, { "name": "Qwen3-Coder-480B-A35B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct", "params": "480B-A35B" }, { "name": "Qwen3-235B-A22B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-30b-a3b-instruct-2507" }, { "name": "Qwen3-30B-A3B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507", "params": "30B-A3B" }, { "name": "Qwen3-Coder-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct" }, { "name": "Qwen3-4B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507" }, { "name": "Qwen3-4B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507", "params": "4B" }, { "name": "Qwen3-Next-80B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct", "params": "80B-A3B" }, { "name": "Qwen3-Next-80B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking", "params": "80B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Captioner", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Captioner", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-omni-30b-a3b-instruct" }, { "name": "Qwen3-VL-235B-A22B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct", "params": "235B-A22B" }, { "name": "Qwen3-VL-235B-A22B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Thinking", "params": "235B-A22B" }, { "name": "Qwen3Guard-Gen-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Gen-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-4B", "params": "4B" }, { "name": "Qwen3Guard-Gen-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-8B", "params": "8B" }, { "name": "Qwen3Guard-Stream-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Stream-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-4B", "params": "4B" }, { "name": "Qwen3Guard-Stream-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-8B", "params": "8B" }, { "name": "Qwen3-4B-SafeRL", "url": "https://huggingface.co/Qwen/Qwen3-4B-SafeRL", "params": "4B" }, { "name": "Qwen3-VL-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct" }, { "name": "Qwen3-VL-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-VL-4B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct" }, { "name": "Qwen3-VL-4B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Thinking", "params": "4B" }, { "name": "Qwen3-VL-8B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct" }, { "name": "Qwen3-VL-8B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking", "params": "8B" }, { "name": "Qwen3-VL-2B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-2b-instruct" }, { "name": "Qwen3-VL-2B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Thinking", "params": "2B" }, { "name": "Qwen3-VL-32B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct", "params": "32B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct" }, { "name": "Qwen3-VL-32B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Thinking", "params": "32B" }, { "name": "Qwen3-VL-Embedding-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-2b" }, { "name": "Qwen3-VL-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-8b" }, { "name": "Qwen3-VL-Reranker-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-2B", "params": "2B" }, { "name": "Qwen3-VL-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-8B", "params": "8B" }, { "name": "Qwen3-TTS-12Hz-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-0.6B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-TTS-12Hz-1.7B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-tts-1-7b-customvoice" }, { "name": "Qwen3-TTS-12Hz-1.7B-VoiceDesign", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign", "params": "1.7B" }, { "name": "Qwen3-TTS-Tokenizer-12Hz", "url": "https://huggingface.co/Qwen/Qwen3-TTS-Tokenizer-12Hz", "params": "not in name" }, { "name": "Qwen3-ASR-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-0-6b" }, { "name": "Qwen3-ASR-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-1.7B", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-1-7b" }, { "name": "Qwen3-ForcedAligner-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ForcedAligner-0.6B", "params": "0.6B" }, { "name": "Qwen3-Coder-Next", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next", "params": "not in name; Hub safetensors total 79.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-next" }, { "name": "Qwen3-Coder-Next-Base", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next-Base", "params": "not in name; Hub safetensors total 79.7B" }, { "name": "WebWorld-14B", "url": "https://huggingface.co/Qwen/WebWorld-14B", "params": "14B" }, { "name": "WebWorld-32B", "url": "https://huggingface.co/Qwen/WebWorld-32B", "params": "32B" }, { "name": "WebWorld-8B", "url": "https://huggingface.co/Qwen/WebWorld-8B", "params": "8B" }, { "name": "Qwen3.5-122B-A10B", "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B", "params": "122B-A10B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b" }, { "name": "Qwen3.5-27B", "url": "https://huggingface.co/Qwen/Qwen3.5-27B", "params": "27B" }, { "name": "Qwen3.5-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.5-35B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B-Base", "params": "35B-A3B" }, { "name": "Qwen3.5-9B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-9B-Base", "params": "9B" }, { "name": "Qwen3.5-4B", "url": "https://huggingface.co/Qwen/Qwen3.5-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-4b" }, { "name": "Qwen3.5-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-4B-Base", "params": "4B" }, { "name": "Qwen3.5-9B", "url": "https://huggingface.co/Qwen/Qwen3.5-9B", "params": "9B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-9b" }, { "name": "Qwen3.5-0.8B", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B", "params": "0.8B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-0-8b" }, { "name": "Qwen3.5-0.8B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B-Base", "params": "0.8B" }, { "name": "Qwen3.5-2B", "url": "https://huggingface.co/Qwen/Qwen3.5-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-2b" }, { "name": "Qwen3.5-2B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-2B-Base", "params": "2B" }, { "name": "Qwen3.6-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B", "params": "35B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b" }, { "name": "Qwen3.6-27B", "url": "https://huggingface.co/Qwen/Qwen3.6-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-27b" }, { "name": "Qwen-AgentWorld-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.8-27B", "url": "https://huggingface.co/Qwen/Qwen3.8-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-27b" }, { "name": "Qwen3.8-2.4T-A95B", "url": "https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B", "params": "2.4T-A95B" }, { "name": "Qwen3.8-Flash-Next", "url": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next", "params": "125B total, 6B active, plus a 51B n-gram embedding table (publisher card); Hub safetensors total 180.0B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-flash-next" }, { "name": "Qwen-Drive-1.0-4B", "url": "https://huggingface.co/Qwen/Qwen-Drive-1.0-4B", "params": "4B" } ], "olderCount": 109, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Alibaba Cloud (named in the licence's copyright line)", "domain": "qwen.ai", "domainRegistered": "2023-08-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The LICENSE file reads Copyright 2026 Alibaba Cloud. The domain's registration date is per the 20 October 2026 RDAP check for qwen3-8-27b.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog for the files. The commit history's messages are uploads, README updates and two merged pull requests (a KTransformers guide on 20 February and a corrected medical VQA score on 15 March 2026), the last commit on 24 April 2026. The News list in github.com/QwenLM/Qwen3.5 dates the release to 16 February 2026.", "qwen.ai answers /.well-known/security.txt with a redirect to its home page, so there is no security.txt." ], "score": 54 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b.json", "live": { "slug": "qwen3-5-397b-a17b", "securityTxt": { "url": "https://qwen.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:09:18.413964975Z" }, "domain": { "domain": "qwen.ai", "registered": "2023-08-27", "source": "https://rdap.identitydigital.services/rdap/domain/qwen.ai", "checkedAt": "2026-11-08T15:07:12.468678256Z" }, "updatedAt": "2026-11-10T16:09:18.413964975Z" } }, "edges": [ { "by": 40, "category": "upkeep", "leads": "otel-2-0-llm-31b-it", "name": "Documentation and upkeep", "reasons": { "a": "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.", "b": "The card covers five of the seven items and misses limitations and a cut-off (14). Training data is a general description without a cut-off (5). The files last changed on 24 April 2026, 192 days before this check, past the 180-day band and within a year (5), and Qwen's GitHub repository holds only a README and licence, so no reference code of Alibaba's changed later. Alibaba answered none of the 40 most recent questions and reports, recounted on 2 November (0), and GitHub issues couldn't be counted. It's a first release at this size (5), and two template reports and a template fix still wait unanswered (5). 14 + 5 + 5 + 0 + 5 + 5 = 34." }, "scores": { "a": 74, "b": 34 } }, { "by": 20, "category": "reach", "leads": "qwen3-5-397b-a17b", "name": "Reach", "reasons": { "a": "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.", "b": "Memory scores 0, since even the smallest 4-bit build, LM Studio's MLX 4-bit at 223.9 GB, needs about 269 GB with a fifth added, and Alibaba's own GPTQ-Int4 about 283 GB. Runtimes reach the 25 cap on released llama.cpp, vLLM, SGLang and Transformers and LM Studio's catalogue, though Ollama has no local build. Alibaba's FP8 and GPTQ-Int4 builds give builds 15. Apache 2.0 with nothing added gives terms 15, the ungated repositories give download 10, and Alibaba's Model Studio plus five live providers on Hugging Face give hosted too its 5." }, "scores": { "a": 50, "b": 70 } }, { "by": 10, "category": "interface", "leads": "qwen3-5-397b-a17b", "name": "Agent interface", "reasons": { "a": "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.", "b": "The template ships in the repository (20) and renders several tool calls per turn for a model trained for tool use (20). Tool-calling format gets 10, because the card names the qwen3_coder parser but leaves the declaration and call format to the template. Thinking turns off per request and comes back in think tags split by the qwen3 parser (10), and 262,144 tokens is native (10). Structured output scores 0, as nothing documents JSON output for the model." }, "scores": { "a": 60, "b": 70 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "AT\u0026T (Open Telco AI)", "b": "Alibaba (Qwen team)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-11-01", "b": "2026-02-16", "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": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Qwen3.5-397B-A17B scores 64.1 (B) 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, OTel-2.0-LLM-31B-IT or Qwen3.5-397B-A17B?" } ], "goodFor": [ { "aheadOn": [ "Documentation and upkeep, 74 against 34" ], "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" }, { "aheadOn": [ "Reach, 70 against 50", "Agent interface, 70 against 60" ], "also": null, "goodFor": "Agents with access to an 8-GPU server, or calling a hosted endpoint, that want the strongest open Qwen3.5 with vision and a plain licence.", "slug": "qwen3-5-397b-a17b", "watchFor": "No build fits a 256 GB machine, and Ollama has no local build" } ], "history": { "otel-2-0-llm-31b-it": null, "qwen3-5-397b-a17b": [ { "causes": [ "time" ], "date": "2026-11-02", "delta": -0.5, "from": 64.6, "fromGrade": "B", "to": 64.1, "toGrade": "B" } ] }, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-otel-2-0-llm-31b-it.json", "title": "Agents-A1-4B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-397b-a17b.json", "title": "Agents-A1-4B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-otel-2-0-llm-31b-it.json", "title": "Apertus v1.5 8B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-397b-a17b.json", "title": "Apertus v1.5 8B vs Qwen3.5-397B-A17B", "url": 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"title": "Granite 4.2 3B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-5-397b-a17b.json", "title": "Granite 4.2 3B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-otel-2-0-llm-31b-it.json", "title": "Granite 4.2 8B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-5-397b-a17b.json", "title": "Granite 4.2 8B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-otel-2-0-llm-31b-it.json", "title": "Hy-Memory-30B-A3B vs OTel-2.0-LLM-31B-IT", "url": 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"https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-5-397b-a17b.json", "title": "Inkling-Small vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-otel-2-0-llm-31b-it.json", "title": "Inkling vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/inkling-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-397b-a17b.json", "title": "Inkling vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-otel-2-0-llm-31b-it.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-5-397b-a17b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-otel-2-0-llm-31b-it.json", "title": "Kimi K3.1 Pro vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-397b-a17b.json", "title": "Kimi K3.1 Pro vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-otel-2-0-llm-31b-it.json", "title": "Kimi K3 vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-397b-a17b.json", "title": "Kimi K3 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-otel-2-0-llm-31b-it.json", "title": "Kolibri-1 vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-397b-a17b.json", "title": "Kolibri-1 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-otel-2-0-llm-31b-it.json", "title": "Kronos vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/kronos-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-5-397b-a17b.json", "title": "Kronos vs Qwen3.5-397B-A17B", "url": 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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" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-397b-a17b.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-5-397b-a17b.json", "title": "Qwen3-30B-A3B-Instruct-2507 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-397b-a17b.json", "title": "Qwen3-4B-Instruct-2507 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-397b-a17b.json", "title": "Qwen3.5-0.8B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-5-397b-a17b.json", "title": "Qwen3.5-122B-A10B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-5-397b-a17b.json", "title": "Qwen3.5-2B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-5-4b.json", "title": "Qwen3.5-397B-A17B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-5-9b.json", "title": "Qwen3.5-397B-A17B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-6-27b.json", "title": "Qwen3.5-397B-A17B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-6-35b-a3b.json", "title": "Qwen3.5-397B-A17B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-8-27b.json", "title": "Qwen3.5-397B-A17B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-8-flash-next.json", "title": "Qwen3.5-397B-A17B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Qwen3.5-397B-A17B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-coder-next.json", "title": "Qwen3.5-397B-A17B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Qwen3.5-397B-A17B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-2b-instruct.json", "title": "Qwen3.5-397B-A17B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3.5-397B-A17B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3.5-397B-A17B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.5-397B-A17B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3.5-397B-A17B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.5-397B-A17B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-recursive-flint.json", "title": "Qwen3.5-397B-A17B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-ripple-65b-preview.json", "title": "Qwen3.5-397B-A17B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-sensenova-loop-9b-preview.json", "title": "Qwen3.5-397B-A17B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-smollm3-3b.json", "title": "Qwen3.5-397B-A17B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-spark-x2-6-4b.json", "title": "Qwen3.5-397B-A17B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-step-3-7-flash.json", "title": "Qwen3.5-397B-A17B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3.5-397B-A17B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.5-397B-A17B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.5-397B-A17B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-unlimited-ocr-2.json", "title": "Qwen3.5-397B-A17B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-wevisone-vl-2b.json", "title": "Qwen3.5-397B-A17B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-wevisone-vl-4b.json", "title": "Qwen3.5-397B-A17B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-zaya1-8b.json", "title": "Qwen3.5-397B-A17B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 20, "edge": "qwen3-5-397b-a17b", "key": "reach", "name": "Reach", "otel-2-0-llm-31b-it": 50, "qwen3-5-397b-a17b": 70, "weight": 25 }, { "by": 0, "edge": "", "key": "risk", "name": "Risk", "otel-2-0-llm-31b-it": 55, "qwen3-5-397b-a17b": 55, "weight": 20 }, { "by": 10, "edge": "qwen3-5-397b-a17b", "key": "interface", "name": "Agent interface", "otel-2-0-llm-31b-it": 60, "qwen3-5-397b-a17b": 70, "weight": 10 }, { "by": 40, "edge": "otel-2-0-llm-31b-it", "key": "upkeep", "name": "Documentation and upkeep", "otel-2-0-llm-31b-it": 74, "qwen3-5-397b-a17b": 34, "weight": 10 } ], "summary": "Qwen3.5-397B-A17B scores 64.1 (B) 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": { "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.", "qwen3-5-397b-a17b": "Alibaba's biggest open Qwen3.5, a cousin for hard coding, agent and vision work. Even at 4 bits it needs about 270 GB, so most agents will call it through Alibaba's Model Studio or one of the many providers that host it. The main risk is silence, with no safety results, no agent guidance and template reports without a reply." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "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-qwen3-5-397b-a17b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-397b-a17b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-397b-a17b.min.md" }, "markdown": "Qwen3.5-397B-A17B scores 64.1 (B) 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- 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- Qwen3.5-397B-A17B: grade B, 64.1/100, rank #23 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-397b-a17b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-397b-a17b.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### 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 34\n\nWatch for: BF16 only at 62.6 GB, with no GGUF or other quantised build from anyone we checked\n\n### Qwen3.5-397B-A17B (B)\n\nGood for: Agents with access to an 8-GPU server, or calling a hosted endpoint, that want the strongest open Qwen3.5 with vision and a plain licence.\n\nAhead on:\n- Reach, 70 against 50\n- Agent interface, 70 against 60\n\nWatch for: No build fits a 256 GB machine, and Ollama has no local build\n\n\n## Why the edge\n\n### Documentation and upkeep: OTel-2.0-LLM-31B-IT +40\n\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- Qwen3.5-397B-A17B (34): The card covers five of the seven items and misses limitations and a cut-off (14). Training data is a general description without a cut-off (5). The files last changed on 24 April 2026, 192 days before this check, past the 180-day band and within a year (5), and Qwen's GitHub repository holds only a README and licence, so no reference code of Alibaba's changed later. Alibaba answered none of the 40 most recent questions and reports, recounted on 2 November (0), and GitHub issues couldn't be counted. It's a first release at this size (5), and two template reports and a template fix still wait unanswered (5). 14 + 5 + 5 + 0 + 5 + 5 = 34.\n\n### Reach: Qwen3.5-397B-A17B +20\n\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- Qwen3.5-397B-A17B (70): Memory scores 0, since even the smallest 4-bit build, LM Studio's MLX 4-bit at 223.9 GB, needs about 269 GB with a fifth added, and Alibaba's own GPTQ-Int4 about 283 GB. Runtimes reach the 25 cap on released llama.cpp, vLLM, SGLang and Transformers and LM Studio's catalogue, though Ollama has no local build. Alibaba's FP8 and GPTQ-Int4 builds give builds 15. Apache 2.0 with nothing added gives terms 15, the ungated repositories give download 10, and Alibaba's Model Studio plus five live providers on Hugging Face give hosted too its 5.\n\n### Agent interface: Qwen3.5-397B-A17B +10\n\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- Qwen3.5-397B-A17B (70): The template ships in the repository (20) and renders several tool calls per turn for a model trained for tool use (20). Tool-calling format gets 10, because the card names the qwen3_coder parser but leaves the declaration and call format to the template. Thinking turns off per request and comes back in think tags split by the qwen3 parser (10), and 262,144 tokens is native (10). Structured output scores 0, as nothing documents JSON output for the model.\n\n## Score by category\n\n| Category | Weight | OTel-2.0-LLM-31B-IT | Qwen3.5-397B-A17B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 50 | 70 | Qwen3.5-397B-A17B +20 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 60 | 70 | Qwen3.5-397B-A17B +10 |\n| Documentation and upkeep | 10% (10 this run) | 74 | 34 | OTel-2.0-LLM-31B-IT +40 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **56.8 · C** | **64.1 · B** | |\n\n## Facts side by side\n\n| Fact | OTel-2.0-LLM-31B-IT | Qwen3.5-397B-A17B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | AT\u0026T (Open Telco AI) | Alibaba (Qwen team) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-01 | 2026-02-16 |\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 | 2.8/5 (4) |\n\n## How the scores have moved\n\n### OTel-2.0-LLM-31B-IT\n\nNo score change since it was listed.\n\n### Qwen3.5-397B-A17B\n\n- -0.5 on 2 November 2026: time\n\n## Verdicts\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**Qwen3.5-397B-A17B.** Alibaba's biggest open Qwen3.5, a cousin for hard coding, agent and vision work. Even at 4 bits it needs about 270 GB, so most agents will call it through Alibaba's Model Studio or one of the many providers that host it. The main risk is silence, with no safety results, no agent guidance and template reports without a reply.\n\n## Before you call either\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### Qwen3.5-397B-A17B\n\n1. The card's server command is `vllm serve Qwen/Qwen3.5-397B-A17B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, and the FP8 build halves the memory\n2. It doesn't fit our 128 GB reference machine at any precision, so call a hosted endpoint instead (Model Studio's Qwen3.5-Plus, or a provider serving Qwen/Qwen3.5-397B-A17B)\n3. On Model Studio pass `\"enable_thinking\": false` directly, while vLLM and SGLang take `\"chat_template_kwargs\": {\"enable_thinking\": false}`\n4. Sampling per the card is temperature 0.6, top_p 0.95 and top_k 20 when thinking, and temperature 0.7, top_p 0.8, top_k 20 and presence_penalty 1.5 without\n5. For multi-token prediction in vLLM add `--speculative-config '{\"method\":\"qwen3_next_mtp\",\"num_speculative_tokens\":2}'`\n6. Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens\n7. Validate any JSON it returns, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, OTel-2.0-LLM-31B-IT or Qwen3.5-397B-A17B?\n\nQwen3.5-397B-A17B scores 64.1 (B) 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/otel-2-0-llm-31b-it-vs-qwen3-5-397b-a17b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-397b-a17b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"otel-2-0-llm-31b-it\", \"b\": \"qwen3-5-397b-a17b\"}`. 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