{ "data": { "a": { "slug": "ling-3-1-flash-1019", "name": "Ling-3.1-flash-1019", "vendor": "inclusionAI (Ant Group)", "vendorUrl": "https://www.inclusion-ai.org", "kind": "weights", "category": "open-weights", "summary": "Ling-3.1-flash-1019 is inclusionAI's open-weight hybrid reasoning mixture of experts with 560B parameters and 25B active, a post-trained update of Ling-3.1-flash, with switchable thinking, tool calling and a 262,144-token native context, published in FP8 under MIT.", "url": "https://www.anchorterminal.com/tools/ling-3-1-flash-1019", "markdownUrl": "https://www.anchorterminal.com/tools/ling-3-1-flash-1019.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ling-3-1-flash-1019.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ling-3-1-flash-1019.json", "dns": "ling-3-1-flash-1019.grade.anchorterminal.com", "repo": "https://huggingface.co/inclusionAI/Ling-3.1-flash-1019", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "inclusionAI/Ling-3.1-flash-1019" } ], "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 MIT (named in the card's metadata; the repository holds no LICENSE file). Running it yourself costs the hardware, 573.8 GB of FP8 files, which the card serves on 8 H200 or 4 GB300 GPUs (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/inclusionAI/Ling-3.1-flash-1019", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "hybrid-attention", "reasoning", "tool-calling", "long-context", "mit", "safetensors", "fp8", "self-hosted" ], "lastRelease": "2026-11-04", "graded": true, "anchor": { "graded": true, "score": 54.8, "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": 94, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 60, "reach": 60, "risk": 45, "upkeep": 56 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "inclusionAI's newest flagship, a 560B hybrid mixture of experts under plain MIT that vLLM, SGLang, Transformers and llama.cpp run in released versions. It ships only as 574 GB of FP8, so it needs a multi-GPU server. The card overstates the context, documents little about tool calls, and has no safety results.", "bestFor": "Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding", "strengths": [ "Plain MIT, ungated, with a hash for every shard", "Released vLLM, SGLang, Transformers and llama.cpp all have the architecture", "A 262,144-token native context, and 1M with YaRN per SGLang's cookbook", "A training-content summary that names source categories, the size band and a collection date", "Updated a week before this check" ], "weaknesses": [ "574 GB in FP8 with no smaller build from inclusionAI, so no single machine under 690 GB of GPU memory runs it", "The card says 1M tokens of context where the files give 262,144", "The tool-call format is only in the template", "No safety results, no agent guidance and no security contact", "No inference provider mapped for this release, only for the earlier one", "Neither question on the repository has a reply" ], "agentNotes": [ "Serve it with SGLang 0.5.23 or later on 8 H200 or 4 GB300 GPUs, per the card, and use this repository's name as the model path, not the earlier one the card's command shows", "With vLLM pass `--trust-remote-code`, `--tool-call-parser ling3` and `--reasoning-parser ling3`", "Set `chat_template_kwargs` `enable_thinking` false for direct answers. Reasoning comes back in `reasoning_content` per SGLang's cookbook", "Plan on 262,144 tokens of context. The 1M figure needs the YaRN override in SGLang's cookbook", "Use temperature 1.0, top_p 0.95 and top_k 20, per the card", "Validate JSON output yourself, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 54.8 } ], "editorialScores": { "interface": 60, "reach": 60, "risk": 45, "upkeep": 56 }, "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 SGLang 0.5.23 or later, on 8 H200 GPUs:\npython3 -m sglang.launch_server --model-path inclusionAI/Ling-3.1-flash-1019 \\\n --tp 8 --ep 8 --speculative-algorithm NEXTN --host 0.0.0.0 --port 30000\n# or with vLLM 0.33.0, per the card's serve command:\nvllm serve inclusionAI/Ling-3.1-flash-1019 --trust-remote-code --tensor-parallel-size 8 \\\n --enable-prefix-caching --enable-expert-parallel --mamba-cache-mode align \\\n --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3\n# or, with llama.cpp and bartowski's GGUF, a third party's conversion:\nllama-server -hf bartowski/Ling-3.1-flash-1019-GGUF:Q4_K_M --jinja", "http": "curl http://localhost:30000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"inclusionAI/Ling-3.1-flash-1019\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"chat_template_kwargs\": {\"enable_thinking\": false}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/ling-3-1-flash-1019" }, "sameCompany": [ "ming-3-0-asr-tiny", "ming-3-0-audio-flash", "ming-3-0-audio-tiny", "ming-3-0-tts-tiny", "lbs-27b", "ling-3-0-tiny" ], "weights": { "trainedBy": "inclusionAI (Ant Group)", "builtOn": "inclusionAI/Ling-3.1-flash, post-trained further (named on the card; the metadata names no base model)", "params": "560B (25B active)", "context": 262144, "goodFor": "Large hybrid reasoning model for agents, office work and software engineering on a multi-GPU server, with thinking switchable per request. It needs about 690 GB of GPU memory in FP8.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "bartowski Ling-3.1-flash-1019-GGUF (Q4_K_M)", "url": "https://huggingface.co/bartowski/Ling-3.1-flash-1019-GGUF", "who": "bartowski", "precision": "q4_k_m", "sizeGB": 357.97, "memoryGB": 512, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not inclusionAI's. 357.97 GB of Q4_K_M weights, from the Hub's model information on 10 November 2026. Reach doesn't count it" }, { "name": "Ling-3.1-flash-1019", "url": "https://huggingface.co/inclusionAI/Ling-3.1-flash-1019", "who": "publisher", "precision": "fp8", "sizeGB": 573.78, "memoryGB": 1024, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ], "note": "The release itself, FP8 E4M3 weights in 128×128 blocks. inclusionAI publishes no smaller build" } ], "run": "python3 -m sglang.launch_server --model-path inclusionAI/Ling-3.1-flash-1019 --tp 8 --ep 8 --speculative-algorithm NEXTN", "runBuild": "Ling-3.1-flash-1019", "checked": "2026-11-10", "minMemoryGB": 512 }, "family": { "lab": "inclusionAI (Ant Group)", "family": "Ling 3", "url": "https://www.anchorterminal.com/open-weights/#inclusionai", "others": [ { "name": "Ling-3.0-flash", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash", "params": "124B total, 5.1B active" }, { "name": "Ling-3.0-flash-base", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-base", "params": "124B total, 5.1B active" }, { "name": "Ling-3.0-flash-base-30T", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-base-30T", "params": "124B total, 5.1B active" }, { "name": "Ling-3.0-flash-base-midtrain", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-base-midtrain", "params": "124B total, 5.1B active" }, { "name": "Ling-3.0-flash-Fin", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-Fin", "params": "124B total, 5.1B active" }, { "name": "Ling-3.0-flash-VL", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-VL", "params": "124B total, 5.5B active" }, { "name": "Ling-3.0-flash-dspark", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-dspark", "params": "1.36B (draft)" }, { "name": "Ling-3.0-flash-singprobe", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-singprobe", "params": "5.18M (probe)" }, { "name": "Ling-3.0-tiny", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny", "params": "7.9B total, 1.3B active", "listing": "https://www.anchorterminal.com/tools/ling-3-0-tiny" }, { "name": "Ling-3.0-tiny-base", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-base", "params": "7.9B total, 1.3B active" }, { "name": "Ling-3.0-tiny-base-30T", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-base-30T", "params": "7.9B total, 1.3B active" }, { "name": "Ling-3.0-tiny-base-midtrain", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-base-midtrain", "params": "7.9B total, 1.3B active" }, { "name": "Ling-3.0-tiny-singprobe", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-singprobe", "params": "3.22M (probe)" }, { "name": "Ling-3.1-flash", "url": "https://huggingface.co/inclusionAI/Ling-3.1-flash", "params": "560B total, 25B active" }, { "name": "Ling-3.1-flash-base", "url": "https://huggingface.co/inclusionAI/Ling-3.1-flash-base", "params": "560B total, 25B active" } ], "olderCount": 54 }, "area": "models", "provenance": { "legalEntity": "Ant Intelligence (Hang Zhou) Technology Co., Ltd (named as provider in the training-content summary); inclusionAI, Ant Group in the site footer of inclusion-ai.org", "domain": "inclusion-ai.org", "domainRegistered": "2025-04-03", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/inclusionAI/Ling-3.1-flash-1019/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "inclusion-ai.org links to huggingface.co/inclusionAI and github.com/inclusionAI. The domain registration date is carried from the RDAP check made for the Ming 3.0 listings on 21 October 2026.", "Nothing is hosted by the publisher, so the MIT licence stands in for terms of service and no privacy policy is scored.", "The changelog is the Hugging Face commit history, 8 commits from 27 October to 4 November 2026, the last three card edits.", "www.inclusion-ai.org/.well-known/security.txt answered 404 on 10 November 2026." ], "score": 61 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/ling-3-1-flash-1019.json" }, "answer": "Qwen3-VL-30B-A3B-Instruct scores 58 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 2 of 4 scored categories. Ling-3.1-flash-1019 leads on documentation and upkeep.", "b": { "slug": "qwen3-vl-30b-a3b-instruct", "name": "Qwen3-VL-30B-A3B-Instruct", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3-VL-30B-A3B-Instruct is Alibaba's open-weight vision-language mixture of experts with about 31B parameters and 3B active, 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-30b-a3b-instruct", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-vl-30b-a3b-instruct.json", "dns": "qwen3-vl-30b-a3b-instruct.grade.anchorterminal.com", "repo": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3-VL-30B-A3B-Instruct" }, { "registry": "huggingface", "name": "Qwen/Qwen3-VL-30B-A3B-Instruct-FP8" }, { "registry": "huggingface", "name": "Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF" }, { "registry": "ollama", "name": "qwen3-vl:30b-a3b-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, 62.2 GB for the BF16 files, 32.3 GB for Alibaba's FP8 build and 18.6 GB plus a 0.7 to 1.1 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-30B-A3B-Instruct", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "vision", "video", "tool-calling", "apache-2.0", "safetensors", "fp8", "gguf", "self-hosted" ], "lastRelease": "2025-11-01", "graded": true, "anchor": { "graded": true, "score": 58, "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": 79, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 39, "interface": 60, "reach": 95, "risk": 55, "upkeep": 36 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-04" }, "negative": 0, "verdict": "A vision-language mixture of experts for screenshots, documents, charts and video, with about 3B of its 31B parameters active. Alibaba's Q4_K_M GGUF with its vision file needs about 23 GB, it loads in Transformers, vLLM, SGLang, llama.cpp and Ollama under Apache 2.0, and three providers host it. No safety work is published, so add guardrails before it drives a GUI.", "bestFor": "An agent with 24 GB or more to spare that wants a local reader of images, documents and video with 262,144 tokens of context", "strengths": [ "About 23 GB of memory for Alibaba's own Q4_K_M GGUF and vision file, with FP8 and Q8_0 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 and a checksum on every file", "Served by Novita, Featherless AI and DeepInfra through Hugging Face, and listed with SiliconFlow on OpenRouter, for agents without the hardware" ], "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", "No reply from Qwen to any of the eight Hugging Face discussions that aren't pull requests", "No change to the weights since September 2025, and the card leaves out limitations, languages, knowledge cut-off and memory needs" ], "agentNotes": [ "Start it with `llama-server -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:30b-a3b-instruct`", "In Ollama the plain `qwen3-vl:30b` tag is the Thinking edition, so name `30b-a3b-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 shipped sampling, temperature 0.7, top_p 0.8 and top_k 20, from generation_config.json", "Validate JSON output yourself, since structured output isn't documented", "vLLM needs 0.11.0 or later, Transformers 4.57.0 or later and llama.cpp b6907 or later" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 58 } ], "editorialScores": { "capability": 39, "interface": 60, "reach": 95, "risk": 55, "upkeep": 36 }, "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:30b-a3b-instruct\n# or with llama.cpp and Alibaba's own GGUF (llama.cpp b6907 or later, per the GGUF card):\nllama-server -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M --jinja -c 32768\n# or with vLLM 0.11.0 or later:\nvllm serve Qwen/Qwen3-VL-30B-A3B-Instruct-FP8 --max-model-len 131072", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"qwen3-vl:30b-a3b-instruct\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen3-vl-30b-a3b-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-32b-instruct", "qwen3-vl-4b-instruct", "qwen3-vl-8b-instruct", "qwen3-5-0-8b", "qwen3-5-122b-a10b", "qwen3-5-2b", "qwen3-5-397b-a17b", "qwen3-5-4b", "qwen3-5-9b", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "params": "31.1B (3B active)", "context": 262144, "goodFor": "Vision-language mixture of experts for images, documents, screens and video with text out, about 3B of 31B parameters active per token, no thinking mode in this Instruct edition.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "Qwen3VL-30B-A3B-Instruct-Q4_K_M", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF/blob/main/Qwen3VL-30B-A3B-Instruct-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 18.6, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Add mmproj-Qwen3VL-30B-A3B-Instruct-Q8_0.gguf (0.7 GB) for image input, 19.3 GB together, still within 24 GB with a fifth added" }, { "name": "ollama qwen3-vl:30b-a3b-instruct", "url": "https://ollama.com/library/qwen3-vl:30b-a3b-instruct", "who": "ollama", "precision": "q4_k_m", "sizeGB": 20, "memoryGB": 24, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M with its vision projector (digest 9b053ed92b6a). qwen3-vl:30b, the library default for this size, is the Thinking edition. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB" }, { "name": "Qwen3-VL-30B-A3B-Instruct-FP8", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 32.3, "memoryGB": 48, "format": "safetensors" }, { "name": "Qwen3VL-30B-A3B-Instruct-Q8_0", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF/blob/main/Qwen3VL-30B-A3B-Instruct-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 32.5, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Add an mmproj file for image input" }, { "name": "Qwen3-VL-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct", "who": "publisher", "precision": "bf16", "sizeGB": 62.2, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] }, { "name": "Qwen3VL-30B-A3B-Instruct-F16", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF/blob/main/Qwen3VL-30B-A3B-Instruct-F16-split-00001-of-00002.gguf", "who": "publisher", "precision": "f16", "sizeGB": 61.1, "memoryGB": 96, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Two split files; add an mmproj file (0.7 or 1.1 GB) for image input" } ], "run": "llama-server -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M --jinja -c 32768", "runBuild": "Qwen3VL-30B-A3B-Instruct-Q4_K_M", "checked": "2026-10-21", "minMemoryGB": 24 }, "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-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": "Qwen3.5-397B-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 pilot'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 repository has no LICENSE file. The GitHub repository's LICENSE is Apache 2.0. The legal entity and domain date are carried from the pilot's RDAP check of qwen.ai on 20 October 2026.", "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 (the 30B-A3B models on 4 October 2025), and the Hugging Face commit messages are uploads and README updates.", "No security.txt was found for qwen.ai, as the pilot and the Qwen3-VL-8B listing recorded." ], "score": 54 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct.json", "live": { "slug": "qwen3-vl-30b-a3b-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": 35, "category": "reach", "leads": "qwen3-vl-30b-a3b-instruct", "name": "Reach", "reasons": { "a": "Memory 0, inclusionAI's only build is the FP8 release at 573.78 GB, × 1.2 = 688.5 GB, above 256 GB, and no runtime's library has a build. Runtimes 25, Released model code in Transformers 5.22.2 (bailing_hybrid), vLLM 0.33.0 (BailingMoeV3ForCausalLM, with ling3 tool and reasoning parsers), SGLang 0.5.23 (bailing_moe_v3.py, its bailing_hybrid config and MTP module) and llama.cpp b12242 (bailingmoe3), and Ollama, credited through its pinned llama.cpp as on the Ling-3.0-tiny listing: v0.40.16 sets LLAMA_CPP_VERSION to b12177. Builds 10, the release is FP8, the one quantised precision inclusionAI publishes for it. Terms 15, MIT in the card's metadata, with no use policy or other terms added on the card or in the repository. Download 10, the Hub API's model info gives gated false on 10 November 2026, so the files download with no account and no terms to accept. Hosted 0, the Hub API's provider mapping is empty for this repository on 10 November 2026. 0 + 25 + 10 + 15 + 10 + 0 = 60.", "b": "Alibaba's own Q4_K_M GGUF is 18.6 GB, so with a fifth added it needs about 22 GB, or 23 GB with the vision file, and fits 24 GB (25). Transformers 4.57.0, vLLM 0.11.0, SGLang 0.5.4, llama.cpp and Ollama all run it in released versions, which reaches the 25 cap. Alibaba publishes FP8 and GGUF builds at Q8_0 and Q4_K_M (15). Apache 2.0 with nothing added (15). No gate and no account (10). Novita, Featherless AI and DeepInfra serve it on Hugging Face, and SiliconFlow too on OpenRouter (5). 25 + 25 + 15 + 15 + 10 + 5 = 95." }, "scores": { "a": 60, "b": 95 } }, { "by": 20, "category": "upkeep", "leads": "ling-3-1-flash-1019", "name": "Documentation and upkeep", "reasons": { "a": "Model card 11, Intended use (complex reasoning, multi-step tasks and agentic workflows), architecture, parameters (560B total, 25B active) and context (1M claimed) are on the card. Training data 15, the training-content summary the card links names the source categories (Common Crawl and other public datasets, commercially licensed data, the Ant Spider crawler, synthetic data), the size band (over 10 trillion text tokens), data collected up to about July 2026 and the filtering, without listing datasets with sizes. Last change 20, the newest commit, on 4 November 2026, edited the card, six days before this check, and the files went up on 27 October. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (both opened on 27 October 2026) has a reply from an inclusionAI member. Version policy 5, the update went into a repository of its own and Ling-3.1-flash and Ling 3.0 stay in theirs. File health 5, the card states a 1M-token context while config.json gives 262,144 with no rope scaling, and its serve commands name the earlier repository as the model path. 11 + 15 + 20 + 0 + 5 + 5 = 56.", "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 newest Hub commit is the library-tag commit of 26 November 2025 and the weights haven't changed since September 2025, so the qwen-vl-utils fix of 4 January 2026 in the code that runs it sets the line, within a year (5). Qwen answered none of the eight Hugging Face discussions that aren't pull requests (0), and GitHub issues weren't counted. Earlier versions kept in their own repositories without notes (5). Config, template and card agree and no broken-file report waits (10). 11 + 5 + 5 + 0 + 5 + 10 = 36." }, "scores": { "a": 56, "b": 36 } }, { "by": 10, "category": "risk", "leads": "qwen3-vl-30b-a3b-instruct", "name": "Risk", "reasons": { "a": "File safety 15, safetensors only, no .bin, .pt or .pth files (25), less 10 because the card's documented vLLM command passes --trust-remote-code, which runs the repository's configuration_bailing_moe_v3.py, since vLLM 0.33.0 registers no bailing_hybrid config of its own. Provenance 15, files in the inclusionAI organisation that inclusion-ai.org links to, per the Ling-3.0-tiny listing (10). Safety evaluation 0, no safety results for the model on its card or in the training-content summary. Agent guidance 0, the card has nothing on prompt injection, refusals or safeguards, and inclusionAI publishes no guardrail probe for this model as it did for Ling 3.0. Licence risk 15, MIT with no use policy, no field-of-use limits, no threshold and no clause letting inclusionAI change or revoke it. Security programme 0, www.inclusion-ai.org answers 404 for security.txt on 10 November 2026, and the Ling-3.0-tiny listing found no SECURITY.md or disclosure policy in the inclusionAI .github repository. 15 + 15 + 0 + 0 + 15 + 0 = 45.", "b": "Safetensors and GGUF only and no code in the repositories, so the FP8 card's trust_remote_code flag runs nothing (25). Files in the Qwen organisation with a checksum on every file (15). No safety evaluation (0). No guidance for agent use, which matters for a model sold as a GUI agent (0). Apache 2.0 carries none of the licence risks (15). No security contact, disclosure policy or public advisories found for Qwen (0), and Alibaba's group-wide security centre wasn't checked." }, "scores": { "a": 45, "b": 55 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "inclusionAI (Ant Group)", "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": "MIT", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-11-04", "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": "3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Qwen3-VL-30B-A3B-Instruct scores 58 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 2 of 4 scored categories. Ling-3.1-flash-1019 leads on documentation and upkeep.", "question": "Which is better for AI agents, Ling-3.1-flash-1019 or Qwen3-VL-30B-A3B-Instruct?" } ], "goodFor": [ { "aheadOn": [ "Documentation and upkeep, 56 against 36" ], "also": null, "goodFor": "Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding", "slug": "ling-3-1-flash-1019", "watchFor": "574 GB in FP8 with no smaller build from inclusionAI, so no single machine under 690 GB of GPU memory runs it" }, { "aheadOn": [ "Reach, 95 against 60", "Risk, 55 against 45" ], "also": null, "goodFor": "An agent with 24 GB or more to spare that wants a local reader of images, documents and video with 262,144 tokens of context", "slug": "qwen3-vl-30b-a3b-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": 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}, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-30b-a3b-instruct.json", "title": "occamy-1.0 vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Olmo 3 7B Think vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Ornith-1.5-122B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Ornith-1.5-35B-A3B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Ornith-1.5-9B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-30b-a3b-instruct.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3-30B-A3B-Instruct-2507 vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3-4B-Instruct-2507 vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3.5-0.8B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3.5-122B-A10B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3.5-2B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-30b-a3b-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/qwen3-5-4b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3.5-4B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3.5-9B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3.6-27B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3.6-35B-A3B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3.8-27B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3.8-Flash-Next vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3-Coder-Next vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3-VL-2B-Instruct vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwen3-vl-30b-a3b-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-30b-a3b-instruct-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-recursive-flint.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-sensenova-loop-9b-preview.json", "title": "Qwen3-VL-30B-A3B-Instruct vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-smollm3-3b.json", "title": "Qwen3-VL-30B-A3B-Instruct vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-spark-x2-6-4b.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-step-3-7-flash.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-VL-30B-A3B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-unlimited-ocr-2.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-wevisone-vl-2b.json", "title": "Qwen3-VL-30B-A3B-Instruct vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-wevisone-vl-4b.json", "title": "Qwen3-VL-30B-A3B-Instruct vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-zaya1-8b.json", "title": "Qwen3-VL-30B-A3B-Instruct vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 35, "edge": "qwen3-vl-30b-a3b-instruct", "key": "reach", "ling-3-1-flash-1019": 60, "name": "Reach", "qwen3-vl-30b-a3b-instruct": 95, "weight": 25 }, { "by": 10, "edge": "qwen3-vl-30b-a3b-instruct", "key": "risk", "ling-3-1-flash-1019": 45, "name": "Risk", "qwen3-vl-30b-a3b-instruct": 55, "weight": 20 }, { "by": 0, "edge": "", "key": "interface", "ling-3-1-flash-1019": 60, "name": "Agent interface", "qwen3-vl-30b-a3b-instruct": 60, "weight": 10 }, { "by": 20, "edge": "ling-3-1-flash-1019", "key": "upkeep", "ling-3-1-flash-1019": 56, "name": "Documentation and upkeep", "qwen3-vl-30b-a3b-instruct": 36, "weight": 10 } ], "summary": "Qwen3-VL-30B-A3B-Instruct scores 58 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 2 of 4 scored categories. Ling-3.1-flash-1019 leads on documentation and upkeep. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "ling-3-1-flash-1019": "inclusionAI's newest flagship, a 560B hybrid mixture of experts under plain MIT that vLLM, SGLang, Transformers and llama.cpp run in released versions. It ships only as 574 GB of FP8, so it needs a multi-GPU server. The card overstates the context, documents little about tool calls, and has no safety results.", "qwen3-vl-30b-a3b-instruct": "A vision-language mixture of experts for screenshots, documents, charts and video, with about 3B of its 31B parameters active. Alibaba's Q4_K_M GGUF with its vision file needs about 23 GB, it loads in Transformers, vLLM, SGLang, llama.cpp and Ollama under Apache 2.0, and three providers host it. No safety work is published, so add guardrails before it drives a GUI." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-vl-30b-a3b-instruct", "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-vl-30b-a3b-instruct.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-vl-30b-a3b-instruct.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-vl-30b-a3b-instruct.min.md" }, "markdown": "Qwen3-VL-30B-A3B-Instruct scores 58 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 2 of 4 scored categories. Ling-3.1-flash-1019 leads on documentation and upkeep. Both do open-weight models you run yourself.\n\n- Ling-3.1-flash-1019: grade C, 54.8/100, rank #126 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/ling-3-1-flash-1019.md · JSON https://www.anchorterminal.com/api/v1/tools/ling-3-1-flash-1019.json\n- Qwen3-VL-30B-A3B-Instruct: grade C, 58/100, rank #104 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-vl-30b-a3b-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### Ling-3.1-flash-1019 (C)\n\nGood for: Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding\n\nAhead on:\n- Documentation and upkeep, 56 against 36\n\nWatch for: 574 GB in FP8 with no smaller build from inclusionAI, so no single machine under 690 GB of GPU memory runs it\n\n### Qwen3-VL-30B-A3B-Instruct (C)\n\nGood for: An agent with 24 GB or more to spare that wants a local reader of images, documents and video with 262,144 tokens of context\n\nAhead on:\n- Reach, 95 against 60\n- Risk, 55 against 45\n\nWatch for: No safety evaluation, agent-use guidance or security contact\n\n\n## Why the edge\n\n### Reach: Qwen3-VL-30B-A3B-Instruct +35\n\n- Ling-3.1-flash-1019 (60): Memory 0, inclusionAI's only build is the FP8 release at 573.78 GB, × 1.2 = 688.5 GB, above 256 GB, and no runtime's library has a build. Runtimes 25, Released model code in Transformers 5.22.2 (bailing_hybrid), vLLM 0.33.0 (BailingMoeV3ForCausalLM, with ling3 tool and reasoning parsers), SGLang 0.5.23 (bailing_moe_v3.py, its bailing_hybrid config and MTP module) and llama.cpp b12242 (bailingmoe3), and Ollama, credited through its pinned llama.cpp as on the Ling-3.0-tiny listing: v0.40.16 sets LLAMA_CPP_VERSION to b12177. Builds 10, the release is FP8, the one quantised precision inclusionAI publishes for it. Terms 15, MIT in the card's metadata, with no use policy or other terms added on the card or in the repository. Download 10, the Hub API's model info gives gated false on 10 November 2026, so the files download with no account and no terms to accept. Hosted 0, the Hub API's provider mapping is empty for this repository on 10 November 2026. 0 + 25 + 10 + 15 + 10 + 0 = 60.\n- Qwen3-VL-30B-A3B-Instruct (95): Alibaba's own Q4_K_M GGUF is 18.6 GB, so with a fifth added it needs about 22 GB, or 23 GB with the vision file, and fits 24 GB (25). Transformers 4.57.0, vLLM 0.11.0, SGLang 0.5.4, llama.cpp and Ollama all run it in released versions, which reaches the 25 cap. Alibaba publishes FP8 and GGUF builds at Q8_0 and Q4_K_M (15). Apache 2.0 with nothing added (15). No gate and no account (10). Novita, Featherless AI and DeepInfra serve it on Hugging Face, and SiliconFlow too on OpenRouter (5). 25 + 25 + 15 + 15 + 10 + 5 = 95.\n\n### Documentation and upkeep: Ling-3.1-flash-1019 +20\n\n- Ling-3.1-flash-1019 (56): Model card 11, Intended use (complex reasoning, multi-step tasks and agentic workflows), architecture, parameters (560B total, 25B active) and context (1M claimed) are on the card. Training data 15, the training-content summary the card links names the source categories (Common Crawl and other public datasets, commercially licensed data, the Ant Spider crawler, synthetic data), the size band (over 10 trillion text tokens), data collected up to about July 2026 and the filtering, without listing datasets with sizes. Last change 20, the newest commit, on 4 November 2026, edited the card, six days before this check, and the files went up on 27 October. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (both opened on 27 October 2026) has a reply from an inclusionAI member. Version policy 5, the update went into a repository of its own and Ling-3.1-flash and Ling 3.0 stay in theirs. File health 5, the card states a 1M-token context while config.json gives 262,144 with no rope scaling, and its serve commands name the earlier repository as the model path. 11 + 15 + 20 + 0 + 5 + 5 = 56.\n- Qwen3-VL-30B-A3B-Instruct (36): 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 newest Hub commit is the library-tag commit of 26 November 2025 and the weights haven't changed since September 2025, so the qwen-vl-utils fix of 4 January 2026 in the code that runs it sets the line, within a year (5). Qwen answered none of the eight Hugging Face discussions that aren't pull requests (0), and GitHub issues weren't counted. Earlier versions kept in their own repositories without notes (5). Config, template and card agree and no broken-file report waits (10). 11 + 5 + 5 + 0 + 5 + 10 = 36.\n\n### Risk: Qwen3-VL-30B-A3B-Instruct +10\n\n- Ling-3.1-flash-1019 (45): File safety 15, safetensors only, no .bin, .pt or .pth files (25), less 10 because the card's documented vLLM command passes --trust-remote-code, which runs the repository's configuration_bailing_moe_v3.py, since vLLM 0.33.0 registers no bailing_hybrid config of its own. Provenance 15, files in the inclusionAI organisation that inclusion-ai.org links to, per the Ling-3.0-tiny listing (10). Safety evaluation 0, no safety results for the model on its card or in the training-content summary. Agent guidance 0, the card has nothing on prompt injection, refusals or safeguards, and inclusionAI publishes no guardrail probe for this model as it did for Ling 3.0. Licence risk 15, MIT with no use policy, no field-of-use limits, no threshold and no clause letting inclusionAI change or revoke it. Security programme 0, www.inclusion-ai.org answers 404 for security.txt on 10 November 2026, and the Ling-3.0-tiny listing found no SECURITY.md or disclosure policy in the inclusionAI .github repository. 15 + 15 + 0 + 0 + 15 + 0 = 45.\n- Qwen3-VL-30B-A3B-Instruct (55): Safetensors and GGUF only and no code in the repositories, so the FP8 card's trust_remote_code flag runs nothing (25). Files in the Qwen organisation with a checksum on every file (15). No safety evaluation (0). No guidance for agent use, which matters for a model sold as a GUI agent (0). Apache 2.0 carries none of the licence risks (15). No security contact, disclosure policy or public advisories found for Qwen (0), and Alibaba's group-wide security centre wasn't checked.\n\n## Score by category\n\n| Category | Weight | Ling-3.1-flash-1019 | Qwen3-VL-30B-A3B-Instruct | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 60 | 95 | Qwen3-VL-30B-A3B-Instruct +35 |\n| Risk | 20% (20 this run) | 45 | 55 | Qwen3-VL-30B-A3B-Instruct +10 |\n| Agent interface | 10% (10 this run) | 60 | 60 | even |\n| Documentation and upkeep | 10% (10 this run) | 56 | 36 | Ling-3.1-flash-1019 +20 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **54.8 · C** | **58 · C** | |\n\n## Facts side by side\n\n| Fact | Ling-3.1-flash-1019 | Qwen3-VL-30B-A3B-Instruct |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | inclusionAI (Ant Group) | 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 | MIT | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-04 | 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 | 3/5 (4) |\n\n## Verdicts\n\n**Ling-3.1-flash-1019.** inclusionAI's newest flagship, a 560B hybrid mixture of experts under plain MIT that vLLM, SGLang, Transformers and llama.cpp run in released versions. It ships only as 574 GB of FP8, so it needs a multi-GPU server. The card overstates the context, documents little about tool calls, and has no safety results.\n\n**Qwen3-VL-30B-A3B-Instruct.** A vision-language mixture of experts for screenshots, documents, charts and video, with about 3B of its 31B parameters active. Alibaba's Q4_K_M GGUF with its vision file needs about 23 GB, it loads in Transformers, vLLM, SGLang, llama.cpp and Ollama under Apache 2.0, and three providers host it. No safety work is published, so add guardrails before it drives a GUI.\n\n## Before you call either\n\n### Ling-3.1-flash-1019\n\n1. Serve it with SGLang 0.5.23 or later on 8 H200 or 4 GB300 GPUs, per the card, and use this repository's name as the model path, not the earlier one the card's command shows\n2. With vLLM pass `--trust-remote-code`, `--tool-call-parser ling3` and `--reasoning-parser ling3`\n3. Set `chat_template_kwargs` `enable_thinking` false for direct answers. Reasoning comes back in `reasoning_content` per SGLang's cookbook\n4. Plan on 262,144 tokens of context. The 1M figure needs the YaRN override in SGLang's cookbook\n5. Use temperature 1.0, top_p 0.95 and top_k 20, per the card\n6. Validate JSON output yourself, since structured output isn't documented\n\n### Qwen3-VL-30B-A3B-Instruct\n\n1. Start it with `llama-server -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:30b-a3b-instruct`\n2. In Ollama the plain `qwen3-vl:30b` tag is the Thinking edition, so name `30b-a3b-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 shipped sampling, temperature 0.7, top_p 0.8 and top_k 20, from generation_config.json\n6. Validate JSON output yourself, since structured output isn't documented\n7. vLLM needs 0.11.0 or later, Transformers 4.57.0 or later and llama.cpp b6907 or later\n\n## Questions\n\n### Which is better for AI agents, Ling-3.1-flash-1019 or Qwen3-VL-30B-A3B-Instruct?\n\nQwen3-VL-30B-A3B-Instruct scores 58 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 2 of 4 scored categories. Ling-3.1-flash-1019 leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-vl-30b-a3b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-vl-30b-a3b-instruct.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"ling-3-1-flash-1019\", \"b\": \"qwen3-vl-30b-a3b-instruct\"}`. 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[Qwen3-VL-30B-A3B-Instruct vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-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": "Ling-3.1-flash-1019 vs Qwen3-VL-30B-A3B-Instruct", "url": "" } ], "description": "Qwen3-VL-30B-A3B-Instruct scores 58 (C) to Ling-3.1-flash-1019's 54.8 (C) for open-weight models you run yourself. 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