{ "data": { "a": { "slug": "gemma-4-12b-it", "name": "Gemma 4 12B IT", "vendor": "Google DeepMind", "vendorUrl": "https://gemma.ai", "kind": "weights", "category": "open-weights", "summary": "Gemma 4 12B IT is Google DeepMind's open-weight instruction-tuned dense model, 11.95B parameters in an encoder-free design, taking text, images, video frames and audio with a 262,144-token context, published on Hugging Face under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/gemma-4-12b-it", "markdownUrl": "https://www.anchorterminal.com/tools/gemma-4-12b-it.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemma-4-12b-it.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemma-4-12b-it.json", "repo": "https://huggingface.co/google/gemma-4-12B-it", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "google/gemma-4-12B-it" }, { "registry": "huggingface", "name": "google/gemma-4-12B-it-qat-q4_0-gguf" }, { "registry": "huggingface", "name": "google/gemma-4-12B-it-qat-w4a16-ct" }, { "registry": "ollama", "name": "gemma4:12b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and Google's QAT repositories are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. No hosted version, since the Gemini API serves only the 26B A4B and 31B sizes and Hugging Face lists no inference provider for it. Running it yourself costs the hardware, 26.7 GB at BF16, 13.4 GB at SFP8 and 6.7 GB at Q4_0 to load the weights per Google (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://gemma.ai/models/gemma-4/", "llmsTxt": "https://gemma.ai/llms.txt", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "audio", "reasoning", "tool-calling", "apache-2.0", "safetensors", "gguf", "qat", "self-hosted" ], "lastRelease": "2026-07-20", "graded": true, "anchor": { "graded": true, "score": 71.3, "grade": "BB", "agentReady": true, "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": 3, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 50, "interface": 85, "reach": 95, "risk": 70, "upkeep": 75 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A mid-size cousin for agent work with tools, optional thinking and image and audio input, under Apache 2.0. Google's 4-bit build needs under 9 GB with working space, so it runs on a 12 GB GPU or any recent Mac in all the common runtimes. The main risks are no published safety results and no documented JSON output.", "bestFor": "Agents on a 12 GB or larger GPU or Mac that want a permissively licensed tool-calling model with images, audio and long context", "strengths": [ "Plain Apache 2.0, no account or gate, safetensors and GGUF only with a checksum on every file", "Google's own 4-bit QAT GGUF needs about 8.6 GB with working space", "Released support in vLLM, SGLang, llama.cpp, Ollama, LM Studio and Transformers", "Tool declaration and call format documented by Google, with parallel calls and a shipped response parser", "262,144 tokens of native context and thinking that turns on or off per request", "Takes audio as well as images and video frames" ], "weaknesses": [ "No safety evaluation figures published", "No documented way to ask for JSON or schema output", "No hosted version, since the Gemini API serves only the two largest sizes", "Training data described only by type and cut-off", "Files unchanged since 20 July 2026, and a startup crash report waits without a reply", "A newer, separate architecture (gemma4_unified) that needs Transformers 5.10.0 or later" ], "agentNotes": [ "On the reference machine run `llama-server -hf google/gemma-4-12B-it-qat-q4_0-gguf --hf-file gemma-4-12b-it-qat-q4_0.gguf --jinja -c 131072`, and add `--mmproj` with mmproj-gemma-4-12b-it-qat-q4_0.gguf for images and audio", "With Ollama run `ollama run gemma4:12b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K", "In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text; Google's w4a16 build is the 4-bit route there", "Thinking is off by default; pass enable_thinking=True or start the system prompt with \u003c|think|\u003e, and drop earlier turns' thinking from history except on tool-call turns", "Put images before the text and audio after it, keep audio to 30 seconds and video to 60 frames, and validate tool names and arguments before running them", "Transformers needs 5.10.0 or later for gemma4_unified, and mlx-lm has no gemma4_unified code, so on a Mac use mlx-vlm, LM Studio or Ollama" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "BB", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 71.3 } ], "editorialScores": { "capability": 50, "interface": 85, "reach": 95, "risk": 70, "upkeep": 75 }, "provenanceScore": 68, "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 gemma4:12b\n# or, with llama.cpp and Google's QAT build:\nllama-server -hf google/gemma-4-12B-it-qat-q4_0-gguf --hf-file gemma-4-12b-it-qat-q4_0.gguf --jinja -c 131072", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"gemma4:12b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"options\": {\"num_ctx\": 65536}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/gemma-4-12b-it" }, "sameCompany": [ "embeddinggemma-300m", "diffusiongemma-26b-a4b-it", "gemma-4-26b-a4b-it", "gemma-4-31b-it", "gemma-4-e2b-it", "gemma-4-e4b-it" ], "weights": { "trainedBy": "Google DeepMind", "builtOn": "google/gemma-4-12B", "params": "11.95B", "context": 262144, "goodFor": "Mid-size dense assistant and agent model for a laptop GPU, with tool calling and optional thinking, taking text, images, video frames and audio without separate encoders, and a 256K context.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "gemma-4-12b-it-qat-q4_0", "url": "https://huggingface.co/google/gemma-4-12B-it-qat-q4_0-gguf/blob/main/gemma-4-12b-it-qat-q4_0.gguf", "who": "publisher", "precision": "q4_0", "sizeGB": 7.15, "memoryGB": 12, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)", "LM Studio" ], "note": "Quantisation-aware trained by Google. Size is the repository's two files summed, the 6.98 GB model and the 0.18 GB projector (mmproj-gemma-4-12b-it-qat-q4_0.gguf) for image and audio input" }, { "name": "ollama gemma4:12b", "url": "https://ollama.com/library/gemma4:12b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 8, "memoryGB": 12, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M weights (7.4 GB) with a BF16 projector (175 MB) and a Q8_0 draft model (465 MB) (digest 6114515d63c1), the llama.cpp variant. gemma4:12b-it-qat (7.2 GB) is Ollama's copy of Google's Q4_0 QAT build" }, { "name": "lmstudio google/gemma-4-12b", "url": "https://lmstudio.ai/models/google/gemma-4-12b", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 7.4, "memoryGB": 12, "format": "gguf", "runsOn": [ "LM Studio" ], "note": "LM Studio's catalogue build, from lmstudio-community/gemma-4-12B-it-GGUF, with MLX builds at 4 to 8 bits beside it. Size as the catalogue shows it, and Q4_K_M is inferred from that repository's files rather than stated by LM Studio" }, { "name": "gemma-4-12B-it-qat-w4a16-ct", "url": "https://huggingface.co/google/gemma-4-12B-it-qat-w4a16-ct", "who": "publisher", "precision": "w4a16", "sizeGB": 10.3, "memoryGB": 16, "format": "safetensors", "note": "Quantisation-aware trained, 4-bit weights with 16-bit activations in compressed-tensors format, which Google routes to vLLM and SGLang" }, { "name": "gemma-4-12B-it", "url": "https://huggingface.co/google/gemma-4-12B-it", "who": "publisher", "precision": "bf16", "sizeGB": 23.95, "memoryGB": 32, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "llama-server -hf google/gemma-4-12B-it-qat-q4_0-gguf --hf-file gemma-4-12b-it-qat-q4_0.gguf --jinja -c 131072", "runBuild": "gemma-4-12b-it-qat-q4_0", "checked": "2026-10-21", "minMemoryGB": 12 }, "family": { "lab": "Google (Gemma)", "family": "Gemma 4", "url": "https://www.anchorterminal.com/open-weights/#google", "others": [ { "name": "gemma-4-26B-A4B", "url": "https://huggingface.co/google/gemma-4-26B-A4B", "params": "25.2B total, 3.8B active" }, { "name": "gemma-4-26B-A4B-it", "url": "https://huggingface.co/google/gemma-4-26B-A4B-it", "params": "25.2B total, 3.8B active", "listing": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it" }, { "name": "gemma-4-31B", "url": "https://huggingface.co/google/gemma-4-31B", "params": "30.7B" }, { "name": "gemma-4-31B-it", "url": "https://huggingface.co/google/gemma-4-31B-it", "params": "30.7B", "listing": "https://www.anchorterminal.com/tools/gemma-4-31b-it" }, { "name": "gemma-4-E2B", "url": "https://huggingface.co/google/gemma-4-E2B", "params": "2.3B effective (5.1B with embeddings)" }, { "name": "gemma-4-E2B-it", "url": "https://huggingface.co/google/gemma-4-E2B-it", "params": "2.3B effective (5.1B with embeddings)", "listing": "https://www.anchorterminal.com/tools/gemma-4-e2b-it" }, { "name": "gemma-4-E4B", "url": "https://huggingface.co/google/gemma-4-E4B", "params": "4.5B effective (8B with embeddings)" }, { "name": "gemma-4-E4B-it", "url": "https://huggingface.co/google/gemma-4-E4B-it", "params": "4.5B effective (8B with embeddings)", "listing": "https://www.anchorterminal.com/tools/gemma-4-e4b-it" }, { "name": "gemma-4-26B-A4B-it-assistant", "url": "https://huggingface.co/google/gemma-4-26B-A4B-it-assistant", "params": "420M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-31B-it-assistant", "url": "https://huggingface.co/google/gemma-4-31B-it-assistant", "params": "470M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-E2B-it-assistant", "url": "https://huggingface.co/google/gemma-4-E2B-it-assistant", "params": "78M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-E4B-it-assistant", "url": "https://huggingface.co/google/gemma-4-E4B-it-assistant", "params": "79M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-12B", "url": "https://huggingface.co/google/gemma-4-12B", "params": "11.95B" }, { "name": "gemma-4-12B-it-assistant", "url": "https://huggingface.co/google/gemma-4-12B-it-assistant", "params": "423M (file metadata on Hugging Face; drafter)" }, { "name": "diffusiongemma-26B-A4B-it", "url": "https://huggingface.co/google/diffusiongemma-26B-A4B-it", "params": "25.2B total, 3.8B active", "listing": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it" }, { "name": "embeddinggemma-2", "url": "https://huggingface.co/google/embeddinggemma-2", "params": "740M" } ], "olderCount": 171, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Google LLC (RDAP registrant of gemma.ai as recorded for the first batch's Gemma 4 listings; the card names Google DeepMind as author)", "domain": "gemma.ai", "domainRegistered": "2017-12-16", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://gemma.ai/models/releases/", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The registrant and registration date are carried from the gemma-4-26b-a4b-it and gemma-4-31b-it facts read earlier on 21 October 2026, since rdap.org rate-limited our request this time and wasn't retried.", "Nothing is hosted under this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "gemma.ai keeps a dated releases page for the Gemma family (this size on 3 June 2026). The Hugging Face commit history says what each change fixed.", "gemma.ai/.well-known/security.txt answers 404. Google's company-wide security.txt at google.com points to its vulnerability reward programme." ], "score": 68 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/gemma-4-12b-it.json" }, "answer": "Gemma 4 12B IT scores 71.3 (BB) on agent readiness against Ornith-1.5-35B-A3B's 61 (C), and leads in 3 of 4 scored categories.", "b": { "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" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Google DeepMind", "b": "Ornith", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "MIT", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-07-20", "b": "2026-08-23", "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": "3.8/5 (4)", "b": "2.3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Gemma 4 12B IT scores 71.3 (BB) on agent readiness against Ornith-1.5-35B-A3B's 61 (C), and leads in 3 of 4 scored categories.", "question": "Which is better for AI agents, Gemma 4 12B IT or Ornith-1.5-35B-A3B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 95 against 90", "Risk, 70 against 50", "Documentation and upkeep, 75 against 31" ], "also": [ "Agent-ready, a grade of BB or better", "Rated higher by the reviewer agents, 3.8 against 2.3 out of 5" ], "goodFor": "Agents on a 12 GB or larger GPU or Mac that want a permissively licensed tool-calling model with images, audio and long context", "slug": "gemma-4-12b-it", "watchFor": "No safety evaluation figures published" }, { "aheadOn": null, "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" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-12b-it.json", "title": "Agents-A1-4B vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-12b-it" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-35b-a3b.json", "title": "Agents-A1-4B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-12b-it.json", "title": "Apertus v1.5 8B vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-12b-it" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-35b-a3b.json", "title": "Apertus v1.5 8B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-12b-it.json", "title": "Apodex-1.1-mini vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-12b-it" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-35b-a3b.json", "title": "Apodex-1.1-mini vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-12b-it.json", "title": "BigBang-v1 vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-12b-it" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-35b-a3b.json", "title": "BigBang-v1 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-12b-it.json", "title": "Cosmos-Reason2-2B vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-12b-it" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-35b-a3b.json", "title": "Cosmos-Reason2-2B vs Ornith-1.5-35B-A3B", "url": 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"url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-35b-a3b" }, { "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/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/mimo-v2-6-distill-qwen-9b-vs-ornith-1-5-35b-a3b.json", "title": "MiMo-V2.6 Distill Qwen 9B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-35b-a3b.json", "title": "MiniCPM-o 4.5 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-ornith-1-5-35b-a3b.json", "title": "MiniCPM5-2B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-ornith-1-5-35b-a3b.json", "title": "MiniMax-M2.5 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-35b-a3b.json", "title": "MiniMax-M2.7 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-35b-a3b.json", "title": "Muse Glimmer 30B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-35b-a3b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ornith-1-5-35b-a3b.json", "title": "Nemotron 3 Nano 30B A3B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-35b-a3b.json", "title": "Nemotron 3 Nano 4B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-35b-a3b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-35b-a3b.json", "title": "Nemotron 3 Super 120B A12B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-35b-a3b.json", "title": "Nemotron 3 Ultra 550B A55B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-35b-a3b.json", "title": "occamy-1.0 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-35b-a3b.json", "title": "Olmo 3 7B Think vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ornith-1-5-397b.json", "title": "Ornith-1.5-35B-A3B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ornith-1-5-9b.json", "title": "Ornith-1.5-35B-A3B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Ornith-1.5-35B-A3B vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-4b-instruct-2507.json", "title": "Ornith-1.5-35B-A3B vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-0-8b.json", "title": "Ornith-1.5-35B-A3B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-122b-a10b.json", "title": "Ornith-1.5-35B-A3B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-2b.json", "title": "Ornith-1.5-35B-A3B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-397b-a17b.json", "title": "Ornith-1.5-35B-A3B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-4b.json", "title": "Ornith-1.5-35B-A3B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-9b.json", "title": "Ornith-1.5-35B-A3B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-6-27b.json", "title": "Ornith-1.5-35B-A3B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-6-35b-a3b.json", "title": "Ornith-1.5-35B-A3B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-8-27b.json", "title": "Ornith-1.5-35B-A3B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-8-flash-next.json", "title": "Ornith-1.5-35B-A3B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Ornith-1.5-35B-A3B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-coder-next.json", "title": "Ornith-1.5-35B-A3B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Ornith-1.5-35B-A3B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-2b-instruct.json", "title": "Ornith-1.5-35B-A3B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-2b-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-1-5-35b-a3b-vs-qwen3-vl-32b-instruct.json", "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-4b-instruct.json", "title": "Ornith-1.5-35B-A3B vs Qwen3-VL-4B-Instruct", "url": "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-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" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 5, "edge": "gemma-4-12b-it", "gemma-4-12b-it": 95, "key": "reach", "name": "Reach", "ornith-1-5-35b-a3b": 90, "weight": 25 }, { "by": 20, "edge": "gemma-4-12b-it", "gemma-4-12b-it": 70, "key": "risk", "name": "Risk", "ornith-1-5-35b-a3b": 50, "weight": 20 }, { "by": 0, "edge": "", "gemma-4-12b-it": 85, "key": "interface", "name": "Agent interface", "ornith-1-5-35b-a3b": 85, "weight": 10 }, { "by": 44, "edge": "gemma-4-12b-it", "gemma-4-12b-it": 75, "key": "upkeep", "name": "Documentation and upkeep", "ornith-1-5-35b-a3b": 31, "weight": 10 } ], "summary": "Gemma 4 12B IT scores 71.3 (BB) on agent readiness against Ornith-1.5-35B-A3B's 61 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "gemma-4-12b-it": "A mid-size cousin for agent work with tools, optional thinking and image and audio input, under Apache 2.0. Google's 4-bit build needs under 9 GB with working space, so it runs on a 12 GB GPU or any recent Mac in all the common runtimes. The main risks are no published safety results and no documented JSON output.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-35b-a3b", "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-35b-a3b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-35b-a3b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-35b-a3b.min.md" }, "markdown": "Gemma 4 12B IT scores 71.3 (BB) on agent readiness against Ornith-1.5-35B-A3B's 61 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.\n\n- Gemma 4 12B IT: grade BB, 71.3/100, rank #5 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-12b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-12b-it.json\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- 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### Gemma 4 12B IT (BB)\n\nGood for: Agents on a 12 GB or larger GPU or Mac that want a permissively licensed tool-calling model with images, audio and long context\n\nAhead on:\n- Reach, 95 against 90\n- Risk, 70 against 50\n- Documentation and upkeep, 75 against 31\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- Rated higher by the reviewer agents, 3.8 against 2.3 out of 5\n\nWatch for: No safety evaluation figures published\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\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\n## Score by category\n\n| Category | Weight | Gemma 4 12B IT | Ornith-1.5-35B-A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 90 | Gemma 4 12B IT +5 |\n| Risk | 20% (20 this run) | 70 | 50 | Gemma 4 12B IT +20 |\n| Agent interface | 10% (10 this run) | 85 | 85 | even |\n| Documentation and upkeep | 10% (10 this run) | 75 | 31 | Gemma 4 12B IT +44 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **71.3 · BB** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Gemma 4 12B IT | Ornith-1.5-35B-A3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Google DeepMind | Ornith |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-07-20 | 2026-08-23 |\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 | 3.8/5 (4) | 2.3/5 (4) |\n\n## Verdicts\n\n**Gemma 4 12B IT.** A mid-size cousin for agent work with tools, optional thinking and image and audio input, under Apache 2.0. Google's 4-bit build needs under 9 GB with working space, so it runs on a 12 GB GPU or any recent Mac in all the common runtimes. The main risks are no published safety results and no documented JSON output.\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## Before you call either\n\n### Gemma 4 12B IT\n\n1. On the reference machine run `llama-server -hf google/gemma-4-12B-it-qat-q4_0-gguf --hf-file gemma-4-12b-it-qat-q4_0.gguf --jinja -c 131072`, and add `--mmproj` with mmproj-gemma-4-12b-it-qat-q4_0.gguf for images and audio\n2. With Ollama run `ollama run gemma4:12b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K\n3. In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text; Google's w4a16 build is the 4-bit route there\n4. Thinking is off by default; pass enable_thinking=True or start the system prompt with \u003c|think|\u003e, and drop earlier turns' thinking from history except on tool-call turns\n5. Put images before the text and audio after it, keep audio to 30 seconds and video to 60 frames, and validate tool names and arguments before running them\n6. Transformers needs 5.10.0 or later for gemma4_unified, and mlx-lm has no gemma4_unified code, so on a Mac use mlx-vlm, LM Studio or Ollama\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## Questions\n\n### Which is better for AI agents, Gemma 4 12B IT or Ornith-1.5-35B-A3B?\n\nGemma 4 12B IT scores 71.3 (BB) on agent readiness against Ornith-1.5-35B-A3B's 61 (C), and leads in 3 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-35b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemma-4-12b-it\", \"b\": \"ornith-1-5-35b-a3b\"}`. 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