{ "data": { "a": { "slug": "laguna-s-2-1", "name": "Laguna S 2.1", "vendor": "Poolside", "vendorUrl": "https://poolside.ai", "kind": "weights", "category": "open-weights", "summary": "Laguna S 2.1 is Poolside's open-weight Mixture-of-Experts model for agentic coding, 118B total and 8B active parameters, with switchable reasoning and a 1,048,576-token context, published on Hugging Face under OpenMDW-1.1.", "url": "https://www.anchorterminal.com/tools/laguna-s-2-1", "markdownUrl": "https://www.anchorterminal.com/tools/laguna-s-2-1.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/laguna-s-2-1.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/laguna-s-2-1.json", "repo": "https://huggingface.co/poolside/Laguna-S-2.1", "license": "OpenMDW-1.1", "transports": [], "packages": [ { "registry": "huggingface", "name": "poolside/Laguna-S-2.1" }, { "registry": "huggingface", "name": "poolside/Laguna-S-2.1-FP8" }, { "registry": "huggingface", "name": "poolside/Laguna-S-2.1-INT4" }, { "registry": "huggingface", "name": "poolside/Laguna-S-2.1-NVFP4" }, { "registry": "huggingface", "name": "poolside/Laguna-S-2.1-NVFP4-mlx" }, { "registry": "huggingface", "name": "poolside/Laguna-S-2.1-GGUF" }, { "registry": "ollama", "name": "laguna-s-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, 235.1 GB for the BF16 files, 96.0 GB for Poolside's Q4_K_M GGUF and 66 GB for Ollama's MLX NVFP4 build. Poolside's API, OpenRouter, Featherless AI, Vercel AI Gateway and Baseten host it (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-S-2.1", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "coding", "reasoning", "tool-calling", "long-context", "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": 32, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 85, "reach": 75, "risk": 55, "upkeep": 41 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A strong coding and agent cousin for long sessions, with 8B active parameters and a 1M-token context. At 4-bit it needs a 96 to 128 GB machine, tight on a 128 GB one. The main risks are that nobody at Poolside answers its many reports, there are no safety results, and a build card misdescribes its file.", "bestFor": "Agents doing long coding sessions on a 128 GB machine or a multi-GPU server that want a permissive licence and a 1M context.", "strengths": [ "Released support in llama.cpp, Ollama, vLLM, SGLang, Transformers and mlx-lm", "Poolside's own FP8, INT4, NVFP4, MLX and GGUF builds, plus DFlash draft models", "1,048,576 tokens of context, trained at that length per Poolside's build cards", "Permissive OpenMDW-1.1 licence with no gate, account or use policy", "Poolside's own API and Featherless AI host it" ], "weaknesses": [ "No reply from Poolside to any of 34 discussions, many of them reports about thinking and tool calls", "The GGUF card's Q4_K_M entry no longer matches the file, which was replaced in place", "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", "Known limitations in third-party harnesses and JSON arguments, and only off or max thinking" ], "agentNotes": [ "Quickest start is `ollama run laguna-s-2.1`, 96 GB for the default Q4_K_M, or the 66 GB nvfp4 tag on Apple Silicon", "On our reference machine try `llama-server -hf poolside/Laguna-S-2.1-GGUF:Q4_K_M --jinja -c 65536`, from a llama.cpp release with the laguna architecture (b11772 has it). The 96 GB file leaves little room for context in 107.4 GB", "With vLLM or SGLang use `--tool-call-parser poolside_v1 --reasoning-parser poolside_v1`, and with TensorRT-LLM `--tool_parser poolside_v1 --reasoning_parser laguna`", "Thinking is on by default in the template, pass `chat_template_kwargs` with `enable_thinking` false to turn it off, and keep `reasoning_content` from earlier turns", "The GGUFs ship set to 262,144 tokens. For 1M with llama.cpp add `--ctx-size 1048576 --rope-scaling yarn --rope-scale 128 --yarn-orig-ctx 8192`, which Poolside says can cost quality", "Sampling per the generation config is temperature 1.0 and top_k 20, and Poolside suggests temperature 0.7 and top_p 0.95 at 1M", "Expect occasional tool-schema slips in harnesses unlike Poolside's own, and validate JSON arguments, which Poolside lists as known limitations" ], "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": 75, "risk": 55, "upkeep": 41 }, "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-s-2.1\n# or, with vLLM on four GPUs:\nvllm serve poolside/Laguna-S-2.1 --tensor-parallel-size 4 --tool-call-parser poolside_v1 \\\n --reasoning-parser poolside_v1 --enable-auto-tool-choice --default-chat-template-kwargs '{\"enable_thinking\": true}'", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"laguna-s-2.1\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/laguna-s-2-1" }, "sameCompany": [ "laguna-m-1", "laguna-xs-2-1" ], "weights": { "trainedBy": "Poolside", "params": "117.6B total, 8B active", "context": 1048576, "goodFor": "Long-horizon agentic coding and terminal work with up to 1M tokens of context, on a 128 GB machine at 4-bit or a multi-GPU server.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "Laguna-S-2.1-NVFP4-mlx", "url": "https://huggingface.co/poolside/Laguna-S-2.1-NVFP4-mlx", "who": "publisher", "precision": "nvfp4", "sizeGB": 71.9, "memoryGB": 96, "format": "mlx", "runsOn": [ "MLX" ] }, { "name": "ollama laguna-s-2.1:nvfp4", "url": "https://ollama.com/library/laguna-s-2.1:nvfp4", "who": "ollama", "precision": "nvfp4", "sizeGB": 66, "memoryGB": 96, "format": "mlx", "runsOn": [ "Ollama" ], "note": "Ollama's MLX build, for Apple Silicon. Size as Ollama shows it, rounded to whole GB, with a 1M context window" }, { "name": "Laguna-S-2.1-INT4", "url": "https://huggingface.co/poolside/Laguna-S-2.1-INT4", "who": "publisher", "precision": "int4 (w4a16)", "sizeGB": 99.7, "memoryGB": 128, "format": "safetensors" }, { "name": "Laguna-S-2.1-NVFP4", "url": "https://huggingface.co/poolside/Laguna-S-2.1-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 99.7, "memoryGB": 128, "format": "safetensors", "note": "For NVIDIA Blackwell GPUs and DGX Spark" }, { "name": "Laguna-S-2.1-GGUF Q4_K_M", "url": "https://huggingface.co/poolside/Laguna-S-2.1-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 96, "memoryGB": 128, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "laguna-s-2.1-Q4_K_M.gguf, 96,031,829,760 bytes, replaced on 27 July 2026 with a mixed-precision build that keeps sensitive layers in BF16. The build card still lists it as 68 GB. The repository also holds Q8_0 (128.8 GB, 192 GB of memory), F16 (235.2 GB) and a 2.2 GB DFlash draft" }, { "name": "ollama laguna-s-2.1:latest", "url": "https://ollama.com/library/laguna-s-2.1:latest", "who": "ollama", "precision": "q4_k_m", "sizeGB": 96, "memoryGB": 128, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Size as Ollama's tags page shows it, rounded to whole GB. Its model blob (948255a146c0) isn't Poolside's GGUF file. The library also has q8_0 (128 GB), f16 (235 GB) and MLX tags" }, { "name": "Laguna-S-2.1-FP8", "url": "https://huggingface.co/poolside/Laguna-S-2.1-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 131.3, "memoryGB": 192, "format": "safetensors" }, { "name": "Laguna-S-2.1", "url": "https://huggingface.co/poolside/Laguna-S-2.1", "who": "publisher", "precision": "bf16", "sizeGB": 235.1, "memoryGB": 512, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ] } ], "run": "llama-server -hf poolside/Laguna-S-2.1-GGUF:Q4_K_M --jinja -c 65536 --port 8000", "runBuild": "Laguna-S-2.1-GGUF Q4_K_M", "checked": "2026-10-21", "minMemoryGB": 96 }, "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-XS-2.1", "url": "https://huggingface.co/poolside/Laguna-XS-2.1", "params": "33.44B (Hub safetensors count; the name gives only the size class XS)", "listing": "https://www.anchorterminal.com/tools/laguna-xs-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": 5 }, "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 release post links the repository.", "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, with a July 2026 entry for S 2.1.", "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-s-2-1.json" }, "answer": "Laguna S 2.1 scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 3 of 4 scored categories.", "b": { "slug": "ornith-1-5-397b", "name": "Ornith-1.5-397B", "vendor": "Ornith", "vendorUrl": "https://ornith.ai", "kind": "weights", "category": "open-weights", "summary": "Ornith-1.5-397B is Ornith's flagship open-weight mixture-of-experts reasoning model for coding and agent work, about 397B parameters on Qwen3.5's architecture with a 262,144-token context, published on Hugging Face in BF16, FP8, NVFP4 and GGUF and marked MIT.", "url": "https://www.anchorterminal.com/tools/ornith-1-5-397b", "markdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-397b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-397b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ornith-1-5-397b.json", "repo": "https://huggingface.co/ornith-ai/Ornith-1.5-397B", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-397B" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-397B-FP8" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-397B-NVFP4" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-397B-GGUF" }, { "registry": "ollama", "name": "ornith-1.5:397b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and Ornith's FP8, NVFP4 and GGUF builds 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 807 GB for the BF16 files, 418 GB for FP8 or 244 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-1.5-397B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "coding", "mit", "safetensors", "gguf", "fp8", "nvfp4", "self-hosted" ], "lastRelease": "2026-08-23", "graded": true, "anchor": { "graded": true, "score": 59, "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": 53, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 85, "reach": 65, "risk": 50, "upkeep": 36 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "Ornith's flagship coding and terminal-agent cousin, with XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It needs about 290 GB even at 4 bits, so a multi-GPU server, not the 128 GB reference machine. The main risk is an absent publisher, with no licence file, no safety results and files replaced without a note.", "bestFor": "A self-hosted frontier-scale coding agent on an 8-GPU server, where a clear licence file and an answering publisher aren't required", "strengths": [ "Runs in vLLM, SGLang, Transformers, llama.cpp and Ollama from their released versions", "Ornith publishes FP8, NVFP4 and four GGUF precisions, all ungated", "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 code in the repository" ], "weaknesses": [ "Needs about 290 GB of memory at 4 bits and about 970 GB in BF16, so it doesn't fit the reference machine", "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", "Every GGUF and the NVFP4 shards were replaced in place in August 2026 with no note, and the NVFP4 build now holds eight shards its index doesn't reference", "The two chat templates disagree on keeping earlier reasoning, structured output isn't documented and the card doesn't give the active parameters" ], "agentNotes": [ "Start it with the card's vLLM command at `--tensor-parallel-size 8`, or `ollama run ornith-1.5:397b` on a machine with about 300 GB of memory", "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", "When downloading the NVFP4 build, fetch only the files its model.safetensors.index.json names (the 25-shard set, 241.7 GB), not the eight extra shards", "Pin a revision when you download, since Ornith has replaced files in place", "Validate JSON output yourself, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 59 } ], "editorialScores": { "interface": 85, "reach": 65, "risk": 50, "upkeep": 36 }, "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-1.5:397b\n# or, with the card's vLLM command on eight GPUs:\nvllm serve ornith-ai/Ornith-1.5-397B --served-model-name Ornith-1.5-397B --tensor-parallel-size 8 --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-1.5:397b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/ornith-1-5-397b" }, "sameCompany": [ "ornith-1-5-35b-a3b", "ornith-1-5-9b" ], "weights": { "trainedBy": "Ornith", "builtOn": "Qwen3.5-397B-A17B, 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 Qwen3.5-397B-A17B (Apache 2.0)", "params": "397B", "context": 262144, "goodFor": "Flagship coding and terminal-agent mixture-of-experts model with tool calling and thinking on by default, text and images in, for a multi-GPU server.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Ornith-1.5-397B-FP8", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 418.4, "memoryGB": 512, "format": "safetensors", "note": "Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-397B-NVFP4", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 319.5, "memoryGB": 512, "format": "safetensors", "note": "NVFP4 needs NVIDIA Blackwell. The repository's files sum to 319.5 GB, but its index uses only the 25-shard set (241.7 GB); eight further shards uploaded on 20 October 2026 aren't in the index. Either way it needs 512 GB" }, { "name": "Ornith-1.5-397B-Q6_K", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 331, "memoryGB": 512, "format": "gguf", "note": "Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-397B-Q5_K_M", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q5_K_M.gguf", "who": "publisher", "precision": "q5_k_m", "sizeGB": 286.3, "memoryGB": 512, "format": "gguf", "note": "Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-397B-Q4_K_M", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 244.3, "memoryGB": 512, "format": "gguf", "note": "Add mmproj-Ornith-1.5-397B-BF16.gguf (0.9 GB) for image input. Too big for the 128 GB reference machine" }, { "name": "ollama ornith-1.5:397b", "url": "https://ollama.com/library/ornith-1.5:397b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 242, "memoryGB": 512, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M (241 GB) with a BF16 vision projector (922 MB); the library has no latest tag. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB. Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-397B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B", "who": "publisher", "precision": "bf16", "sizeGB": 806.8, "memoryGB": 1024, "format": "safetensors", "note": "Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-397B-Q8_0", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 428.5, "memoryGB": 1024, "format": "gguf", "note": "Too big for the 128 GB reference machine" } ], "run": "ollama run ornith-1.5:397b", "runBuild": "ollama ornith-1.5:397b", "checked": "2026-10-21", "minMemoryGB": 512 }, "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", "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-DFlash", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-DFlash", "params": "1.29B (Hub safetensors count)" } ], "unrestrictedCopies": 4 }, "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.", "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-1-5-397b.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": "2.3/5 (4)", "b": "2/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Laguna S 2.1 scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 3 of 4 scored categories.", "question": "Which is better for AI agents, Laguna S 2.1 or Ornith-1.5-397B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 75 against 65", "Risk, 55 against 50", "Documentation and upkeep, 41 against 36" ], "also": null, "goodFor": "Agents doing long coding sessions on a 128 GB machine or a multi-GPU server that want a permissive licence and a 1M context.", "slug": "laguna-s-2-1", "watchFor": "No reply from Poolside to any of 34 discussions, many of them reports about thinking and tool calls" }, { "aheadOn": null, "also": null, "goodFor": "A self-hosted frontier-scale coding agent on an 8-GPU server, where a clear licence file and an answering publisher aren't required", "slug": "ornith-1-5-397b", "watchFor": "Needs about 290 GB of memory at 4 bits and about 970 GB in BF16, so it doesn't fit the reference machine" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-s-2-1.json", "title": "Agents-A1-4B vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-397b.json", "title": "Agents-A1-4B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-s-2-1.json", "title": "Apertus v1.5 8B vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-397b.json", "title": "Apertus v1.5 8B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-s-2-1.json", "title": "Apodex-1.1-mini vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-397b.json", "title": "Apodex-1.1-mini vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-s-2-1.json", "title": "BigBang-v1 vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-397b.json", "title": "BigBang-v1 vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-laguna-s-2-1.json", "title": "Cosmos-Reason2-2B vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-397b.json", "title": "Cosmos-Reason2-2B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-laguna-s-2-1.json", "title": "Darwin-36B-Opus vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-397b.json", "title": "Darwin-36B-Opus vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-s-2-1.json", "title": "DeepSeek-V3.2 vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-397b.json", "title": "DeepSeek-V3.2 vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-s-2-1.json", "title": "DeepSeek V4.1 Flash vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-397b.json", "title": "DeepSeek V4.1 Flash vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-s-2-1.json", "title": "DeepSeek-V4-Flash-0731 vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-397b.json", "title": "DeepSeek-V4-Flash-0731 vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-laguna-s-2-1.json", "title": "DiffusionGemma 26B A4B IT vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-397b.json", "title": "DiffusionGemma 26B A4B IT vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-s-2-1.json", "title": "Gemma 4 12B IT vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-397b.json", "title": "Gemma 4 12B IT vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-s-2-1.json", "title": "Gemma 4 26B A4B IT vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-397b.json", "title": "Gemma 4 26B A4B IT vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-s-2-1.json", "title": "Gemma 4 31B IT vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-397b.json", "title": "Gemma 4 31B IT vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-s-2-1.json", "title": "Gemma 4 E2B IT vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-397b.json", "title": "Gemma 4 E2B IT vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-laguna-s-2-1.json", "title": "Gemma 4 E4B IT vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-397b.json", "title": "Gemma 4 E4B IT vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-s-2-1.json", "title": "GLM-4.5-Air vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-397b.json", "title": "GLM-4.5-Air vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-laguna-s-2-1.json", "title": "GLM-4.7-Flash vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-397b.json", "title": "GLM-4.7-Flash vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-s-2-1.json", "title": "GLM-5.3-Flash vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-397b.json", "title": "GLM-5.3-Flash vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-laguna-s-2-1.json", "title": "GLM-5.3 vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-397b.json", "title": "GLM-5.3 vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-laguna-s-2-1.json", "title": "gpt-oss-120b vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ornith-1-5-397b.json", "title": "gpt-oss-120b vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-laguna-s-2-1.json", "title": "gpt-oss-20b vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ornith-1-5-397b.json", "title": "gpt-oss-20b vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-laguna-s-2-1.json", "title": "Granite 4.2 3B vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-397b.json", "title": "Granite 4.2 3B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-laguna-s-2-1.json", "title": "Hy3 vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/hy3-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-397b.json", "title": "Hy3 vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-laguna-s-2-1.json", "title": "Inkling-Small vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-ornith-1-5-397b.json", "title": "Inkling-Small vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-laguna-s-2-1.json", "title": "Inkling vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/inkling-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-397b.json", "title": "Inkling vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-laguna-s-2-1.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ornith-1-5-397b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-s-2-1.json", "title": "Kimi K3 vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-397b.json", "title": "Kimi K3 vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-laguna-s-2-1.json", "title": "Kolibri-1 vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-laguna-s-2-1" }, { "json": 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"json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-397b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ornith-1-5-397b.json", "title": "Nemotron 3 Nano 30B A3B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-397b.json", "title": "Nemotron 3 Nano 4B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-397b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-397b.json", "title": "Nemotron 3 Super 120B A12B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-397b.json", "title": "Nemotron 3 Ultra 550B A55B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-397b.json", "title": "occamy-1.0 vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-397b.json", "title": "Olmo 3 7B Think vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-397b" }, { "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-1-5-397b-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-1-5-397b-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Ornith-1.5-397B vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-4b-instruct-2507.json", "title": "Ornith-1.5-397B vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-0-8b.json", "title": "Ornith-1.5-397B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-122b-a10b.json", "title": "Ornith-1.5-397B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-2b.json", "title": "Ornith-1.5-397B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-397b-a17b.json", "title": "Ornith-1.5-397B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-4b.json", "title": "Ornith-1.5-397B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-9b.json", "title": "Ornith-1.5-397B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-27b.json", "title": "Ornith-1.5-397B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-35b-a3b.json", "title": "Ornith-1.5-397B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-27b.json", "title": "Ornith-1.5-397B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-flash-next.json", "title": "Ornith-1.5-397B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-next.json", "title": "Ornith-1.5-397B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-2b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-32b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-4b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-8b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwopus3-8-27b-flash-v2.json", "title": "Ornith-1.5-397B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-smollm3-3b.json", "title": "Ornith-1.5-397B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-spark-x2-6-4b.json", "title": "Ornith-1.5-397B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-27b.json", "title": "Ornith-1.5-397B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Ornith-1.5-397B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-thinkingcap-qwen3-8-27b.json", "title": "Ornith-1.5-397B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 10, "edge": "laguna-s-2-1", "key": "reach", "laguna-s-2-1": 75, "name": "Reach", "ornith-1-5-397b": 65, "weight": 25 }, { "by": 5, "edge": "laguna-s-2-1", "key": "risk", "laguna-s-2-1": 55, "name": "Risk", "ornith-1-5-397b": 50, "weight": 20 }, { "by": 0, "edge": "", "key": "interface", "laguna-s-2-1": 85, "name": "Agent interface", "ornith-1-5-397b": 85, "weight": 10 }, { "by": 5, "edge": "laguna-s-2-1", "key": "upkeep", "laguna-s-2-1": 41, "name": "Documentation and upkeep", "ornith-1-5-397b": 36, "weight": 10 } ], "summary": "Laguna S 2.1 scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "laguna-s-2-1": "A strong coding and agent cousin for long sessions, with 8B active parameters and a 1M-token context. At 4-bit it needs a 96 to 128 GB machine, tight on a 128 GB one. The main risks are that nobody at Poolside answers its many reports, there are no safety results, and a build card misdescribes its file.", "ornith-1-5-397b": "Ornith's flagship coding and terminal-agent cousin, with XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It needs about 290 GB even at 4 bits, so a multi-GPU server, not the 128 GB reference machine. The main risk is an absent publisher, with no licence file, no safety results and files replaced without a note." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-397b", "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-397b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-397b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-397b.min.md" }, "markdown": "Laguna S 2.1 scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.\n\n- Laguna S 2.1: grade C, 61/100, rank #39 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-s-2-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-s-2-1.json\n- Ornith-1.5-397B: grade C, 59/100, rank #64 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-397b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-397b.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 S 2.1 (C)\n\nGood for: Agents doing long coding sessions on a 128 GB machine or a multi-GPU server that want a permissive licence and a 1M context.\n\nAhead on:\n- Reach, 75 against 65\n- Risk, 55 against 50\n- Documentation and upkeep, 41 against 36\n\nWatch for: No reply from Poolside to any of 34 discussions, many of them reports about thinking and tool calls\n\n### Ornith-1.5-397B (C)\n\nGood for: A self-hosted frontier-scale coding agent on an 8-GPU server, where a clear licence file and an answering publisher aren't required\n\nWatch for: Needs about 290 GB of memory at 4 bits and about 970 GB in BF16, so it doesn't fit the reference machine\n\n\n## Score by category\n\n| Category | Weight | Laguna S 2.1 | Ornith-1.5-397B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 75 | 65 | Laguna S 2.1 +10 |\n| Risk | 20% (20 this run) | 55 | 50 | Laguna S 2.1 +5 |\n| Agent interface | 10% (10 this run) | 85 | 85 | even |\n| Documentation and upkeep | 10% (10 this run) | 41 | 36 | Laguna S 2.1 +5 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **59 · C** | |\n\n## Facts side by side\n\n| Fact | Laguna S 2.1 | Ornith-1.5-397B |\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 | 2.3/5 (4) | 2/5 (4) |\n\n## Verdicts\n\n**Laguna S 2.1.** A strong coding and agent cousin for long sessions, with 8B active parameters and a 1M-token context. At 4-bit it needs a 96 to 128 GB machine, tight on a 128 GB one. The main risks are that nobody at Poolside answers its many reports, there are no safety results, and a build card misdescribes its file.\n\n**Ornith-1.5-397B.** Ornith's flagship coding and terminal-agent cousin, with XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It needs about 290 GB even at 4 bits, so a multi-GPU server, not the 128 GB reference machine. The main risk is an absent publisher, with no licence file, no safety results and files replaced without a note.\n\n## Before you call either\n\n### Laguna S 2.1\n\n1. Quickest start is `ollama run laguna-s-2.1`, 96 GB for the default Q4_K_M, or the 66 GB nvfp4 tag on Apple Silicon\n2. On our reference machine try `llama-server -hf poolside/Laguna-S-2.1-GGUF:Q4_K_M --jinja -c 65536`, from a llama.cpp release with the laguna architecture (b11772 has it). The 96 GB file leaves little room for context in 107.4 GB\n3. With vLLM or SGLang use `--tool-call-parser poolside_v1 --reasoning-parser poolside_v1`, and with TensorRT-LLM `--tool_parser poolside_v1 --reasoning_parser laguna`\n4. Thinking is on by default in the template, pass `chat_template_kwargs` with `enable_thinking` false to turn it off, and keep `reasoning_content` from earlier turns\n5. The GGUFs ship set to 262,144 tokens. For 1M with llama.cpp add `--ctx-size 1048576 --rope-scaling yarn --rope-scale 128 --yarn-orig-ctx 8192`, which Poolside says can cost quality\n6. Sampling per the generation config is temperature 1.0 and top_k 20, and Poolside suggests temperature 0.7 and top_p 0.95 at 1M\n7. Expect occasional tool-schema slips in harnesses unlike Poolside's own, and validate JSON arguments, which Poolside lists as known limitations\n\n### Ornith-1.5-397B\n\n1. Start it with the card's vLLM command at `--tensor-parallel-size 8`, or `ollama run ornith-1.5:397b` on a machine with about 300 GB of memory\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. When downloading the NVFP4 build, fetch only the files its model.safetensors.index.json names (the 25-shard set, 241.7 GB), not the eight extra shards\n6. Pin a revision when you download, since Ornith has replaced files in place\n7. Validate JSON output yourself, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, Laguna S 2.1 or Ornith-1.5-397B?\n\nLaguna S 2.1 scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 3 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-397b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-397b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"laguna-s-2-1\", \"b\": \"ornith-1-5-397b\"}`. From a terminal: `anchor compare laguna-s-2-1 ornith-1-5-397b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/laguna-s-2-1.json and https://www.anchorterminal.com/api/v1/tools/ornith-1-5-397b.json\n\n## Other comparisons with Laguna S 2.1 or Ornith-1.5-397B\n\n- [Agents-A1-4B vs Laguna S 2.1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-s-2-1.md)\n- [Agents-A1-4B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-397b.md)\n- [Apertus v1.5 8B vs Laguna S 2.1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-s-2-1.md)\n- [Apertus v1.5 8B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-397b.md)\n- [Apodex-1.1-mini vs Laguna S 2.1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-s-2-1.md)\n- [Apodex-1.1-mini vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-397b.md)\n- [BigBang-v1 vs Laguna S 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S 2.1 vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Laguna S 2.1 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-2b-instruct.md)\n- [Laguna S 2.1 vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-30b-a3b-instruct.md)\n- [Laguna S 2.1 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-32b-instruct.md)\n- [Laguna S 2.1 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-4b-instruct.md)\n- [Laguna S 2.1 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-8b-instruct.md)\n- [Laguna S 2.1 vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwopus3-8-27b-flash-v2.md)\n- [Laguna S 2.1 vs SmolLM3-3B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-smollm3-3b.md)\n- [Laguna S 2.1 vs 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[Ornith-1.5-397B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Ornith-1.5-397B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/ornith-1-5-397b-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": "2026-10-22", "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 S 2.1 vs Ornith-1.5-397B", "url": "" } ], "description": "Laguna S 2.1 scores 61 (C) to Ornith-1.5-397B's 59 (C) for open-weight models you run yourself. 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