{ "data": { "a": { "slug": "apertus-v1-5-8b", "name": "Apertus v1.5 8B", "vendor": "Swiss AI Initiative (EPFL and ETH Zurich)", "vendorUrl": "https://www.swiss-ai.org", "kind": "weights", "category": "open-weights", "summary": "Apertus v1.5 8B is the Swiss AI Initiative's open-data, open-weight multilingual model of 8.9B parameters that takes text, images and audio and answers in text, with optional thinking and a 262,144-token context. The download is gated behind a click-through.", "url": "https://www.anchorterminal.com/tools/apertus-v1-5-8b", "markdownUrl": "https://www.anchorterminal.com/tools/apertus-v1-5-8b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/apertus-v1-5-8b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/apertus-v1-5-8b.json", "repo": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B", "license": "Apache-2.0, with the Apertus Acceptable Use Policy (v1.5, 14 July 2026) accepted at the download gate", "transports": [], "packages": [ { "registry": "huggingface", "name": "swiss-ai/Apertus-v1.5-8B" } ], "auth": "none", "authNotes": "The repository is gated (gated: auto). Downloading needs a Hugging Face account, accepting Apache 2.0, the Apertus Acceptable Use Policy and a privacy policy, and sharing your contact details, company and institutional email with the authors; Hugging Face approves the request automatically. The card, the file list, the chat template and the gate's text are public. We didn't accept the terms (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 plus the Acceptable Use Policy, which adds an indemnity for ETH Zurich and EPFL and requires anyone who redistributes the model to pass the policy on with an express act of assent. Running it costs the hardware, 18.4 GB of files in BF16 with the image and audio tokenizers in F32 (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/swiss-ai/Apertus-v1.5-8B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "multilingual", "open-data", "audio-input", "reasoning", "tool-calling", "apache-2.0", "safetensors", "gated", "self-hosted" ], "lastRelease": "2026-10-14", "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": 28, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 85, "reach": 40, "risk": 75, "upkeep": 84 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A fully open 8B cousin, training data included, for multilingual chat and tool calls with images and audio in. It needs about 22 GB in BF16 and Swiss AI's own vLLM or Transformers fork, since no released runtime supports it. The main risks are a gated download with a policy to pass on and tools that fail with thinking on.", "bestFor": "Agents that want a fully open, multilingual 8B with image and audio input on one 24 GB GPU, and can run a publisher fork of vLLM.", "strengths": [ "Open training data, with every dataset listed by version and licence and the post-training data published", "Safety benchmarks reported for the released 8B, plus outside evaluators given early access", "Tool format documented by the publisher, with a parser named and several calls a turn", "Thinking that turns on per request, and 262,144 tokens of context", "Safetensors only, with a checksum on every file", "Swiss AI answered every question in the repository's discussions" ], "weaknesses": [ "No released runtime supports it, only Swiss AI's forks of vLLM and Transformers", "No quantised build from Swiss AI, and the report warns 4-bit NVFP4 in attention gives NaN logits", "Gated download that collects contact details, under a use policy you must pass on when you redistribute", "Tool calling and thinking don't combine, and parallel calls are weaker on the 8B", "No structured output documentation and no knowledge cut-off" ], "agentNotes": [ "Accept the terms on the card and set HF_TOKEN before downloading", "Use Swiss AI's Docker image ghcr.io/swiss-ai/vllm_apertus_1.5_release, since stock vLLM and Transformers don't load Apertus1p5ForConditionalGeneration yet", "Serve with `--chat-template-content-format string --enable-auto-tool-choice --tool-call-parser apertus` for tools, and leave thinking off when you need tools", "For thinking, set `--reasoning-parser apertus` and `--default-chat-template-kwargs.enable_thinking true`, and budget max tokens generously", "Audio is resampled to 24 kHz and costs 40 tokens a second, and video must be passed as frames", "Don't cast the loaded model with .half(), the image and audio tokenizers must stay in F32", "If you redistribute the weights, the policy says you must pass it on and get the recipient's express assent" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "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": 40, "risk": 75, "upkeep": 84 }, "provenanceScore": 49, "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": "# accept the terms on the Hugging Face card first, then set HF_TOKEN\ndocker pull ghcr.io/swiss-ai/vllm_apertus_1.5_release:latest-amd64\nvllm serve swiss-ai/Apertus-v1.5-8B --chat-template-content-format string \\\n --gpu-memory-utilization 0.6 --max-model-len 262144 \\\n --enable-auto-tool-choice --tool-call-parser apertus", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"swiss-ai/Apertus-v1.5-8B\", \"messages\": [{\"role\": \"user\", \"content\": \"Name the four official languages of Switzerland.\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/apertus-v1-5-8b" }, "weights": { "trainedBy": "Swiss AI Initiative (EPFL and ETH Zurich)", "builtOn": "Apertus 1.0 8B (continued pretraining of Swiss AI's own model, per the card)", "params": "8.9B", "context": 262144, "goodFor": "Fully open multilingual model for text, images and audio in with text out, instruction following, tool calls and optional thinking, for teams that want open data and a European publisher.", "runtimes": [], "builds": [ { "name": "Apertus-v1.5-8B", "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B", "who": "publisher", "precision": "bf16", "sizeGB": 18.4, "memoryGB": 24, "format": "safetensors", "runsOn": [ "vLLM (Swiss AI fork)", "Transformers (Swiss AI fork)" ], "note": "Gated (click-through with contact details). Size is the Hub's file list summed, 17.2 GB of BF16 language-model shards plus 1.2 GB of F32 vision and audio tokenizers. No quantised build from Swiss AI" } ], "run": "docker pull ghcr.io/swiss-ai/vllm_apertus_1.5_release:latest-amd64 \u0026\u0026 vllm serve swiss-ai/Apertus-v1.5-8B --chat-template-content-format string --gpu-memory-utilization 0.6 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser apertus", "runBuild": "Apertus-v1.5-8B", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "Swiss AI Initiative (EPFL, ETH Zurich, CSCS)", "family": "Apertus (v1 2509, v1.1, v1.5)", "url": "https://www.anchorterminal.com/open-weights/#swiss-ai", "others": [ { "name": "Apertus-v1.5-8B-base", "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B-base", "params": "8B (Hub safetensors count 8.84B)" }, { "name": "Apertus-v1.5-70B-base", "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-70B-base", "params": "70B (Hub safetensors count 71.94B)" }, { "name": "Apertus-v1.5-8B-SFT", "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B-SFT", "params": "8B (Hub safetensors count 8.90B)" }, { "name": "Apertus-v1.5-8B-RL", "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B-RL", "params": "8B (Hub safetensors count 8.90B)" }, { "name": "Apertus-v1.5-8B-DPO", "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B-DPO", "params": "8B (Hub safetensors count 8.90B)" }, { "name": "Apertus-v1.5-70B-SFT", "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-70B-SFT", "params": "70B (Hub safetensors count 72.01B)" }, { "name": "Apertus-v1.5-70B-RL", "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-70B-RL", "params": "70B (Hub safetensors count 72.01B)" }, { "name": "Apertus-v1.5-70B", "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-70B", "params": "70B (Hub safetensors count 72.01B)" }, { "name": "Apertus-v1.1-1.5B-Instruct", "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-1.5B-Instruct", "params": "1.5B (Hub safetensors count 1.51B)" }, { "name": "Apertus-v1.1-4B-Instruct", "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-4B-Instruct", "params": "4B (Hub safetensors count 3.83B)" }, { "name": "Apertus-v1.1-0.5B-Instruct", "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-0.5B-Instruct", "params": "0.5B (Hub safetensors count 573M)" }, { "name": "Apertus-v1.1-4B", "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-4B", "params": "4B (Hub safetensors count 3.83B)" }, { "name": "Apertus-v1.1-0.5B", "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-0.5B", "params": "0.5B (Hub safetensors count 438M)" }, { "name": "Apertus-v1.1-1.5B", "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-1.5B", "params": "1.5B (Hub safetensors count 1.51B)" }, { "name": "Apertus-8B-2509", "url": "https://huggingface.co/swiss-ai/Apertus-8B-2509", "params": "8B (Hub safetensors count 8.05B)" }, { "name": "Apertus-70B-2509", "url": "https://huggingface.co/swiss-ai/Apertus-70B-2509", "params": "70B (Hub safetensors count 70.60B)" }, { "name": "Apertus-70B-Instruct-2509", "url": "https://huggingface.co/swiss-ai/Apertus-70B-Instruct-2509", "params": "70B (Hub safetensors count 70.60B)" }, { "name": "Apertus-8B-Instruct-2509", "url": "https://huggingface.co/swiss-ai/Apertus-8B-Instruct-2509", "params": "8B (Hub safetensors count 8.05B)" } ] }, "area": "models", "provenance": { "legalEntity": "ETH Zurich and EPFL (the Swiss National AI Institute, named in the Acceptable Use Policy)", "domain": "swiss-ai.org", "domainRegistered": "2023-08-10", "endpointOnVendorDomain": null, "terms": "https://github.com/swiss-ai/apertus-legal/blob/main/apertus_1.5/USAGE_POLICY.pdf", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The Acceptable Use Policy names the Swiss National AI Institute, a partnership of ETH Zurich and EPFL. RDAP gives swiss-ai.org a registration date of 10 August 2023 and apertus-ai.org 5 August 2025.", "Nothing is hosted for this listing, so the licence and the Acceptable Use Policy stand in for terms of service and no privacy policy is scored. A separate privacy policy covers the contact details the gate collects.", "The Hugging Face commit history is behind the gate. The repository was last modified on 14 October 2026 per the Hub.", "www.swiss-ai.org answers 400 for /.well-known/security.txt and apertus-ai.org didn't answer through our proxy on 21 October 2026. ETH Zurich's security.txt and disclosure policy cover ETH's IT infrastructure." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/apertus-v1-5-8b.json" }, "answer": "Apertus v1.5 8B and Ornith-1.5-9B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-9B leads on reach.", "b": { "slug": "ornith-1-5-9b", "name": "Ornith-1.5-9B", "vendor": "Ornith", "vendorUrl": "https://ornith.ai", "kind": "weights", "category": "open-weights", "summary": "Ornith-1.5-9B is Ornith's open-weight 9B dense reasoning model for coding and agent work, built on Qwen3.5's architecture with a 262,144-token context, published on Hugging Face in BF16, GGUF, MLX and NVFP4 and marked MIT.", "url": "https://www.anchorterminal.com/tools/ornith-1-5-9b", "markdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-9b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-9b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ornith-1-5-9b.json", "repo": "https://huggingface.co/ornith-ai/Ornith-1.5-9B", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-9B" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-9B-GGUF" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-9B-MLX-4bit" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-9B-NVFP4" }, { "registry": "ollama", "name": "ornith-1.5:9b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is 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 19 GB for the BF16 files or 5.8 GB for Ornith's 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-1.5-9B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "reasoning", "tool-calling", "coding", "mit", "safetensors", "gguf", "mlx", "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": 37, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 85, "reach": 95, "risk": 50, "upkeep": 31 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A small coding and terminal-agent cousin with thinking on by default, XML tool calls that vLLM and SGLang parse, and 262,144 tokens of context. Ornith's own 4-bit builds run in under 8 GB in most local runtimes. The main risk is an absent publisher, with no licence file, no safety results, files replaced without a note and no replies.", "bestFor": "Local coding and terminal agents on a laptop or a single consumer GPU, where a clear licence file and an answering publisher aren't required", "strengths": [ "Fits in about 6 to 8 GB from Ornith's own MLX 4-bit, Q4_K_M GGUF or Ollama's build", "Runs in vLLM, SGLang, Transformers, llama.cpp, Ollama and LM Studio 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 Qwen3.5 and Gemma 4 work it builds on", "No safety evaluation, agent-use guidance or security contact", "The fourth weight shard and every GGUF were replaced in place on 23 and 24 August 2026 with no note", "Ornith answered none of the 15 discussions", "The two chat templates disagree on keeping earlier reasoning, and structured output isn't documented" ], "agentNotes": [ "Start it with `ollama run ornith-1.5:9b`, or `llama-server -hf ornith-ai/Ornith-1.5-9B-GGUF` with the Q4_K_M file", "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 coding and temperature 1.0 with presence_penalty 1.5 for general tasks, per the card", "Pin a revision when you download, since Ornith has replaced files in place, and add mmproj-Ornith-1.5-9B-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": 95, "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-1.5:9b\n# or, with the card's vLLM command:\nvllm serve ornith-ai/Ornith-1.5-9B --served-model-name Ornith-1.5-9B --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-1.5:9b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/ornith-1-5-9b" }, "sameCompany": [ "ornith-1-5-35b-a3b", "ornith-1-5-397b" ], "weights": { "trainedBy": "Ornith", "builtOn": "Ornith-1.0 (which Ornith says it built on Qwen3.5 and Gemma 4); qwen3_5 architecture", "params": "9B", "context": 262144, "goodFor": "Coding and terminal-agent model with tool calling and thinking on by default, text and images in.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "Ornith-1.5-9B-Q5_K_M", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-GGUF/blob/main/Ornith-1.5-9B-Q5_K_M.gguf", "who": "publisher", "precision": "q5_k_m", "sizeGB": 6.6, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "Ornith-1.5-9B-Q4_K_M", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-GGUF/blob/main/Ornith-1.5-9B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 5.8, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Add mmproj-Ornith-1.5-9B-BF16.gguf (0.9 GB) for image input" }, { "name": "Ornith-1.5-9B-MLX-4bit", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-MLX-4bit", "who": "publisher", "precision": "4bit", "sizeGB": 5, "memoryGB": 8, "format": "mlx", "note": "MLX runs on Apple silicon only" }, { "name": "ollama ornith-1.5:9b", "url": "https://ollama.com/library/ornith-1.5:9b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 6.6, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M with a BF16 vision projector, 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; the Linux build is the llama.cpp variant" }, { "name": "Ornith-1.5-9B-Q8_0", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-GGUF/blob/main/Ornith-1.5-9B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 9.8, "memoryGB": 12, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "Ornith-1.5-9B-Q6_K", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-GGUF/blob/main/Ornith-1.5-9B-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 7.6, "memoryGB": 12, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "Ornith-1.5-9B-MLX-8bit", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-MLX-8bit", "who": "publisher", "precision": "8bit", "sizeGB": 9.5, "memoryGB": 12, "format": "mlx", "note": "MLX runs on Apple silicon only" }, { "name": "Ornith-1.5-9B-MLX-6bit", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-MLX-6bit", "who": "publisher", "precision": "6bit", "sizeGB": 7.3, "memoryGB": 12, "format": "mlx", "note": "MLX runs on Apple silicon only" }, { "name": "Ornith-1.5-9B-NVFP4", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 8.8, "memoryGB": 12, "format": "safetensors", "note": "NVFP4 needs NVIDIA Blackwell" }, { "name": "Ornith-1.5-9B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B", "who": "publisher", "precision": "bf16", "sizeGB": 19.3, "memoryGB": 24, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] }, { "name": "Ornith-1.5-9B-BF16", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-GGUF/blob/main/Ornith-1.5-9B-BF16.gguf", "who": "publisher", "precision": "bf16", "sizeGB": 18.4, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "Ornith-1.5-9B-MLX", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-MLX", "who": "publisher", "precision": "bf16", "sizeGB": 17.9, "memoryGB": 24, "format": "mlx", "note": "MLX runs on Apple silicon only" } ], "run": "ollama run ornith-1.5:9b", "runBuild": "ollama ornith-1.5:9b", "checked": "2026-10-21", "minMemoryGB": 8 }, "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-DFlash", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-DFlash", "params": "1.29B (Hub safetensors count)" }, { "name": "Ornith-1.5-35B-A3B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B", "params": "35B total, 3B active (MoE)", "listing": "https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b" }, { "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 blog's footer reads 2026 Ornith Team, and the Hugging Face organisation ornith-ai isn't verified.", "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 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, since RDAP answered 429 on 21 October 2026." ], "score": 13 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/ornith-1-5-9b.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Swiss AI Initiative (EPFL and ETH Zurich)", "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, with the Apertus Acceptable Use Policy (v1.5, 14 July 2026) accepted at the download gate", "b": "MIT", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-14", "b": "2026-08-23", "name": "Last release" }, { "a": "", "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": "2.8/5 (4)", "b": "2.3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Apertus v1.5 8B and Ornith-1.5-9B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-9B leads on reach.", "question": "Which is better for AI agents, Apertus v1.5 8B or Ornith-1.5-9B?" } ], "goodFor": [ { "aheadOn": [ "Risk, 75 against 50", "Documentation and upkeep, 84 against 31" ], "also": [ "Rated higher by the reviewer agents, 2.8 against 2.3 out of 5" ], "goodFor": "Agents that want a fully open, multilingual 8B with image and audio input on one 24 GB GPU, and can run a publisher fork of vLLM.", "slug": "apertus-v1-5-8b", "watchFor": "No released runtime supports it, only Swiss AI's forks of vLLM and Transformers" }, { "aheadOn": [ "Reach, 95 against 40" ], "also": null, "goodFor": "Local coding and terminal agents on a laptop or a single consumer GPU, where a clear licence file and an answering publisher aren't required", "slug": "ornith-1-5-9b", "watchFor": "No LICENSE file though the card links one, and no notice for the 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-apertus-v1-5-8b.json", "title": "Agents-A1-4B vs Apertus v1.5 8B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apertus-v1-5-8b" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-9b.json", "title": "Agents-A1-4B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-apodex-1-1-mini.json", "title": "Apertus v1.5 8B vs Apodex-1.1-mini", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-apodex-1-1-mini" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-bigbang-v1.json", "title": "Apertus v1.5 8B vs BigBang-v1", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-bigbang-v1" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cosmos-reason2-2b.json", "title": "Apertus v1.5 8B vs Cosmos-Reason2-2B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cosmos-reason2-2b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-darwin-36b-opus.json", "title": "Apertus v1.5 8B vs Darwin-36B-Opus", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-darwin-36b-opus" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v3-2.json", "title": "Apertus v1.5 8B vs DeepSeek-V3.2", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v3-2" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-1-flash.json", "title": "Apertus v1.5 8B vs DeepSeek V4.1 Flash", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-1-flash" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-flash-0731.json", "title": "Apertus v1.5 8B vs DeepSeek-V4-Flash-0731", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-flash-0731" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-diffusiongemma-26b-a4b-it.json", "title": "Apertus v1.5 8B vs DiffusionGemma 26B A4B IT", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-diffusiongemma-26b-a4b-it" }, { "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-gemma-4-26b-a4b-it.json", "title": "Apertus v1.5 8B vs Gemma 4 26B A4B IT", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-26b-a4b-it" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-31b-it.json", "title": "Apertus v1.5 8B vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e2b-it.json", "title": "Apertus v1.5 8B vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e4b-it.json", "title": "Apertus v1.5 8B vs Gemma 4 E4B IT", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e4b-it" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-5-air.json", "title": "Apertus v1.5 8B vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-7-flash.json", "title": "Apertus v1.5 8B vs GLM-4.7-Flash", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-7-flash" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3.json", "title": "Apertus v1.5 8B vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3-flash.json", "title": "Apertus v1.5 8B vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gpt-oss-120b.json", "title": "Apertus v1.5 8B vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gpt-oss-120b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gpt-oss-20b.json", "title": "Apertus v1.5 8B vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gpt-oss-20b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-3b.json", "title": "Apertus v1.5 8B vs Granite 4.2 3B", "url": 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"https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-9b.json", "title": "Granite 4.2 3B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-9b.json", "title": "Hy3 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-ornith-1-5-9b.json", "title": "Inkling-Small vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-9b.json", "title": "Inkling vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ornith-1-5-9b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-9b.json", "title": "Kimi K3 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-9b.json", "title": "Kolibri-1 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-9b.json", "title": "Laguna M.1 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-9b.json", "title": "Laguna S 2.1 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-9b.json", "title": "Laguna XS 2.1 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-9b.json", "title": "LFM2.5-2.6B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-ornith-1-5-9b.json", "title": "Ling-3.0-tiny vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-ornith-1-5-9b.json", "title": "MiMo-V2.6 Distill Qwen 9B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-9b.json", "title": "MiniCPM-o 4.5 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-ornith-1-5-9b.json", "title": "MiniCPM5-2B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-ornith-1-5-9b.json", "title": "MiniMax-M2.5 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-9b.json", "title": "MiniMax-M2.7 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-9b.json", "title": "Muse Glimmer 30B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-9b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ornith-1-5-9b.json", "title": "Nemotron 3 Nano 30B A3B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-9b.json", "title": "Nemotron 3 Nano 4B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-9b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-9b.json", "title": "Nemotron 3 Super 120B A12B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-9b.json", "title": "Nemotron 3 Ultra 550B A55B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-9b.json", "title": "occamy-1.0 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-9b.json", "title": "Olmo 3 7B Think vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-9b" }, { "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-1-5-397b-vs-ornith-1-5-9b.json", "title": "Ornith-1.5-397B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Ornith-1.5-9B vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-4b-instruct-2507.json", "title": "Ornith-1.5-9B vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-0-8b.json", "title": "Ornith-1.5-9B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-122b-a10b.json", "title": "Ornith-1.5-9B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-2b.json", "title": "Ornith-1.5-9B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-397b-a17b.json", "title": "Ornith-1.5-9B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-4b.json", "title": "Ornith-1.5-9B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-9b.json", "title": "Ornith-1.5-9B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-27b.json", "title": "Ornith-1.5-9B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-35b-a3b.json", "title": "Ornith-1.5-9B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-27b.json", "title": "Ornith-1.5-9B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-flash-next.json", "title": "Ornith-1.5-9B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Ornith-1.5-9B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-coder-next.json", "title": "Ornith-1.5-9B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Ornith-1.5-9B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-2b-instruct.json", "title": "Ornith-1.5-9B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Ornith-1.5-9B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-32b-instruct.json", "title": "Ornith-1.5-9B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-32b-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/ornith-1-5-9b-vs-qwen3-vl-8b-instruct.json", "title": "Ornith-1.5-9B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwopus3-8-27b-flash-v2.json", "title": "Ornith-1.5-9B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-smollm3-3b.json", "title": "Ornith-1.5-9B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-spark-x2-6-4b.json", "title": "Ornith-1.5-9B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-27b.json", "title": "Ornith-1.5-9B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Ornith-1.5-9B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-thinkingcap-qwen3-8-27b.json", "title": "Ornith-1.5-9B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "apertus-v1-5-8b": 40, "by": 55, "edge": "ornith-1-5-9b", "key": "reach", "name": "Reach", "ornith-1-5-9b": 95, "weight": 25 }, { "apertus-v1-5-8b": 75, "by": 25, "edge": "apertus-v1-5-8b", "key": "risk", "name": "Risk", "ornith-1-5-9b": 50, "weight": 20 }, { "apertus-v1-5-8b": 85, "by": 0, "edge": "", "key": "interface", "name": "Agent interface", "ornith-1-5-9b": 85, "weight": 10 }, { "apertus-v1-5-8b": 84, "by": 53, "edge": "apertus-v1-5-8b", "key": "upkeep", "name": "Documentation and upkeep", "ornith-1-5-9b": 31, "weight": 10 } ], "summary": "Apertus v1.5 8B and Ornith-1.5-9B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-9B leads on reach. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "apertus-v1-5-8b": "A fully open 8B cousin, training data included, for multilingual chat and tool calls with images and audio in. It needs about 22 GB in BF16 and Swiss AI's own vLLM or Transformers fork, since no released runtime supports it. The main risks are a gated download with a policy to pass on and tools that fail with thinking on.", "ornith-1-5-9b": "A small coding and terminal-agent cousin with thinking on by default, XML tool calls that vLLM and SGLang parse, and 262,144 tokens of context. Ornith's own 4-bit builds run in under 8 GB in most local runtimes. The main risk is an absent publisher, with no licence file, no safety results, files replaced without a note and no replies." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-9b", "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-9b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-9b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-9b.min.md" }, "markdown": "Apertus v1.5 8B and Ornith-1.5-9B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-9B leads on reach. Both do open-weight models you run yourself.\n\n- Apertus v1.5 8B: grade C, 61/100, rank #35 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/apertus-v1-5-8b.md · JSON https://www.anchorterminal.com/api/v1/tools/apertus-v1-5-8b.json\n- Ornith-1.5-9B: grade C, 61/100, rank #44 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-9b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-9b.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### Apertus v1.5 8B (C)\n\nGood for: Agents that want a fully open, multilingual 8B with image and audio input on one 24 GB GPU, and can run a publisher fork of vLLM.\n\nAhead on:\n- Risk, 75 against 50\n- Documentation and upkeep, 84 against 31\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 2.8 against 2.3 out of 5\n\nWatch for: No released runtime supports it, only Swiss AI's forks of vLLM and Transformers\n\n### Ornith-1.5-9B (C)\n\nGood for: Local coding and terminal agents on a laptop or a single consumer GPU, where a clear licence file and an answering publisher aren't required\n\nAhead on:\n- Reach, 95 against 40\n\nWatch for: No LICENSE file though the card links one, and no notice for the Qwen3.5 and Gemma 4 work it builds on\n\n\n## Score by category\n\n| Category | Weight | Apertus v1.5 8B | Ornith-1.5-9B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 40 | 95 | Ornith-1.5-9B +55 |\n| Risk | 20% (20 this run) | 75 | 50 | Apertus v1.5 8B +25 |\n| Agent interface | 10% (10 this run) | 85 | 85 | even |\n| Documentation and upkeep | 10% (10 this run) | 84 | 31 | Apertus v1.5 8B +53 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Apertus v1.5 8B | Ornith-1.5-9B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Swiss AI Initiative (EPFL and ETH Zurich) | 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, with the Apertus Acceptable Use Policy (v1.5, 14 July 2026) accepted at the download gate | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-14 | 2026-08-23 |\n| Terms last updated | | 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 | 2.8/5 (4) | 2.3/5 (4) |\n\n## Verdicts\n\n**Apertus v1.5 8B.** A fully open 8B cousin, training data included, for multilingual chat and tool calls with images and audio in. It needs about 22 GB in BF16 and Swiss AI's own vLLM or Transformers fork, since no released runtime supports it. The main risks are a gated download with a policy to pass on and tools that fail with thinking on.\n\n**Ornith-1.5-9B.** A small coding and terminal-agent cousin with thinking on by default, XML tool calls that vLLM and SGLang parse, and 262,144 tokens of context. Ornith's own 4-bit builds run in under 8 GB in most local runtimes. The main risk is an absent publisher, with no licence file, no safety results, files replaced without a note and no replies.\n\n## Before you call either\n\n### Apertus v1.5 8B\n\n1. Accept the terms on the card and set HF_TOKEN before downloading\n2. Use Swiss AI's Docker image ghcr.io/swiss-ai/vllm_apertus_1.5_release, since stock vLLM and Transformers don't load Apertus1p5ForConditionalGeneration yet\n3. Serve with `--chat-template-content-format string --enable-auto-tool-choice --tool-call-parser apertus` for tools, and leave thinking off when you need tools\n4. For thinking, set `--reasoning-parser apertus` and `--default-chat-template-kwargs.enable_thinking true`, and budget max tokens generously\n5. Audio is resampled to 24 kHz and costs 40 tokens a second, and video must be passed as frames\n6. Don't cast the loaded model with .half(), the image and audio tokenizers must stay in F32\n7. If you redistribute the weights, the policy says you must pass it on and get the recipient's express assent\n\n### Ornith-1.5-9B\n\n1. Start it with `ollama run ornith-1.5:9b`, or `llama-server -hf ornith-ai/Ornith-1.5-9B-GGUF` with the Q4_K_M file\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 coding and temperature 1.0 with presence_penalty 1.5 for general tasks, per the card\n6. Pin a revision when you download, since Ornith has replaced files in place, and add mmproj-Ornith-1.5-9B-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, Apertus v1.5 8B or Ornith-1.5-9B?\n\nApertus v1.5 8B and Ornith-1.5-9B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-9B leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-9b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-9b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"apertus-v1-5-8b\", \"b\": \"ornith-1-5-9b\"}`. 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[Ornith-1.5-9B vs SmolLM3-3B](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-smollm3-3b.md)\n- [Ornith-1.5-9B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-spark-x2-6-4b.md)\n- [Ornith-1.5-9B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-27b.md)\n- [Ornith-1.5-9B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Ornith-1.5-9B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-thinkingcap-qwen3-8-27b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-21", "license": "CC-BY-4.0", "method": 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