{ "data": { "a": { "slug": "ornith-1-5-35b-a3b", "name": "Ornith-1.5-35B-A3B", "vendor": "Ornith", "vendorUrl": "https://ornith.ai", "kind": "weights", "category": "open-weights", "summary": "Ornith-1.5-35B-A3B is Ornith's open-weight mixture-of-experts reasoning model for coding and agent work, about 35B parameters with 3B active per token on Qwen3.5's architecture, with a 262,144-token context, published in BF16, FP8, NVFP4, GGUF and MLX and marked MIT.", "url": "https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b", "markdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ornith-1-5-35b-a3b.json", "repo": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-35B-A3B" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-35B-A3B-GGUF" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-35B-A3B-FP8" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-35B-A3B-NVFP4" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-35B-A3B-MLX-4bit" }, { "registry": "ollama", "name": "ornith-1.5:35b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and all eight of Ornith's build repositories for this size are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the MIT licence named in the card's metadata. The repository has no LICENSE file, and the card's licence link returns 404. Running it yourself costs the hardware, about 72 GB for the BF16 files, 19.5 GB for Ornith's MLX 4-bit build or 21.7 GB 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/ornith-ai/Ornith-1.5-35B-A3B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "coding", "mit", "safetensors", "gguf", "mlx", "fp8", "nvfp4", "self-hosted" ], "lastRelease": "2026-08-23", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 36, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 85, "reach": 90, "risk": 50, "upkeep": 31 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A mid-size coding and terminal-agent cousin with about 3B active parameters, XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It runs in about 24 GB from Ornith's MLX 4-bit build or 32 GB from its GGUF. The main risk is an absent publisher, with no licence file, safety results or replies to 35 reports.", "bestFor": "Local coding and terminal agents on a 24 to 32 GB GPU or a Mac, where a clear licence file and an answering publisher aren't required", "strengths": [ "Fits in about 24 GB from Ornith's MLX 4-bit build and 32 GB from its Q4_K_M GGUF, NVFP4 or Ollama's build, with only about 3B parameters active per token", "Runs in vLLM, SGLang, Transformers, llama.cpp and Ollama from their released versions", "The card names the tool and reasoning parsers for vLLM and SGLang, with a tool-call example", "262,144 tokens of native context", "Safetensors and GGUF only, no gate and no code in the repository" ], "weaknesses": [ "No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on", "No safety evaluation, agent-use guidance or security contact", "A weight shard, every GGUF, an FP8 shard and the NVFP4 shards were replaced in place between 23 and 26 August 2026 with no note", "Ornith answered none of the 35 discussions", "The two chat templates disagree on keeping earlier reasoning, and structured output isn't documented" ], "agentNotes": [ "Start it with `ollama run ornith-1.5:35b`, or `llama-server -hf ornith-ai/Ornith-1.5-35B-A3B-GGUF --port 8000 -c 262144` as the card gives it", "In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed", "Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention", "Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it", "Use temperature 0.6, top_p 0.95, top_k 20 for general tasks and temperature 1.0 to reproduce the card's benchmarks", "Pin a revision when you download, since Ornith has replaced files in place, and add mmproj-Ornith-1.5-35B-BF16.gguf (0.9 GB) for image input in llama.cpp", "Validate JSON output yourself, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.3, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 85, "reach": 90, "risk": 50, "upkeep": 31 }, "provenanceScore": 13, "checked": { "categories": { "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run ornith-1.5:35b\n# or, with the card's vLLM command:\nvllm serve ornith-ai/Ornith-1.5-35B-A3B --served-model-name Ornith-1.5-35B-A3B --tensor-parallel-size 2 --max-model-len 262144 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"ornith-1.5:35b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/ornith-1-5-35b-a3b" }, "sameCompany": [ "ornith-1-5-397b", "ornith-1-5-9b" ], "weights": { "trainedBy": "Ornith", "builtOn": "Qwen3.5-35B-A3B, per the card's text (Ornith-1.0, which Ornith says it built on Qwen3.5 and Gemma 4, extended in 1.5); not declared in the metadata. Same qwen3_5_moe architecture and an identical parameter count to Qwen3.5-35B-A3B (Apache 2.0)", "params": "35B (3B active)", "context": 262144, "goodFor": "Coding and terminal-agent mixture-of-experts model with tool calling and thinking on by default, text and images in, fast for its size with about 3B active parameters.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Ornith-1.5-35B-A3B-MLX-4bit", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-MLX-4bit", "who": "publisher", "precision": "4bit", "sizeGB": 19.5, "memoryGB": 24, "format": "mlx", "note": "MLX runs on Apple silicon only" }, { "name": "Ornith-1.5-35B-A3B-NVFP4", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 23.5, "memoryGB": 32, "format": "safetensors", "note": "NVFP4 needs NVIDIA Blackwell" }, { "name": "Ornith-1.5-35B-Q5_K_M", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-GGUF/blob/main/Ornith-1.5-35B-Q5_K_M.gguf", "who": "publisher", "precision": "q5_k_m", "sizeGB": 25.3, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "Ornith-1.5-35B-Q4_K_M", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-GGUF/blob/main/Ornith-1.5-35B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 21.7, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Add mmproj-Ornith-1.5-35B-BF16.gguf (0.9 GB) for image input" }, { "name": "ollama ornith-1.5:35b", "url": "https://ollama.com/library/ornith-1.5:35b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 23, "memoryGB": 32, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M (22 GB) with a BF16 vision projector (903 MB), linked from Ornith's card; the library has no latest tag. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB" }, { "name": "Ornith-1.5-35B-A3B-FP8", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 39.4, "memoryGB": 48, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ] }, { "name": "Ornith-1.5-35B-Q8_0", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-GGUF/blob/main/Ornith-1.5-35B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 37.8, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "Ornith-1.5-35B-Q6_K", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-GGUF/blob/main/Ornith-1.5-35B-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 29.2, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "Ornith-1.5-35B-A3B-MLX-8bit", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-MLX-8bit", "who": "publisher", "precision": "8bit", "sizeGB": 36.9, "memoryGB": 48, "format": "mlx", "note": "MLX runs on Apple silicon only" }, { "name": "Ornith-1.5-35B-A3B-MLX-6bit", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-MLX-6bit", "who": "publisher", "precision": "6bit", "sizeGB": 28.2, "memoryGB": 48, "format": "mlx", "note": "MLX runs on Apple silicon only" }, { "name": "Ornith-1.5-35B-A3B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B", "who": "publisher", "precision": "bf16", "sizeGB": 71.9, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] }, { "name": "Ornith-1.5-35B-BF16", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-GGUF/blob/main/Ornith-1.5-35B-BF16.gguf", "who": "publisher", "precision": "bf16", "sizeGB": 71.1, "memoryGB": 96, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "Ornith-1.5-35B-A3B-MLX", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-MLX", "who": "publisher", "precision": "bf16", "sizeGB": 69.3, "memoryGB": 96, "format": "mlx", "note": "MLX runs on Apple silicon only" } ], "run": "ollama run ornith-1.5:35b", "runBuild": "ollama ornith-1.5:35b", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "Ornith", "family": "Ornith-1", "url": "https://www.anchorterminal.com/open-weights/#ornith", "others": [ { "name": "Ornith-1.0-9B", "url": "https://huggingface.co/ornith-ai/Ornith-1.0-9B", "params": "9B (dense)" }, { "name": "Ornith-1.0-35B", "url": "https://huggingface.co/ornith-ai/Ornith-1.0-35B", "params": "35B (MoE)" }, { "name": "Ornith-1.0-397B", "url": "https://huggingface.co/ornith-ai/Ornith-1.0-397B", "params": "397B (MoE)" }, { "name": "Ornith-1.5-9B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B", "params": "9B (dense)", "listing": "https://www.anchorterminal.com/tools/ornith-1-5-9b" }, { "name": "Ornith-1.5-9B-DFlash", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-DFlash", "params": "1.29B (Hub safetensors count)" }, { "name": "Ornith-1.5-35B-A3B-DFlash", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-DFlash", "params": "0.39B (Hub safetensors count)" }, { "name": "Ornith-1.5-122B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B", "params": "122B (MoE)" }, { "name": "Ornith-1.5-397B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B", "params": "397B (MoE)", "listing": "https://www.anchorterminal.com/tools/ornith-1-5-397b" }, { "name": "Ornith-1.5-397B-DFlash", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-DFlash", "params": "1.29B (Hub safetensors count)" } ], "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "", "domain": "ornith.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "No legal entity named on the card, the blog or the repository. The citation reads Ornith Team, and the Hugging Face organisation ornith-ai (team plan, 10 members) isn't verified.", "The card's blog badge links deep-reinforce.com/ornith.html, which redirects to Ornith's 1.0 blog post on ornith.ai.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored. The licence is MIT in the card's metadata only; the LICENSE file the card links returns 404.", "No changelog for the files. Ollama's library page carries Ornith's dated release lines (19 August and 20 October 2026), and the commit messages are uploads, deletions and README updates.", "ornith.ai/.well-known/security.txt redirects to the home page, so there is no security.txt.", "unchecked: the domain's registration date." ], "score": 13 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b.json" }, "answer": "Ornith-1.5-35B-A3B scores 61 (C) on agent readiness against Qwen3-VL-4B-Instruct's 52.6 (D), and leads in 1 of 4 scored categories. Qwen3-VL-4B-Instruct leads on reach and risk.", "b": { "slug": "qwen3-vl-4b-instruct", "name": "Qwen3-VL-4B-Instruct", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3-VL-4B-Instruct is Alibaba's open-weight dense vision-language model of about 4B 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-4b-instruct", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-vl-4b-instruct.json", "repo": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3-VL-4B-Instruct" }, { "registry": "huggingface", "name": "Qwen/Qwen3-VL-4B-Instruct-FP8" }, { "registry": "huggingface", "name": "Qwen/Qwen3-VL-4B-Instruct-GGUF" }, { "registry": "ollama", "name": "qwen3-vl:4b-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, 8.89 GB for the BF16 files, 6.04 GB for Alibaba's FP8 build and 2.5 GB plus a 0.45 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-4B-Instruct", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "vision", "video", "tool-calling", "apache-2.0", "safetensors", "fp8", "gguf", "self-hosted", "edge" ], "lastRelease": "2025-11-01", "graded": true, "anchor": { "graded": true, "score": 52.6, "grade": "D", "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": 1197, "categoryRank": 68, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 25, "interface": 60, "reach": 95, "risk": 55, "upkeep": 31 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A small vision-language cousin for reading screenshots, documents, charts and video, running in about 3.5 GB from Alibaba's Q4_K_M GGUF in every major runtime under plain Apache 2.0. It has no thinking mode, and Qwen hasn't answered repository questions or published safety work, so add your own guardrails before letting it drive a GUI.", "bestFor": "An agent with 4 GB or more to spare that needs a local model to read images, documents and video", "strengths": [ "About 3.5 GB of memory for Alibaba's own Q4_K_M GGUF with its vision file, 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" ], "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 14 Hugging Face discussions that aren't pull requests", "The card leaves out limitations, languages, knowledge cut-off and memory needs", "Only Featherless was found serving it for agents without the hardware" ], "agentNotes": [ "Start it with `llama-server -hf Qwen/Qwen3-VL-4B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:4b-instruct`", "In Ollama the plain `qwen3-vl:4b` tag is the Thinking edition, so name `4b-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" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "D", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 52.6 } ], "editorialScores": { "capability": 25, "interface": 60, "reach": 95, "risk": 55, "upkeep": 31 }, "provenanceScore": 49, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run qwen3-vl:4b-instruct\n# or with vLLM 0.11.0 or later, per Qwen's README:\nvllm serve Qwen/Qwen3-VL-4B-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-4B-Instruct --tool-call-parser qwen", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"qwen3-vl:4b-instruct\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen3-vl-4b-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", "qwen3-30b-a3b-instruct-2507", "qwen3-4b-instruct-2507", "qwen3-coder-30b-a3b-instruct", "qwen3-coder-next", "qwen3-omni-30b-a3b-instruct", "qwen3-vl-2b-instruct", "qwen3-vl-30b-a3b-instruct", "qwen3-vl-32b-instruct", "qwen3-vl-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": "4.44B", "context": 262144, "goodFor": "Small vision-language model for images, documents and video with text out, fits in 8 GB at 4-bit, no thinking mode in this Instruct edition.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Qwen3-VL-4B-Instruct-FP8", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 6.04, "memoryGB": 8, "format": "safetensors" }, { "name": "Qwen3VL-4B-Instruct-Q8_0", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct-GGUF/blob/main/Qwen3VL-4B-Instruct-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 4.73, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Size includes mmproj-Qwen3VL-4B-Instruct-Q8_0.gguf (0.45 GB), the vision file" }, { "name": "Qwen3VL-4B-Instruct-Q4_K_M", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct-GGUF/blob/main/Qwen3VL-4B-Instruct-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 2.95, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "2.5 GB language file plus mmproj-Qwen3VL-4B-Instruct-Q8_0.gguf (0.45 GB) for image input" }, { "name": "ollama qwen3-vl:4b-instruct", "url": "https://ollama.com/library/qwen3-vl:4b-instruct", "who": "ollama", "precision": "q4_k_m", "sizeGB": 3.3, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M with its vision projector (digest 84f7ed8042fa, updated the day before our check). qwen3-vl:4b, the library default, is the Thinking edition. Size as Ollama's tags page shows it" }, { "name": "Qwen3-VL-4B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct", "who": "publisher", "precision": "bf16", "sizeGB": 8.89, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] }, { "name": "Qwen3VL-4B-Instruct-F16", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct-GGUF/blob/main/Qwen3VL-4B-Instruct-F16.gguf", "who": "publisher", "precision": "f16", "sizeGB": 8.89, "memoryGB": 12, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Size includes mmproj-Qwen3VL-4B-Instruct-F16.gguf (0.84 GB), the vision file" } ], "run": "llama-server -hf Qwen/Qwen3-VL-4B-Instruct-GGUF:Q4_K_M --jinja -c 32768", "runBuild": "Qwen3VL-4B-Instruct-Q4_K_M", "checked": "2026-10-21", "minMemoryGB": 8 }, "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-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 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": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Ornith", "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": "2026-08-23", "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": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "2.3/5 (4)", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Ornith-1.5-35B-A3B scores 61 (C) on agent readiness against Qwen3-VL-4B-Instruct's 52.6 (D), and leads in 1 of 4 scored categories. Qwen3-VL-4B-Instruct leads on reach and risk.", "question": "Which is better for AI agents, Ornith-1.5-35B-A3B or Qwen3-VL-4B-Instruct?" } ], "goodFor": [ { "aheadOn": [ "Agent interface, 85 against 60" ], "also": null, "goodFor": "Local coding and terminal agents on a 24 to 32 GB GPU or a Mac, where a clear licence file and an answering publisher aren't required", "slug": "ornith-1-5-35b-a3b", "watchFor": "No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on" }, { "aheadOn": [ "Reach, 95 against 90", "Risk, 55 against 50" ], "also": [ "Rated higher by the reviewer agents, 2.8 against 2.3 out of 5" ], "goodFor": "An agent with 4 GB or more to spare that needs a local model to read images, documents and video", "slug": "qwen3-vl-4b-instruct", "watchFor": "No safety evaluation, agent-use guidance or security contact" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { 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"title": "Kimi K3 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-35b-a3b.json", "title": "Kolibri-1 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-4b-instruct.json", "title": "Kolibri-1 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-35b-a3b.json", "title": "Laguna M.1 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-4b-instruct.json", "title": "Laguna M.1 vs Qwen3-VL-4B-Instruct", "url": 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"json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-35b-a3b.json", "title": "LFM2.5-2.6B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-4b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-ornith-1-5-35b-a3b.json", "title": "Ling-3.0-tiny vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-4b-instruct.json", "title": "Ling-3.0-tiny vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-4b-instruct" }, { "json": 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"title": "Ornith-1.5-35B-A3B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-8b-instruct.json", "title": "Ornith-1.5-35B-A3B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwopus3-8-27b-flash-v2.json", "title": "Ornith-1.5-35B-A3B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-smollm3-3b.json", "title": "Ornith-1.5-35B-A3B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-spark-x2-6-4b.json", "title": "Ornith-1.5-35B-A3B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-swift-1-5-qwen3-8-27b.json", "title": "Ornith-1.5-35B-A3B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Ornith-1.5-35B-A3B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-thinkingcap-qwen3-8-27b.json", "title": "Ornith-1.5-35B-A3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-4b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-4b-instruct.json", "title": "Ornith-1.5-9B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3-30B-A3B-Instruct-2507 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3-4B-Instruct-2507 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.5-0.8B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.5-122B-A10B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.5-2B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.5-397B-A17B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.5-4B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.5-9B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.6-27B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.6-35B-A3B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.8-27B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.8-Flash-Next vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3-Coder-Next vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3-VL-2B-Instruct vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwen3-vl-4b-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-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-4b-instruct-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3-VL-4B-Instruct vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-VL-4B-Instruct vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-smollm3-3b.json", "title": "Qwen3-VL-4B-Instruct vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-spark-x2-6-4b.json", "title": "Qwen3-VL-4B-Instruct vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3-VL-4B-Instruct vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3-VL-4B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-VL-4B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 5, "edge": "qwen3-vl-4b-instruct", "key": "reach", "name": "Reach", "ornith-1-5-35b-a3b": 90, "qwen3-vl-4b-instruct": 95, "weight": 25 }, { "by": 5, "edge": "qwen3-vl-4b-instruct", "key": "risk", "name": "Risk", "ornith-1-5-35b-a3b": 50, "qwen3-vl-4b-instruct": 55, "weight": 20 }, { "by": 25, "edge": "ornith-1-5-35b-a3b", "key": "interface", "name": "Agent interface", "ornith-1-5-35b-a3b": 85, "qwen3-vl-4b-instruct": 60, "weight": 10 }, { "by": 0, "edge": "", "key": "upkeep", "name": "Documentation and upkeep", "ornith-1-5-35b-a3b": 31, "qwen3-vl-4b-instruct": 31, "weight": 10 } ], "summary": "Ornith-1.5-35B-A3B scores 61 (C) on agent readiness against Qwen3-VL-4B-Instruct's 52.6 (D), and leads in 1 of 4 scored categories. Qwen3-VL-4B-Instruct leads on reach and risk. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "ornith-1-5-35b-a3b": "A mid-size coding and terminal-agent cousin with about 3B active parameters, XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It runs in about 24 GB from Ornith's MLX 4-bit build or 32 GB from its GGUF. The main risk is an absent publisher, with no licence file, safety results or replies to 35 reports.", "qwen3-vl-4b-instruct": "A small vision-language cousin for reading screenshots, documents, charts and video, running in about 3.5 GB from Alibaba's Q4_K_M GGUF in every major runtime under plain Apache 2.0. It has no thinking mode, and Qwen hasn't answered repository questions or published safety work, so add your own guardrails before letting it drive a GUI." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-4b-instruct", "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-4b-instruct.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-4b-instruct.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-4b-instruct.min.md" }, "markdown": "Ornith-1.5-35B-A3B scores 61 (C) on agent readiness against Qwen3-VL-4B-Instruct's 52.6 (D), and leads in 1 of 4 scored categories. Qwen3-VL-4B-Instruct leads on reach and risk. Both do open-weight models you run yourself.\n\n- Ornith-1.5-35B-A3B: grade C, 61/100, rank #43 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-35b-a3b.json\n- Qwen3-VL-4B-Instruct: grade D, 52.6/100, rank #84 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-vl-4b-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 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Ornith-1.5-35B-A3B (C)\n\nGood for: Local coding and terminal agents on a 24 to 32 GB GPU or a Mac, where a clear licence file and an answering publisher aren't required\n\nAhead on:\n- Agent interface, 85 against 60\n\nWatch for: No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on\n\n### Qwen3-VL-4B-Instruct (D)\n\nGood for: An agent with 4 GB or more to spare that needs a local model to read images, documents and video\n\nAhead on:\n- Reach, 95 against 90\n- Risk, 55 against 50\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 2.8 against 2.3 out of 5\n\nWatch for: No safety evaluation, agent-use guidance or security contact\n\n\n## Score by category\n\n| Category | Weight | Ornith-1.5-35B-A3B | Qwen3-VL-4B-Instruct | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 95 | Qwen3-VL-4B-Instruct +5 |\n| Risk | 20% (20 this run) | 50 | 55 | Qwen3-VL-4B-Instruct +5 |\n| Agent interface | 10% (10 this run) | 85 | 60 | Ornith-1.5-35B-A3B +25 |\n| Documentation and upkeep | 10% (10 this run) | 31 | 31 | even |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **52.6 · D** | |\n\n## Facts side by side\n\n| Fact | Ornith-1.5-35B-A3B | Qwen3-VL-4B-Instruct |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Ornith | 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 | 2026-08-23 | 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| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 2.3/5 (4) | 2.8/5 (4) |\n\n## Verdicts\n\n**Ornith-1.5-35B-A3B.** A mid-size coding and terminal-agent cousin with about 3B active parameters, XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It runs in about 24 GB from Ornith's MLX 4-bit build or 32 GB from its GGUF. The main risk is an absent publisher, with no licence file, safety results or replies to 35 reports.\n\n**Qwen3-VL-4B-Instruct.** A small vision-language cousin for reading screenshots, documents, charts and video, running in about 3.5 GB from Alibaba's Q4_K_M GGUF in every major runtime under plain Apache 2.0. It has no thinking mode, and Qwen hasn't answered repository questions or published safety work, so add your own guardrails before letting it drive a GUI.\n\n## Before you call either\n\n### Ornith-1.5-35B-A3B\n\n1. Start it with `ollama run ornith-1.5:35b`, or `llama-server -hf ornith-ai/Ornith-1.5-35B-A3B-GGUF --port 8000 -c 262144` as the card gives it\n2. In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed\n3. Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n4. Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it\n5. Use temperature 0.6, top_p 0.95, top_k 20 for general tasks and temperature 1.0 to reproduce the card's benchmarks\n6. Pin a revision when you download, since Ornith has replaced files in place, and add mmproj-Ornith-1.5-35B-BF16.gguf (0.9 GB) for image input in llama.cpp\n7. Validate JSON output yourself, since structured output isn't documented\n\n### Qwen3-VL-4B-Instruct\n\n1. Start it with `llama-server -hf Qwen/Qwen3-VL-4B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:4b-instruct`\n2. In Ollama the plain `qwen3-vl:4b` tag is the Thinking edition, so name `4b-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\n\n## Questions\n\n### Which is better for AI agents, Ornith-1.5-35B-A3B or Qwen3-VL-4B-Instruct?\n\nOrnith-1.5-35B-A3B scores 61 (C) on agent readiness against Qwen3-VL-4B-Instruct's 52.6 (D), and leads in 1 of 4 scored categories. Qwen3-VL-4B-Instruct leads on reach and risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-4b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-4b-instruct.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"ornith-1-5-35b-a3b\", \"b\": \"qwen3-vl-4b-instruct\"}`. From a terminal: `anchor compare ornith-1-5-35b-a3b qwen3-vl-4b-instruct`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/ornith-1-5-35b-a3b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-vl-4b-instruct.json\n\n## Other comparisons with Ornith-1.5-35B-A3B or Qwen3-VL-4B-Instruct\n\n- [Agents-A1-4B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-35b-a3b.md)\n- [Agents-A1-4B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-vl-4b-instruct.md)\n- [Apertus v1.5 8B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-35b-a3b.md)\n- [Apertus v1.5 8B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-vl-4b-instruct.md)\n- [Apodex-1.1-mini vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-35b-a3b.md)\n- [Apodex-1.1-mini vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-vl-4b-instruct.md)\n- [BigBang-v1 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-35b-a3b.md)\n- [BigBang-v1 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-vl-4b-instruct.md)\n- [Cosmos-Reason2-2B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-35b-a3b.md)\n- [Cosmos-Reason2-2B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-vl-4b-instruct.md)\n- [Darwin-36B-Opus vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-35b-a3b.md)\n- [Darwin-36B-Opus vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-vl-4b-instruct.md)\n- [DeepSeek-V3.2 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-35b-a3b.md)\n- [DeepSeek-V3.2 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-vl-4b-instruct.md)\n- [DeepSeek V4.1 Flash vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-35b-a3b.md)\n- [DeepSeek V4.1 Flash vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-4b-instruct.md)\n- [DeepSeek-V4-Flash-0731 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-35b-a3b.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-vl-4b-instruct.md)\n- [DiffusionGemma 26B A4B IT vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-35b-a3b.md)\n- [DiffusionGemma 26B A4B IT vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-vl-4b-instruct.md)\n- [Gemma 4 12B IT vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-35b-a3b.md)\n- [Gemma 4 12B IT vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-vl-4b-instruct.md)\n- [Gemma 4 26B A4B IT vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-35b-a3b.md)\n- [Gemma 4 26B A4B IT vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-vl-4b-instruct.md)\n- [Gemma 4 31B IT vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-35b-a3b.md)\n- [Gemma 4 31B IT vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-4b-instruct.md)\n- [Gemma 4 E2B IT vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-35b-a3b.md)\n- [Gemma 4 E2B IT vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-vl-4b-instruct.md)\n- [Gemma 4 E4B IT vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-35b-a3b.md)\n- [Gemma 4 E4B IT vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-vl-4b-instruct.md)\n- [GLM-4.5-Air vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-35b-a3b.md)\n- [GLM-4.5-Air vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-vl-4b-instruct.md)\n- [GLM-4.7-Flash vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-35b-a3b.md)\n- [GLM-4.7-Flash vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-4b-instruct.md)\n- [GLM-5.3-Flash vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-35b-a3b.md)\n- [GLM-5.3-Flash vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-4b-instruct.md)\n- [GLM-5.3 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-35b-a3b.md)\n- [GLM-5.3 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-vl-4b-instruct.md)\n- [gpt-oss-120b vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ornith-1-5-35b-a3b.md)\n- [gpt-oss-120b vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-vl-4b-instruct.md)\n- [gpt-oss-20b vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ornith-1-5-35b-a3b.md)\n- [gpt-oss-20b vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-vl-4b-instruct.md)\n- [Granite 4.2 3B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-35b-a3b.md)\n- [Granite 4.2 3B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-vl-4b-instruct.md)\n- [Hy3 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-35b-a3b.md)\n- [Hy3 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-4b-instruct.md)\n- [Inkling-Small vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/inkling-small-vs-ornith-1-5-35b-a3b.md)\n- [Inkling-Small vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-vl-4b-instruct.md)\n- [Inkling vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-35b-a3b.md)\n- [Inkling vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/inkling-vs-qwen3-vl-4b-instruct.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ornith-1-5-35b-a3b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-vl-4b-instruct.md)\n- [Kimi K3 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-35b-a3b.md)\n- [Kimi K3 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-4b-instruct.md)\n- [Kolibri-1 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-35b-a3b.md)\n- [Kolibri-1 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-4b-instruct.md)\n- [Laguna M.1 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-35b-a3b.md)\n- [Laguna M.1 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-4b-instruct.md)\n- [Laguna S 2.1 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-35b-a3b.md)\n- [Laguna S 2.1 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-4b-instruct.md)\n- [Laguna XS 2.1 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-35b-a3b.md)\n- [Laguna XS 2.1 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-4b-instruct.md)\n- [LFM2.5-2.6B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-35b-a3b.md)\n- [LFM2.5-2.6B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-4b-instruct.md)\n- [Ling-3.0-tiny vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-ornith-1-5-35b-a3b.md)\n- [Ling-3.0-tiny vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-4b-instruct.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-ornith-1-5-35b-a3b.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-vl-4b-instruct.md)\n- [MiniCPM-o 4.5 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-35b-a3b.md)\n- [MiniCPM-o 4.5 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-4b-instruct.md)\n- [MiniCPM5-2B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/minicpm5-2b-vs-ornith-1-5-35b-a3b.md)\n- [MiniCPM5-2B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-4b-instruct.md)\n- [MiniMax-M2.5 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-ornith-1-5-35b-a3b.md)\n- [MiniMax-M2.5 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-4b-instruct.md)\n- [MiniMax-M2.7 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-35b-a3b.md)\n- [MiniMax-M2.7 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-4b-instruct.md)\n- [Muse Glimmer 30B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-35b-a3b.md)\n- [Muse Glimmer 30B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-4b-instruct.md)\n- [Nemotron 3.5 Lightning 30B A3B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-35b-a3b.md)\n- [Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-4b-instruct.md)\n- [Nemotron 3 Nano 30B A3B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ornith-1-5-35b-a3b.md)\n- [Nemotron 3 Nano 30B A3B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-4b-instruct.md)\n- [Nemotron 3 Nano 4B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-35b-a3b.md)\n- [Nemotron 3 Nano 4B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-4b-instruct.md)\n- [Nemotron 3 Nano Omni 30B A3B Reasoning vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-35b-a3b.md)\n- [Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-vl-4b-instruct.md)\n- [Nemotron 3 Super 120B A12B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-35b-a3b.md)\n- [Nemotron 3 Super 120B A12B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-vl-4b-instruct.md)\n- [Nemotron 3 Ultra 550B A55B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-35b-a3b.md)\n- [Nemotron 3 Ultra 550B A55B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-vl-4b-instruct.md)\n- [occamy-1.0 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-35b-a3b.md)\n- [occamy-1.0 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-4b-instruct.md)\n- [Olmo 3 7B Think vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-35b-a3b.md)\n- [Olmo 3 7B Think vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-4b-instruct.md)\n- [Ornith-1.5-35B-A3B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ornith-1-5-397b.md)\n- [Ornith-1.5-35B-A3B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ornith-1-5-9b.md)\n- [Ornith-1.5-35B-A3B vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Ornith-1.5-35B-A3B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-4b-instruct-2507.md)\n- [Ornith-1.5-35B-A3B vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-0-8b.md)\n- [Ornith-1.5-35B-A3B vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-122b-a10b.md)\n- [Ornith-1.5-35B-A3B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-2b.md)\n- [Ornith-1.5-35B-A3B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-397b-a17b.md)\n- [Ornith-1.5-35B-A3B vs Qwen3.5-4B](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-4b.md)\n- [Ornith-1.5-35B-A3B vs Qwen3.5-9B](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-9b.md)\n- [Ornith-1.5-35B-A3B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-6-27b.md)\n- [Ornith-1.5-35B-A3B vs 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