{ "data": { "a": { "slug": "laguna-xs-2-1", "name": "Laguna XS 2.1", "vendor": "Poolside", "vendorUrl": "https://poolside.ai", "kind": "weights", "category": "open-weights", "summary": "Laguna XS 2.1 is Poolside's open-weight Mixture-of-Experts coding model, 33B total and 3B active parameters, with switchable reasoning and a 262,144-token context, published on Hugging Face under OpenMDW-1.1.", "url": "https://www.anchorterminal.com/tools/laguna-xs-2-1", "markdownUrl": "https://www.anchorterminal.com/tools/laguna-xs-2-1.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/laguna-xs-2-1.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/laguna-xs-2-1.json", "repo": "https://huggingface.co/poolside/Laguna-XS-2.1", "license": "OpenMDW-1.1", "transports": [], "packages": [ { "registry": "huggingface", "name": "poolside/Laguna-XS-2.1" }, { "registry": "huggingface", "name": "poolside/Laguna-XS-2.1-FP8" }, { "registry": "huggingface", "name": "poolside/Laguna-XS-2.1-INT4" }, { "registry": "huggingface", "name": "poolside/Laguna-XS-2.1-NVFP4" }, { "registry": "huggingface", "name": "poolside/Laguna-XS-2.1-NVFP4-mlx" }, { "registry": "huggingface", "name": "poolside/Laguna-XS-2.1-GGUF" }, { "registry": "ollama", "name": "laguna-xs-2.1" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for the main repository and every build, although the card's metadata carries an unused gate description pointing to Poolside's privacy policy (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under OpenMDW-1.1. Running it yourself costs the hardware, 66.9 GB for the BF16 files and 20.3 GB for Poolside's Q4_K_M GGUF, which is also Ollama's default build. Poolside serves it on OpenRouter with a free and a paid endpoint, both marked to expire on 31 October 2026 (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/poolside/Laguna-XS-2.1", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "coding", "reasoning", "tool-calling", "openmdw-1.1", "safetensors", "gguf", "fp8", "nvfp4", "self-hosted" ], "lastRelease": "2026-08-19", "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": 33, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 85, "reach": 85, "risk": 55, "upkeep": 46 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A small coding and agent cousin with 3B active parameters, so it's quick on modest hardware. Poolside's 4-bit GGUF, also Ollama's default, needs about 24 GB, and every major runtime runs it. The main risks are that nobody at Poolside answers its discussions, there are no safety results, and the SGLang route runs repository code.", "bestFor": "Agents that want a fast local coding model on a 32 GB GPU or Mac, with tool calling and a permissive licence.", "strengths": [ "Released support in llama.cpp, Ollama, vLLM, SGLang, Transformers and mlx-lm", "Poolside's own Q4_K_M, FP8, INT4, NVFP4 and MLX builds, the Q4_K_M at 20.3 GB", "Permissive OpenMDW-1.1 licence with no gate, account or use policy", "Tool calling and the poolside_v1 parser documented on the card, several calls a turn", "Thinking on or off per request, with the reasoning returned in reasoning_content" ], "weaknesses": [ "No reply from Poolside to any discussion, including reports about the tokenizer and remote code", "No safety results and only general cautions for agent use", "The SGLang and TensorRT-LLM commands run the repository's Python with trust_remote_code", "The template defaults thinking off while generation_config.json turns it on", "No structured output documentation, and no limitations, languages or cut-off on the card" ], "agentNotes": [ "Quickest start is `ollama run laguna-xs-2.1`, 20 GB of files, the same Q4_K_M as Poolside's GGUF", "On our reference machine use `llama-server -hf poolside/Laguna-XS-2.1-GGUF:Q4_K_M --jinja -c 131072`, from a llama.cpp release with the laguna architecture (b11772 has it), or BF16 with vLLM (66.9 GB)", "With vLLM use `--tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on a build with vllm-project/vllm#47311, or `--tool-call-parser glm47` on older builds", "Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{\"enable_thinking\": true}'`, which Poolside recommends for agentic coding", "Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped", "Sampling per the card is temperature 1.0 and top_k 20", "The card warns that Ollama on Apple Silicon may return empty output until llama.cpp#25389 ships", "Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented" ], "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": 85, "risk": 55, "upkeep": 46 }, "provenanceScore": 71, "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 laguna-xs-2.1\n# or, with vLLM 0.21.0 or later (a build with vllm-project/vllm#47311):\nvllm serve poolside/Laguna-XS-2.1 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 \\\n --enable-auto-tool-choice --default-chat-template-kwargs '{\"enable_thinking\": true}'", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"laguna-xs-2.1\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/laguna-xs-2-1" }, "sameCompany": [ "laguna-m-1", "laguna-s-2-1" ], "weights": { "trainedBy": "Poolside", "params": "33.4B total, 3B active", "context": 262144, "goodFor": "Agentic coding and long-horizon terminal work on one GPU or a 32 GB machine, with tool calling and reasoning that turns on or off per request.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "Laguna-XS-2.1-INT4", "url": "https://huggingface.co/poolside/Laguna-XS-2.1-INT4", "who": "publisher", "precision": "int4 (w4a16)", "sizeGB": 23.9, "memoryGB": 32, "format": "safetensors" }, { "name": "Laguna-XS-2.1-NVFP4", "url": "https://huggingface.co/poolside/Laguna-XS-2.1-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 21.6, "memoryGB": 32, "format": "safetensors", "note": "For NVIDIA Blackwell GPUs" }, { "name": "Laguna-XS-2.1-NVFP4-mlx", "url": "https://huggingface.co/poolside/Laguna-XS-2.1-NVFP4-mlx", "who": "publisher", "precision": "nvfp4", "sizeGB": 21.6, "memoryGB": 32, "format": "mlx", "runsOn": [ "MLX" ] }, { "name": "Laguna-XS-2.1-GGUF Q4_K_M", "url": "https://huggingface.co/poolside/Laguna-XS-2.1-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 20.3, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp", "Ollama" ], "note": "Laguna-XS-2.1-Q4_K_M.gguf, 20,274,300,032 bytes. The repository also holds a BF16 GGUF of 66.9 GB (96 GB of memory)" }, { "name": "ollama laguna-xs-2.1:latest", "url": "https://ollama.com/library/laguna-xs-2.1:latest", "who": "ollama", "precision": "q4_k_m", "sizeGB": 20.3, "memoryGB": 32, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Ollama shows 20 GB. Its model blob (sha256 1ac7079101fc) is Poolside's Q4_K_M GGUF, so the exact size is Poolside's. The library also has q8_0 (36 GB), bf16 (67 GB) and MLX tags (nvfp4 shown as 20 GB, exact size unpublished, mxfp8 40 GB, mlx-bf16 68 GB). The card warns that ollama run may return empty output on Apple Silicon until llama.cpp#25389 ships" }, { "name": "Laguna-XS-2.1-FP8", "url": "https://huggingface.co/poolside/Laguna-XS-2.1-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 35.3, "memoryGB": 48, "format": "safetensors" }, { "name": "Laguna-XS-2.1", "url": "https://huggingface.co/poolside/Laguna-XS-2.1", "who": "publisher", "precision": "bf16", "sizeGB": 66.9, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ] } ], "run": "llama-server -hf poolside/Laguna-XS-2.1-GGUF:Q4_K_M --jinja -c 131072 --port 8000", "runBuild": "Laguna-XS-2.1-GGUF Q4_K_M", "checked": "2026-10-21", "minMemoryGB": 32 }, "family": { "lab": "Poolside", "family": "Laguna 2 (XS.2, XS 2.1, S 2.1)", "url": "https://www.anchorterminal.com/open-weights/#poolside", "others": [ { "name": "Laguna-S-2.1", "url": "https://huggingface.co/poolside/Laguna-S-2.1", "params": "117.56B (Hub safetensors count; the name gives only the size class S)", "listing": "https://www.anchorterminal.com/tools/laguna-s-2-1" }, { "name": "Laguna-S-2.1-base", "url": "https://huggingface.co/poolside/Laguna-S-2.1-base", "params": "not stated (no safetensors count on the Hub; the instruct model is 117.56B)" }, { "name": "Laguna-XS.2", "url": "https://huggingface.co/poolside/Laguna-XS.2", "params": "33.44B (Hub safetensors count; the name gives only the size class XS)" } ], "unrestrictedCopies": 1 }, "area": "models", "provenance": { "legalEntity": "Poolside, Inc. (named in Poolside's Terms of Use)", "domain": "poolside.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "https://poolside.ai/legal/eula", "privacy": "https://poolside.ai/legal/privacy", "statusPage": "", "changelog": "https://docs.poolside.ai/release-notes/models", "securityTxt": "valid", "checked": "2026-10-21", "notes": [ "The Hugging Face organisation poolside is verified, and Poolside's blog post for this release links its collection there.", "Nothing is hosted for this listing, so the OpenMDW-1.1 licence stands in for terms of service and no privacy policy is scored.", "Poolside's docs keep dated model release notes. The June 2026 entry calls XS 2.1 an incremental update to Laguna XS.2.", "security.txt is PGP-signed, names a security contact at poolside.ai and expires on 1 April 2028. The domain's registration date wasn't checked." ], "score": 71 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/laguna-xs-2-1.json" }, "answer": "Laguna XS 2.1 and Ornith-1.5-35B-A3B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-35B-A3B leads on reach.", "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": "Poolside", "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": "OpenMDW-1.1", "b": "MIT", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-08-19", "b": "2026-08-23", "name": "Last release" }, { "a": "", "b": "no document linked", "name": "Terms last updated" }, { "a": "", "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": "Laguna XS 2.1 and Ornith-1.5-35B-A3B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-35B-A3B leads on reach.", "question": "Which is better for AI agents, Laguna XS 2.1 or Ornith-1.5-35B-A3B?" } ], "goodFor": [ { "aheadOn": [ "Risk, 55 against 50", "Documentation and upkeep, 46 against 31" ], "also": [ "Rated higher by the reviewer agents, 2.8 against 2.3 out of 5" ], "goodFor": "Agents that want a fast local coding model on a 32 GB GPU or Mac, with tool calling and a permissive licence.", "slug": "laguna-xs-2-1", "watchFor": "No reply from Poolside to any discussion, including reports about the tokenizer and remote code" }, { "aheadOn": [ "Reach, 90 against 85" ], "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-laguna-xs-2-1.json", "title": "Agents-A1-4B vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-xs-2-1" }, { "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-laguna-xs-2-1.json", "title": "Apertus v1.5 8B vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-xs-2-1" }, { "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": 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2.1", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-laguna-xs-2-1" }, { "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": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-laguna-xs-2-1.json", "title": "Darwin-36B-Opus vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-35b-a3b.json", "title": "Darwin-36B-Opus vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-xs-2-1.json", "title": "DeepSeek-V3.2 vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-xs-2-1" }, { 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"https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-35b-a3b.json", "title": "Gemma 4 31B IT vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-xs-2-1.json", "title": "Gemma 4 E2B IT vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-35b-a3b.json", "title": "Gemma 4 E2B IT vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-laguna-xs-2-1.json", "title": "Gemma 4 E4B IT vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-35b-a3b.json", "title": "Gemma 4 E4B IT vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-xs-2-1.json", "title": "GLM-4.5-Air vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-35b-a3b.json", "title": "GLM-4.5-Air vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-laguna-xs-2-1.json", "title": "GLM-4.7-Flash vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-35b-a3b.json", "title": "GLM-4.7-Flash vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-35b-a3b" }, { "json": 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"title": "Laguna XS 2.1 vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-nano-4b.json", "title": "Laguna XS 2.1 vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Laguna XS 2.1 vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-super-120b-a12b.json", "title": "Laguna XS 2.1 vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-super-120b-a12b" }, { "json": 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"https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-32b-instruct.json", "title": "Laguna XS 2.1 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-4b-instruct.json", "title": "Laguna XS 2.1 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-8b-instruct.json", "title": "Laguna XS 2.1 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwopus3-8-27b-flash-v2.json", "title": "Laguna XS 2.1 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-smollm3-3b.json", "title": "Laguna XS 2.1 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-spark-x2-6-4b.json", "title": "Laguna XS 2.1 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-swift-1-5-qwen3-8-27b.json", "title": "Laguna XS 2.1 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Laguna XS 2.1 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-thinkingcap-qwen3-8-27b.json", "title": "Laguna XS 2.1 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-thinkingcap-qwen3-8-27b" }, { "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": "ornith-1-5-35b-a3b", "key": "reach", "laguna-xs-2-1": 85, "name": "Reach", "ornith-1-5-35b-a3b": 90, "weight": 25 }, { "by": 5, "edge": "laguna-xs-2-1", "key": "risk", "laguna-xs-2-1": 55, "name": "Risk", "ornith-1-5-35b-a3b": 50, "weight": 20 }, { "by": 0, "edge": "", "key": "interface", "laguna-xs-2-1": 85, "name": "Agent interface", "ornith-1-5-35b-a3b": 85, "weight": 10 }, { "by": 15, "edge": "laguna-xs-2-1", "key": "upkeep", "laguna-xs-2-1": 46, "name": "Documentation and upkeep", "ornith-1-5-35b-a3b": 31, "weight": 10 } ], "summary": "Laguna XS 2.1 and Ornith-1.5-35B-A3B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-35B-A3B leads on reach. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "laguna-xs-2-1": "A small coding and agent cousin with 3B active parameters, so it's quick on modest hardware. Poolside's 4-bit GGUF, also Ollama's default, needs about 24 GB, and every major runtime runs it. The main risks are that nobody at Poolside answers its discussions, there are no safety results, and the SGLang route runs repository code.", "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/laguna-xs-2-1-vs-ornith-1-5-35b-a3b", "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-35b-a3b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-35b-a3b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-35b-a3b.min.md" }, "markdown": "Laguna XS 2.1 and Ornith-1.5-35B-A3B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-35B-A3B leads on reach. Both do open-weight models you run yourself.\n\n- Laguna XS 2.1: grade C, 61/100, rank #40 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-xs-2-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-xs-2-1.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### Laguna XS 2.1 (C)\n\nGood for: Agents that want a fast local coding model on a 32 GB GPU or Mac, with tool calling and a permissive licence.\n\nAhead on:\n- Risk, 55 against 50\n- Documentation and upkeep, 46 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 reply from Poolside to any discussion, including reports about the tokenizer and remote code\n\n### Ornith-1.5-35B-A3B (C)\n\nGood for: Local coding and terminal agents on a 24 to 32 GB GPU or a Mac, where a clear licence file and an answering publisher aren't required\n\nAhead on:\n- Reach, 90 against 85\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 | Laguna XS 2.1 | Ornith-1.5-35B-A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 90 | Ornith-1.5-35B-A3B +5 |\n| Risk | 20% (20 this run) | 55 | 50 | Laguna XS 2.1 +5 |\n| Agent interface | 10% (10 this run) | 85 | 85 | even |\n| Documentation and upkeep | 10% (10 this run) | 46 | 31 | Laguna XS 2.1 +15 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Laguna XS 2.1 | Ornith-1.5-35B-A3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Poolside | 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 | OpenMDW-1.1 | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-19 | 2026-08-23 |\n| Terms last updated | | no document linked |\n| Privacy policy last updated | | 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**Laguna XS 2.1.** A small coding and agent cousin with 3B active parameters, so it's quick on modest hardware. Poolside's 4-bit GGUF, also Ollama's default, needs about 24 GB, and every major runtime runs it. The main risks are that nobody at Poolside answers its discussions, there are no safety results, and the SGLang route runs repository code.\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### Laguna XS 2.1\n\n1. Quickest start is `ollama run laguna-xs-2.1`, 20 GB of files, the same Q4_K_M as Poolside's GGUF\n2. On our reference machine use `llama-server -hf poolside/Laguna-XS-2.1-GGUF:Q4_K_M --jinja -c 131072`, from a llama.cpp release with the laguna architecture (b11772 has it), or BF16 with vLLM (66.9 GB)\n3. With vLLM use `--tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on a build with vllm-project/vllm#47311, or `--tool-call-parser glm47` on older builds\n4. Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{\"enable_thinking\": true}'`, which Poolside recommends for agentic coding\n5. Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped\n6. Sampling per the card is temperature 1.0 and top_k 20\n7. The card warns that Ollama on Apple Silicon may return empty output until llama.cpp#25389 ships\n8. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented\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, Laguna XS 2.1 or Ornith-1.5-35B-A3B?\n\nLaguna XS 2.1 and Ornith-1.5-35B-A3B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-35B-A3B leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-35b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"laguna-xs-2-1\", \"b\": \"ornith-1-5-35b-a3b\"}`. From a terminal: `anchor compare laguna-xs-2-1 ornith-1-5-35b-a3b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/laguna-xs-2-1.json and https://www.anchorterminal.com/api/v1/tools/ornith-1-5-35b-a3b.json\n\n## Other comparisons with Laguna XS 2.1 or Ornith-1.5-35B-A3B\n\n- [Agents-A1-4B vs Laguna XS 2.1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-xs-2-1.md)\n- [Agents-A1-4B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-35b-a3b.md)\n- [Apertus v1.5 8B vs Laguna XS 2.1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-xs-2-1.md)\n- [Apertus v1.5 8B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-35b-a3b.md)\n- [Apodex-1.1-mini vs Laguna XS 2.1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-xs-2-1.md)\n- [Apodex-1.1-mini vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-35b-a3b.md)\n- 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(https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-21", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Laguna XS 2.1 vs Ornith-1.5-35B-A3B", "url": "" } ], "description": "Laguna XS 2.1 and Ornith-1.5-35B-A3B score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. 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