{ "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": "OTel-2.0-LLM-31B-IT and Qwen3-VL-32B-Instruct score within a point of each other on agent readiness, 56.8 (C) and 56.4 (C). Qwen3-VL-32B-Instruct leads on reach.", "b": { "slug": "qwen3-vl-32b-instruct", "name": "Qwen3-VL-32B-Instruct", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3-VL-32B-Instruct is Alibaba's open-weight dense vision-language model of about 32B parameters, taking text, images and video with a 262,144-token context, published on Hugging Face in BF16, FP8 and GGUF under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-vl-32b-instruct.json", "dns": "qwen3-vl-32b-instruct.grade.anchorterminal.com", "repo": "https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3-VL-32B-Instruct" }, { "registry": "huggingface", "name": "Qwen/Qwen3-VL-32B-Instruct-FP8" }, { "registry": "huggingface", "name": "Qwen/Qwen3-VL-32B-Instruct-GGUF" }, { "registry": "ollama", "name": "qwen3-vl:32b-instruct" } ], "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. Running it yourself costs the hardware, 66.73 GB for the BF16 files, 35.53 GB for Alibaba's FP8 build and 19.76 GB plus a 0.77 GB vision file for its Q4_K_M GGUF (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "vision", "video", "tool-calling", "apache-2.0", "safetensors", "fp8", "gguf", "self-hosted" ], "lastRelease": "2025-11-01", "graded": true, "anchor": { "graded": true, "score": 56.4, "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": 86, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 43, "interface": 60, "reach": 85, "risk": 55, "upkeep": 31 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-04" }, "negative": 0, "verdict": "Qwen3-VL-32B-Instruct is the largest dense Qwen3-VL model, for screenshots, documents, charts and video. Alibaba's Q4_K_M GGUF with its vision file needs about 25 GB, and Transformers, vLLM, SGLang, llama.cpp and Ollama run it under plain Apache 2.0. It has no thinking mode or published safety work, and only one provider was found serving it.", "bestFor": "An agent with a 32 GB GPU or more that wants the largest dense Qwen3-VL model for images, documents and video", "strengths": [ "About 25 GB of memory for Alibaba's own Q4_K_M GGUF with its vision file, so a 32 GB machine, with FP8, Q8_0 and F16 builds as well", "Runs in Transformers, vLLM, SGLang, llama.cpp and Ollama in released versions", "Apache 2.0 with no use policy, downloadable with no account", "262,144 tokens of native context over text, images and video", "Safetensors and GGUF only, with a chat template in the repository", "A Qwen3-VL report author answered two early reports on the repository" ], "weaknesses": [ "No safety evaluation, agent-use guidance or security contact", "Tool calling isn't documented beyond the template, and Alibaba names no parser", "No thinking mode in this edition and no documented JSON output", "Qwen staff answered only two of the 11 Hugging Face discussions that aren't pull requests, both in October 2025", "The card leaves out limitations, languages, knowledge cut-off and memory needs", "Only Featherless was found serving it, and OpenRouter lists it with no endpoints", "The FP8 repository held the Thinking edition's files until the day after release, and the swap carries no note" ], "agentNotes": [ "Start it with `llama-server -hf Qwen/Qwen3-VL-32B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:32b-instruct`", "In Ollama the plain `qwen3-vl:32b` tag is the Thinking edition, so name `32b-instruct`", "With the GGUF, load the mmproj vision file too, or images won't be read", "Parse tool calls as JSON inside `\u003ctool_call\u003e` tags, or use SGLang's `--tool-call-parser qwen`", "Use the card's sampling, `temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5` for vision and `temperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0` for text", "Validate JSON output yourself, since structured output isn't documented", "vLLM needs 0.11.0 or later and Transformers 4.57.0 or later", "If you pulled the FP8 build before 22 October 2025, pull it again, since the earlier files were the Thinking edition's" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 56.4 } ], "editorialScores": { "capability": 43, "interface": 60, "reach": 85, "risk": 55, "upkeep": 31 }, "provenanceScore": 54, "checked": { "categories": { "capability": "2026-11-04", "interface": "2026-11-04", "reach": "2026-11-04", "risk": "2026-11-04", "upkeep": "2026-11-04" }, "last": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run qwen3-vl:32b-instruct\n# or with vLLM 0.11.0 or later, per Qwen's README:\nvllm serve Qwen/Qwen3-VL-32B-Instruct-FP8\n# or with SGLang, using the tool parser its cookbook names for Qwen3-VL:\npython -m sglang.launch_server --model-path Qwen/Qwen3-VL-32B-Instruct --tool-call-parser qwen", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"qwen3-vl:32b-instruct\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen3-vl-32b-instruct" }, "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-4b-instruct", "qwen3-vl-8b-instruct", "qwen3-5-0-8b", "qwen3-5-122b-a10b", "qwen3-5-2b", "qwen3-5-397b-a17b", "qwen3-5-4b", "qwen3-5-9b", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "params": "33.36B", "context": 262144, "goodFor": "The largest dense Qwen3-VL model, for images, documents and video with text out, about 21 GB at 4-bit, no thinking mode in this Instruct edition.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Qwen3VL-32B-Instruct-Q4_K_M", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct-GGUF/blob/main/Qwen3VL-32B-Instruct-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 20.53, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "19.76 GB language file plus mmproj-Qwen3VL-32B-Instruct-Q8_0.gguf (0.77 GB) for image input" }, { "name": "ollama qwen3-vl:32b-instruct", "url": "https://ollama.com/library/qwen3-vl:32b-instruct", "who": "ollama", "precision": "q4_k_m", "sizeGB": 21, "memoryGB": 32, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M with its vision projector (digest c9a60b1a1a13, unchanged for 11 months). qwen3-vl:32b, the library default, is the Thinking edition. Size as Ollama's tags page shows it" }, { "name": "Qwen3-VL-32B-Instruct-FP8", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 35.53, "memoryGB": 48, "format": "safetensors" }, { "name": "Qwen3VL-32B-Instruct-Q8_0", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct-GGUF/blob/main/Qwen3VL-32B-Instruct-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 35.59, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Size includes mmproj-Qwen3VL-32B-Instruct-Q8_0.gguf (0.77 GB), the vision file" }, { "name": "Qwen3-VL-32B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct", "who": "publisher", "precision": "bf16", "sizeGB": 66.73, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] }, { "name": "Qwen3VL-32B-Instruct-F16", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct-GGUF/blob/main/Qwen3VL-32B-Instruct-F16-split-00001-of-00002.gguf", "who": "publisher", "precision": "f16", "sizeGB": 66.73, "memoryGB": 96, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Size includes mmproj-Qwen3VL-32B-Instruct-F16.gguf (1.2 GB), the vision file. The language model is split in two files" } ], "run": "llama-server -hf Qwen/Qwen3-VL-32B-Instruct-GGUF:Q4_K_M --jinja -c 32768", "runBuild": "Qwen3VL-32B-Instruct-Q4_K_M", "checked": "2026-10-21", "minMemoryGB": 32 }, "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-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": "Qwen3.5-397B-A17B", "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B", "params": "397B-A17B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b" }, { "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 (per the first batch's RDAP check of qwen.ai; this model's files name no copyright holder)", "domain": "qwen.ai", "domainRegistered": "2023-08-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The weights repositories have no LICENSE file and the GitHub repository's Apache 2.0 LICENSE leaves the copyright line as the template. The legal entity and domain date come from the first batch's check of qwen.ai on 20 October 2026; RDAP wasn't queried again.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog. The GitHub README has a dated news list of releases, and the Hugging Face commit messages are README updates and uploads that don't say what changed.", "qwen.ai/.well-known/security.txt redirects to the home page, so there is no security.txt." ], "score": 54 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct.json", "live": { "slug": "qwen3-vl-32b-instruct", "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": 43, "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 misses limitations, languages and a cut-off (11). Training data is described only in the technical report, with no cut-off (5). The last change to code that runs it was the qwen-vl-utils fix of 4 January 2026, and the weights haven't changed since November 2025; the Hub commits for all three repositories and the QwenLM/Qwen3-VL head (30 January 2026) were the same on 4 November (5). Jie Huang, a Qwen organisation member, a named author of the Qwen3-VL Technical Report and the author of nine commits in QwenLM/Qwen3-VL, answered two of the 11 Hugging Face discussions that aren't pull requests, both on 27 October 2025; no publisher reply since, and GitHub issues weren't counted (5). The FP8 build's files were replaced in place the day after release with no note, after it had shipped the Thinking edition's files for three days (0). The GGUF card's example names a file the repository doesn't hold (5). 11+5+5+5+0+5 = 31." }, "scores": { "a": 74, "b": 31 } }, { "by": 35, "category": "reach", "leads": "qwen3-vl-32b-instruct", "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": "Alibaba's own Q4_K_M GGUF is 19.76 GB and 20.53 GB with the vision file it needs for images, so with a fifth added it needs about 25 GB and the 32 GB band (20). Transformers 4.57.0, vLLM, SGLang, llama.cpp and Ollama all run it in released versions, which reaches the 25 cap. Alibaba publishes FP8 and GGUF builds at F16, Q8_0 and Q4_K_M (15). Apache 2.0 with nothing added (15). No gate and no account (10). On 4 November the Hub's provider mapping lists only Featherless, live, and OpenRouter listed it with no endpoints per the 21 October check, so it isn't hosted too (0). 20+25+15+15+10+0 = 85." }, "scores": { "a": 50, "b": 85 } } ], "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": "2025-11-01", "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": "none", "b": "2.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "OTel-2.0-LLM-31B-IT and Qwen3-VL-32B-Instruct score within a point of each other on agent readiness, 56.8 (C) and 56.4 (C). Qwen3-VL-32B-Instruct leads on reach.", "question": "Which is better for AI agents, OTel-2.0-LLM-31B-IT or Qwen3-VL-32B-Instruct?" } ], "goodFor": [ { "aheadOn": [ "Documentation and upkeep, 74 against 31" ], "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, 85 against 50" ], "also": null, "goodFor": "An agent with a 32 GB GPU or more that wants the largest dense Qwen3-VL model for images, documents and video", "slug": "qwen3-vl-32b-instruct", "watchFor": "No safety evaluation, agent-use guidance or security contact" } ], "history": null, "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-vl-32b-instruct.json", "title": "Agents-A1-4B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-vl-32b-instruct" }, { "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-vl-32b-instruct.json", "title": "Apertus v1.5 8B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-otel-2-0-llm-31b-it.json", "title": "Apodex-1.1-mini vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-vl-32b-instruct.json", "title": "Apodex-1.1-mini vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-otel-2-0-llm-31b-it.json", "title": "Arete-27B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-32b-instruct.json", "title": "Arete-27B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-otel-2-0-llm-31b-it.json", "title": "BigBang-v1 vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-vl-32b-instruct.json", "title": "BigBang-v1 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-otel-2-0-llm-31b-it.json", "title": "Carbon-X vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-vl-32b-instruct.json", "title": "Carbon-X vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-otel-2-0-llm-31b-it.json", "title": "Cola2 vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/cola2-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-vl-32b-instruct.json", "title": "Cola2 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-otel-2-0-llm-31b-it.json", "title": "Cosmos-Reason2-2B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-vl-32b-instruct.json", "title": "Cosmos-Reason2-2B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-otel-2-0-llm-31b-it.json", "title": "Darwin-36B-Opus vs OTel-2.0-LLM-31B-IT", "url": "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-qwen3-vl-32b-instruct.json", "title": "Darwin-36B-Opus vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-otel-2-0-llm-31b-it.json", "title": "DeepSeek-V3.2 vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-vl-32b-instruct.json", "title": "DeepSeek-V3.2 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-otel-2-0-llm-31b-it.json", "title": "DeepSeek V4.1 Flash vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-32b-instruct.json", "title": "DeepSeek V4.1 Flash vs Qwen3-VL-32B-Instruct", "url": 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Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-otel-2-0-llm-31b-it.json", "title": "DiffusionGemma 26B A4B IT vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-vl-32b-instruct.json", "title": "DiffusionGemma 26B A4B IT vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-otel-2-0-llm-31b-it.json", "title": "EXAONE 4.5 33B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-vl-32b-instruct.json", "title": 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"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": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-32b-instruct.json", "title": "Hy-Memory-30B-A3B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-otel-2-0-llm-31b-it.json", "title": "Hy3 vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/hy3-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-32b-instruct.json", "title": "Hy3 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-otel-2-0-llm-31b-it.json", "title": 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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-vl-32b-instruct.json", "title": "Kimi K3 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-32b-instruct" }, { "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-vl-32b-instruct.json", "title": "Kolibri-1 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-32b-instruct" }, { "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": 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"https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-otel-2-0-llm-31b-it.json", "title": "LFM3-VL-2B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-32b-instruct.json", "title": "LFM3-VL-2B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-otel-2-0-llm-31b-it.json", "title": "Ling-3.0-tiny vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-32b-instruct.json", "title": "Ling-3.0-tiny vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-32b-instruct" }, { "json": 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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": 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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-vl-32b-instruct.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3-30B-A3B-Instruct-2507 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3-4B-Instruct-2507 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3.5-0.8B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3.5-122B-A10B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3.5-2B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-32b-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/qwen3-5-4b-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3.5-4B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3.5-9B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3.6-27B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3.6-35B-A3B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3.8-27B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3.8-Flash-Next vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3-Coder-Next vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3-VL-2B-Instruct vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3-VL-32B-Instruct vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3-VL-32B-Instruct vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-VL-32B-Instruct vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-recursive-flint.json", "title": "Qwen3-VL-32B-Instruct vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-VL-32B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-sensenova-loop-9b-preview.json", "title": "Qwen3-VL-32B-Instruct vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-smollm3-3b.json", "title": "Qwen3-VL-32B-Instruct vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-spark-x2-6-4b.json", "title": "Qwen3-VL-32B-Instruct vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-step-3-7-flash.json", "title": "Qwen3-VL-32B-Instruct vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3-VL-32B-Instruct vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3-VL-32B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-VL-32B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-unlimited-ocr-2.json", "title": "Qwen3-VL-32B-Instruct vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-wevisone-vl-2b.json", "title": "Qwen3-VL-32B-Instruct vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-wevisone-vl-4b.json", "title": "Qwen3-VL-32B-Instruct vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-zaya1-8b.json", "title": "Qwen3-VL-32B-Instruct vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 35, "edge": "qwen3-vl-32b-instruct", "key": "reach", "name": "Reach", "otel-2-0-llm-31b-it": 50, "qwen3-vl-32b-instruct": 85, "weight": 25 }, { "by": 0, "edge": "", "key": "risk", "name": "Risk", "otel-2-0-llm-31b-it": 55, "qwen3-vl-32b-instruct": 55, "weight": 20 }, { "by": 0, "edge": "", "key": "interface", "name": "Agent interface", "otel-2-0-llm-31b-it": 60, "qwen3-vl-32b-instruct": 60, "weight": 10 }, { "by": 43, "edge": "otel-2-0-llm-31b-it", "key": "upkeep", "name": "Documentation and upkeep", "otel-2-0-llm-31b-it": 74, "qwen3-vl-32b-instruct": 31, "weight": 10 } ], "summary": "OTel-2.0-LLM-31B-IT and Qwen3-VL-32B-Instruct score within a point of each other on agent readiness, 56.8 (C) and 56.4 (C). Qwen3-VL-32B-Instruct leads on reach. 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-vl-32b-instruct": "Qwen3-VL-32B-Instruct is the largest dense Qwen3-VL model, for screenshots, documents, charts and video. Alibaba's Q4_K_M GGUF with its vision file needs about 25 GB, and Transformers, vLLM, SGLang, llama.cpp and Ollama run it under plain Apache 2.0. It has no thinking mode or published safety work, and only one provider was found serving it." } }, "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-vl-32b-instruct", "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-32b-instruct.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-32b-instruct.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-32b-instruct.min.md" }, "markdown": "OTel-2.0-LLM-31B-IT and Qwen3-VL-32B-Instruct score within a point of each other on agent readiness, 56.8 (C) and 56.4 (C). Qwen3-VL-32B-Instruct leads on reach. 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-VL-32B-Instruct: grade C, 56.4/100, rank #115 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-vl-32b-instruct.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 31\n\nWatch for: BF16 only at 62.6 GB, with no GGUF or other quantised build from anyone we checked\n\n### Qwen3-VL-32B-Instruct (C)\n\nGood for: An agent with a 32 GB GPU or more that wants the largest dense Qwen3-VL model for images, documents and video\n\nAhead on:\n- Reach, 85 against 50\n\nWatch for: No safety evaluation, agent-use guidance or security contact\n\n\n## Why the edge\n\n### Documentation and upkeep: OTel-2.0-LLM-31B-IT +43\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-VL-32B-Instruct (31): The card misses limitations, languages and a cut-off (11). Training data is described only in the technical report, with no cut-off (5). The last change to code that runs it was the qwen-vl-utils fix of 4 January 2026, and the weights haven't changed since November 2025; the Hub commits for all three repositories and the QwenLM/Qwen3-VL head (30 January 2026) were the same on 4 November (5). Jie Huang, a Qwen organisation member, a named author of the Qwen3-VL Technical Report and the author of nine commits in QwenLM/Qwen3-VL, answered two of the 11 Hugging Face discussions that aren't pull requests, both on 27 October 2025; no publisher reply since, and GitHub issues weren't counted (5). The FP8 build's files were replaced in place the day after release with no note, after it had shipped the Thinking edition's files for three days (0). The GGUF card's example names a file the repository doesn't hold (5). 11+5+5+5+0+5 = 31.\n\n### Reach: Qwen3-VL-32B-Instruct +35\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-VL-32B-Instruct (85): Alibaba's own Q4_K_M GGUF is 19.76 GB and 20.53 GB with the vision file it needs for images, so with a fifth added it needs about 25 GB and the 32 GB band (20). Transformers 4.57.0, vLLM, SGLang, llama.cpp and Ollama all run it in released versions, which reaches the 25 cap. Alibaba publishes FP8 and GGUF builds at F16, Q8_0 and Q4_K_M (15). Apache 2.0 with nothing added (15). No gate and no account (10). On 4 November the Hub's provider mapping lists only Featherless, live, and OpenRouter listed it with no endpoints per the 21 October check, so it isn't hosted too (0). 20+25+15+15+10+0 = 85.\n\n## Score by category\n\n| Category | Weight | OTel-2.0-LLM-31B-IT | Qwen3-VL-32B-Instruct | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 50 | 85 | Qwen3-VL-32B-Instruct +35 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 60 | 60 | even |\n| Documentation and upkeep | 10% (10 this run) | 74 | 31 | OTel-2.0-LLM-31B-IT +43 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **56.8 · C** | **56.4 · C** | |\n\n## Facts side by side\n\n| Fact | OTel-2.0-LLM-31B-IT | Qwen3-VL-32B-Instruct |\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 | 2025-11-01 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 2.5/5 (4) |\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-VL-32B-Instruct.** Qwen3-VL-32B-Instruct is the largest dense Qwen3-VL model, for screenshots, documents, charts and video. Alibaba's Q4_K_M GGUF with its vision file needs about 25 GB, and Transformers, vLLM, SGLang, llama.cpp and Ollama run it under plain Apache 2.0. It has no thinking mode or published safety work, and only one provider was found serving it.\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-VL-32B-Instruct\n\n1. Start it with `llama-server -hf Qwen/Qwen3-VL-32B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:32b-instruct`\n2. In Ollama the plain `qwen3-vl:32b` tag is the Thinking edition, so name `32b-instruct`\n3. With the GGUF, load the mmproj vision file too, or images won't be read\n4. Parse tool calls as JSON inside `\u003ctool_call\u003e` tags, or use SGLang's `--tool-call-parser qwen`\n5. Use the card's sampling, `temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5` for vision and `temperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0` for text\n6. Validate JSON output yourself, since structured output isn't documented\n7. vLLM needs 0.11.0 or later and Transformers 4.57.0 or later\n8. If you pulled the FP8 build before 22 October 2025, pull it again, since the earlier files were the Thinking edition's\n\n## Questions\n\n### Which is better for AI agents, OTel-2.0-LLM-31B-IT or Qwen3-VL-32B-Instruct?\n\nOTel-2.0-LLM-31B-IT and Qwen3-VL-32B-Instruct score within a point of each other on agent readiness, 56.8 (C) and 56.4 (C). Qwen3-VL-32B-Instruct leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-32b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-32b-instruct.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"otel-2-0-llm-31b-it\", \"b\": \"qwen3-vl-32b-instruct\"}`. From a terminal: `anchor compare otel-2-0-llm-31b-it qwen3-vl-32b-instruct`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/otel-2-0-llm-31b-it.json and https://www.anchorterminal.com/api/v1/tools/qwen3-vl-32b-instruct.json\n\n## Other comparisons with OTel-2.0-LLM-31B-IT or Qwen3-VL-32B-Instruct\n\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- [Agents-A1-4B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-vl-32b-instruct.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- [Apertus v1.5 8B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-vl-32b-instruct.md)\n- [Apodex-1.1-mini vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-otel-2-0-llm-31b-it.md)\n- [Apodex-1.1-mini vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-vl-32b-instruct.md)\n- [Arete-27B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/arete-27b-vs-otel-2-0-llm-31b-it.md)\n- [Arete-27B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-32b-instruct.md)\n- [BigBang-v1 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/bigbang-v1-vs-otel-2-0-llm-31b-it.md)\n- [BigBang-v1 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-vl-32b-instruct.md)\n- [Carbon-X vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/carbon-x-vs-otel-2-0-llm-31b-it.md)\n- [Carbon-X vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-vl-32b-instruct.md)\n- [Cola2 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/cola2-vs-otel-2-0-llm-31b-it.md)\n- [Cola2 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/cola2-vs-qwen3-vl-32b-instruct.md)\n- [Cosmos-Reason2-2B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-otel-2-0-llm-31b-it.md)\n- [Cosmos-Reason2-2B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-vl-32b-instruct.md)\n- [Darwin-36B-Opus vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-otel-2-0-llm-31b-it.md)\n- [Darwin-36B-Opus vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-vl-32b-instruct.md)\n- [DeepSeek-V3.2 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-otel-2-0-llm-31b-it.md)\n- [DeepSeek-V3.2 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-vl-32b-instruct.md)\n- [DeepSeek V4.1 Flash vs 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Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-vl-32b-instruct.md)\n- [EXAONE 4.5 33B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-otel-2-0-llm-31b-it.md)\n- [EXAONE 4.5 33B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-vl-32b-instruct.md)\n- [Gemma 4 12B IT vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-otel-2-0-llm-31b-it.md)\n- [Gemma 4 12B IT vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-vl-32b-instruct.md)\n- [Gemma 4 26B A4B IT vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-otel-2-0-llm-31b-it.md)\n- [Gemma 4 26B A4B IT vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-vl-32b-instruct.md)\n- [Gemma 4 31B IT vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-otel-2-0-llm-31b-it.md)\n- [Gemma 4 31B IT vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-32b-instruct.md)\n- [Gemma 4 E2B IT vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-otel-2-0-llm-31b-it.md)\n- [Gemma 4 E2B IT vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-vl-32b-instruct.md)\n- [Gemma 4 E4B IT vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-otel-2-0-llm-31b-it.md)\n- [Gemma 4 E4B IT vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-vl-32b-instruct.md)\n- [GLM-4.5-Air vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/glm-4-5-air-vs-otel-2-0-llm-31b-it.md)\n- [GLM-4.5-Air vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-vl-32b-instruct.md)\n- [GLM-4.7-Flash vs 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OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-otel-2-0-llm-31b-it.md)\n- [gpt-oss-20b vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-vl-32b-instruct.md)\n- [Granite 4.2 30B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-otel-2-0-llm-31b-it.md)\n- [Granite 4.2 30B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-vl-32b-instruct.md)\n- [Granite 4.2 3B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-otel-2-0-llm-31b-it.md)\n- [Granite 4.2 3B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-vl-32b-instruct.md)\n- [Granite 4.2 8B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-otel-2-0-llm-31b-it.md)\n- [Granite 4.2 8B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-vl-32b-instruct.md)\n- 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OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-otel-2-0-llm-31b-it.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-vl-32b-instruct.md)\n- [Kimi K3.1 Pro vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-otel-2-0-llm-31b-it.md)\n- [Kimi K3.1 Pro vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-32b-instruct.md)\n- [Kimi K3 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/kimi-k3-vs-otel-2-0-llm-31b-it.md)\n- [Kimi K3 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-32b-instruct.md)\n- [Kolibri-1 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/kolibri-1-vs-otel-2-0-llm-31b-it.md)\n- [Kolibri-1 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-32b-instruct.md)\n- 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OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/lbs-27b-vs-otel-2-0-llm-31b-it.md)\n- [LBS-27B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-32b-instruct.md)\n- [LFM2.5-2.6B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-otel-2-0-llm-31b-it.md)\n- [LFM2.5-2.6B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-32b-instruct.md)\n- [LFM3-1.7B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-otel-2-0-llm-31b-it.md)\n- [LFM3-1.7B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-vl-32b-instruct.md)\n- [LFM3-Audio-2B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-otel-2-0-llm-31b-it.md)\n- [LFM3-Audio-2B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-vl-32b-instruct.md)\n- [LFM3-Omni-2B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-otel-2-0-llm-31b-it.md)\n- [LFM3-Omni-2B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-32b-instruct.md)\n- [LFM3-VL-2B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-otel-2-0-llm-31b-it.md)\n- [LFM3-VL-2B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-32b-instruct.md)\n- [Ling-3.0-tiny vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-otel-2-0-llm-31b-it.md)\n- [Ling-3.0-tiny vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-32b-instruct.md)\n- [Ling-3.1-flash-1019 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-otel-2-0-llm-31b-it.md)\n- [Ling-3.1-flash-1019 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-vl-32b-instruct.md)\n- [LongCat-2.5 vs 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[Ornith-1.5-35B-A3B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-otel-2-0-llm-31b-it.md)\n- [Ornith-1.5-35B-A3B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-32b-instruct.md)\n- [Ornith-1.5-397B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-otel-2-0-llm-31b-it.md)\n- [Ornith-1.5-397B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-32b-instruct.md)\n- [Ornith-1.5-9B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-otel-2-0-llm-31b-it.md)\n- [Ornith-1.5-9B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-32b-instruct.md)\n- [OTel-2.0-LLM-31B-IT vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen-agentworld-35b-a3b.md)\n- [OTel-2.0-LLM-31B-IT vs 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WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-wevisone-vl-4b.md)\n- [Qwen3-VL-32B-Instruct vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-zaya1-8b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-11", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "OTel-2.0-LLM-31B-IT vs Qwen3-VL-32B-Instruct", "url": "" } ], "description": "OTel-2.0-LLM-31B-IT and Qwen3-VL-32B-Instruct score within a point of each other for open-weight models you run yourself, 56.8 and 56.4 out of 100. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "OTel-2.0-LLM-31B-IT C 56.8", "Qwen3-VL-32B-Instruct C 56.4", "scores" ], "h1": "OTel-2.0-LLM-31B-IT vs Qwen3-VL-32B-Instruct", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-32b-instruct", "published": "2026-10-01", "section": "tools", "title": "OTel-2.0-LLM-31B-IT vs Qwen3-VL-32B-Instruct for AI agents (2026)", "toc": null, "updated": "2026-11-10", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-32b-instruct" }, "tokens": { "markdown": 9700, "slim": 630 }, "version": 1 }