{ "data": { "a": { "slug": "apodex-1-1-mini", "name": "Apodex-1.1-mini", "vendor": "Apodex AI", "vendorUrl": "https://www.apodex.com", "kind": "weights", "category": "open-weights", "summary": "Apodex-1.1-mini is Apodex AI's open-weight mixture-of-experts model for long-horizon agent work with files, data, code and tools, a fine-tune of Qwen3.5-35B-A3B with about 36B parameters, about 3B active per token and a 262,144-token context, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/apodex-1-1-mini", "markdownUrl": "https://www.anchorterminal.com/tools/apodex-1-1-mini.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/apodex-1-1-mini.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/apodex-1-1-mini.json", "repo": "https://huggingface.co/apodex/Apodex-1.1-mini", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "apodex/Apodex-1.1-mini" }, { "registry": "huggingface", "name": "apodex/Apodex-1.1-mini-FP8" }, { "registry": "huggingface", "name": "apodex/Apodex-1.1-mini-NVFP4" }, { "registry": "huggingface", "name": "apodex/Apodex-1.1-mini-GPTQ-Int4" }, { "registry": "huggingface", "name": "apodex/Apodex-1.1-mini-GGUF" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and all four of Apodex's build repositories are not gated (checked 2026-10-21). Apodex's hosted API needs an account and key.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 (the repository's LICENSE file). Running it yourself costs the hardware, about 72 GB for the BF16 files or 22.6 GB for Apodex's Q4_K_M GGUF with its vision projector. Apodex's own API serves it as apodex-1.1-mini at $0.10 per million input tokens, $0.01 cached and $1.00 output, doubled for requests over 200K input tokens (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. Apodex's hosted API bills by account (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/apodex/Apodex-1.1-mini", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "agentic", "derivative", "apache-2.0", "safetensors", "gguf", "fp8", "nvfp4", "gptq", "self-hosted" ], "lastRelease": "2026-08-25", "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": 29, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 85, "reach": 85, "risk": 65, "upkeep": 38 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "An agent cousin fine-tuned from Qwen3.5-35B-A3B for long tasks with files, code and tools, with parallel tool calling and about 3B active parameters. It needs 32 GB for Apodex's GGUF, NVFP4 or GPTQ-Int4 builds or about 86 GB in BF16, and Apodex's API serves it too. The main risks are no safety results and no replies from the publisher.", "bestFor": "Long-running agent tasks with tools on a 32 GB GPU or Mac, or through Apodex's API, under a plain licence", "strengths": [ "The card documents the tool-call format, the parsers for SGLang and vLLM and a full agent loop, with parallel calls", "Apodex publishes FP8, NVFP4, GPTQ-Int4 and GGUF builds, the 4-bit ones in 32 GB", "Runs in released vLLM, SGLang, Transformers and llama.cpp as a Qwen3.5 MoE", "Apache 2.0 through the chain, with the base's licence file carried and no gate", "Apodex's own API serves the same model", "Safetensors and GGUF only, with checksums on every weight file" ], "weaknesses": [ "No safety results, and agent guidance only from the harness", "Apodex answered none of the four discussions", "The card states no parameters, architecture, limitations or knowledge cut-off", "The template adds an Apodex identity block when no system prompt is sent and keeps earlier reasoning by default", "Structured output isn't documented" ], "agentNotes": [ "Serve it with `vllm serve apodex/Apodex-1.1-mini --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser qwen3`, or the same parsers in SGLang, so tool calls and reasoning come back parsed", "For llama.cpp, `llama-server -hf apodex/Apodex-1.1-mini-GGUF:Q4_K_M --jinja --reasoning-format deepseek`, and the card passes the repository's chat_template.jinja with --chat-template-file", "Pass tools through the API's tools parameter rather than in the system prompt, as the card asks", "The card recommends temperature 1.0, top_p 0.95, repetition_penalty 1.05 and max_tokens up to 32,768", "Send your own system prompt, or set identity_mode, since the template otherwise adds an Apodex identity block", "The template keeps all earlier reasoning in the context unless `preserve_thinking` is false, which grows long agent sessions", "Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention", "The card's Python example has a broken line where it runs the tool (available_toolsname), so fix it to `available_tools[name](**args)` before running", "Validate JSON output yourself, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.3, "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": 85, "risk": 65, "upkeep": 38 }, "provenanceScore": 40, "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": "llama-server -hf apodex/Apodex-1.1-mini-GGUF:Q4_K_M --jinja --reasoning-format deepseek -c 32768\n# or, with the card's vLLM command:\nvllm serve apodex/Apodex-1.1-mini --max-model-len 262144 --enable-auto-tool-choice \\\n --tool-call-parser qwen3_coder --reasoning-parser qwen3", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"apodex/Apodex-1.1-mini\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/apodex-1-1-mini" }, "weights": { "trainedBy": "Apodex AI", "builtOn": "Qwen/Qwen3.5-35B-A3B (Apache 2.0), a fine-tune, as declared in the metadata (base_model, relation finetune). Same config and the same parameter count, 35,951,822,704", "params": "35B (3B active)", "active": "3B", "context": 262144, "goodFor": "Mixture-of-experts agent model for long-horizon work with files, data, code and tools, fine-tuned from Qwen3.5-35B-A3B, with documented tool calling, parallel calls and thinking on by default.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Apodex-1.1-mini-NVFP4", "url": "https://huggingface.co/apodex/Apodex-1.1-mini-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 24.3, "memoryGB": 32, "format": "safetensors", "note": "NVFP4 (modelopt_mixed) needs NVIDIA Blackwell; the card serves it with SGLang" }, { "name": "Apodex-1.1-mini-GPTQ-Int4", "url": "https://huggingface.co/apodex/Apodex-1.1-mini-GPTQ-Int4", "who": "publisher", "precision": "int4", "sizeGB": 24.7, "memoryGB": 32, "format": "safetensors", "runsOn": [ "SGLang" ], "note": "The card serves it with SGLang" }, { "name": "apodex1.1-35b-Q4_K_M-MTP", "url": "https://huggingface.co/apodex/Apodex-1.1-mini-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 22.6, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "21.7 GB of weights with the multi-token prediction layer and a 0.9 GB F16 vision projector; the card passes the repository's chat_template.jinja to llama-server" }, { "name": "Apodex-1.1-mini-FP8", "url": "https://huggingface.co/apodex/Apodex-1.1-mini-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 37.5, "memoryGB": 48, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ] }, { "name": "Apodex-1.1-mini", "url": "https://huggingface.co/apodex/Apodex-1.1-mini", "who": "publisher", "precision": "bf16", "sizeGB": 71.9, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve apodex/Apodex-1.1-mini --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser qwen3", "runBuild": "Apodex-1.1-mini", "checked": "2026-10-21", "minMemoryGB": 32 }, "family": { "lab": "Apodex AI (apodex)", "family": "Apodex-1", "url": "https://www.anchorterminal.com/open-weights/#apodex", "others": [ { "name": "Apodex-1.0-mini", "url": "https://huggingface.co/apodex/Apodex-1.0-mini", "params": "35B total, 3B active (MoE; 35,951,822,704 by the Hub's safetensors count)" }, { "name": "Apodex-1.0-4B-SFT", "url": "https://huggingface.co/apodex/Apodex-1.0-4B-SFT", "params": "4B (4,659,865,088 by the Hub's safetensors count)" }, { "name": "Apodex-1.0-2B-SFT", "url": "https://huggingface.co/apodex/Apodex-1.0-2B-SFT", "params": "2B (2,274,069,824 by the Hub's safetensors count)" }, { "name": "Apodex-1.0-0.8B-SFT", "url": "https://huggingface.co/apodex/Apodex-1.0-0.8B-SFT", "params": "0.8B (873,438,784 by the Hub's safetensors count)" } ], "unrestrictedCopies": 2 }, "area": "models", "provenance": { "legalEntity": "Apodex (the site's copyright line reads 2026 Apodex)", "domain": "apodex.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "apodex.com links to the apodex organisation on Hugging Face, and the card links back to apodex.com, apodex.ai, platform.apodex.ai and github.com/ApodexAI/FrontierAgent. Every commit is by one account, Eric-LRL-130, an apodex member.", "Nothing is graded as hosted, so the licence stands in for terms of service and no privacy policy is scored. Apodex's API has its own terms, not read here.", "No changelog for the weights. The FrontierAgent harness keeps a CHANGELOG.", "apodex.com and apodex.ai return 404 for /.well-known/security.txt. FrontierAgent's SECURITY.md gives a reporting address and timeline for the harness.", "unchecked: the domain's registration date." ], "score": 40 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/apodex-1-1-mini.json" }, "answer": "Apodex-1.1-mini and Laguna S 2.1 score within a point of each other on agent readiness, 61 (C) and 61 (C).", "b": { "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" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Apodex AI", "b": "Poolside", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "OpenMDW-1.1", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-08-25", "b": "2026-08-19", "name": "Last release" }, { "a": "no document linked", "b": "", "name": "Terms last updated" }, { "a": "no document linked", "b": "", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { 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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" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apodex-1-1-mini.json", "title": "Agents-A1-4B vs Apodex-1.1-mini", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apodex-1-1-mini" }, { "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/apertus-v1-5-8b-vs-apodex-1-1-mini.json", "title": "Apertus v1.5 8B vs Apodex-1.1-mini", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-apodex-1-1-mini" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-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/apodex-1-1-mini-vs-bigbang-v1.json", "title": "Apodex-1.1-mini vs BigBang-v1", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-bigbang-v1" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-cosmos-reason2-2b.json", "title": "Apodex-1.1-mini vs Cosmos-Reason2-2B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-cosmos-reason2-2b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-darwin-36b-opus.json", "title": "Apodex-1.1-mini vs Darwin-36B-Opus", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-darwin-36b-opus" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v3-2.json", "title": "Apodex-1.1-mini vs 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"https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-9b.json", "title": "Laguna S 2.1 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-6-27b.json", "title": "Laguna S 2.1 vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-6-35b-a3b.json", "title": "Laguna S 2.1 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-8-27b.json", "title": "Laguna S 2.1 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-8-flash-next.json", "title": "Laguna S 2.1 vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Laguna S 2.1 vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-coder-next.json", "title": "Laguna S 2.1 vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Laguna S 2.1 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-2b-instruct.json", "title": "Laguna S 2.1 vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Laguna S 2.1 vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-32b-instruct.json", "title": "Laguna S 2.1 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-4b-instruct.json", "title": "Laguna S 2.1 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-8b-instruct.json", "title": "Laguna S 2.1 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwopus3-8-27b-flash-v2.json", "title": "Laguna S 2.1 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-smollm3-3b.json", "title": "Laguna S 2.1 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-spark-x2-6-4b.json", "title": "Laguna S 2.1 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-swift-1-5-qwen3-8-27b.json", "title": "Laguna S 2.1 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Laguna S 2.1 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-thinkingcap-qwen3-8-27b.json", "title": "Laguna S 2.1 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "apodex-1-1-mini": 85, "by": 10, "edge": "apodex-1-1-mini", "key": "reach", "laguna-s-2-1": 75, "name": "Reach", "weight": 25 }, { "apodex-1-1-mini": 65, "by": 10, "edge": "apodex-1-1-mini", "key": "risk", "laguna-s-2-1": 55, "name": "Risk", "weight": 20 }, { "apodex-1-1-mini": 85, "by": 0, "edge": "", "key": "interface", "laguna-s-2-1": 85, "name": "Agent interface", "weight": 10 }, { "apodex-1-1-mini": 38, "by": 3, "edge": "laguna-s-2-1", "key": "upkeep", "laguna-s-2-1": 41, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Apodex-1.1-mini and Laguna S 2.1 score within a point of each other on agent readiness, 61 (C) and 61 (C). Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "apodex-1-1-mini": "An agent cousin fine-tuned from Qwen3.5-35B-A3B for long tasks with files, code and tools, with parallel tool calling and about 3B active parameters. It needs 32 GB for Apodex's GGUF, NVFP4 or GPTQ-Int4 builds or about 86 GB in BF16, and Apodex's API serves it too. The main risks are no safety results and no replies from the publisher.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "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-laguna-s-2-1.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-s-2-1.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-s-2-1.min.md" }, "markdown": "Apodex-1.1-mini and Laguna S 2.1 score within a point of each other on agent readiness, 61 (C) and 61 (C). Both do open-weight models you run yourself.\n\n- Apodex-1.1-mini: grade C, 61/100, rank #36 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/apodex-1-1-mini.md · JSON https://www.anchorterminal.com/api/v1/tools/apodex-1-1-mini.json\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- 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### Apodex-1.1-mini (C)\n\nGood for: Long-running agent tasks with tools on a 32 GB GPU or Mac, or through Apodex's API, under a plain licence\n\nAhead on:\n- Reach, 85 against 75\n- Risk, 65 against 55\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.3 against 2.3 out of 5\n\nWatch for: No safety results, and agent guidance only from the harness\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\nWatch for: No reply from Poolside to any of 34 discussions, many of them reports about thinking and tool calls\n\n\n## Score by category\n\n| Category | Weight | Apodex-1.1-mini | Laguna S 2.1 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 75 | Apodex-1.1-mini +10 |\n| Risk | 20% (20 this run) | 65 | 55 | Apodex-1.1-mini +10 |\n| Agent interface | 10% (10 this run) | 85 | 85 | even |\n| Documentation and upkeep | 10% (10 this run) | 38 | 41 | Laguna S 2.1 +3 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Apodex-1.1-mini | Laguna S 2.1 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Apodex AI | Poolside |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | OpenMDW-1.1 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-25 | 2026-08-19 |\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 | 3.3/5 (4) | 2.3/5 (4) |\n\n## Verdicts\n\n**Apodex-1.1-mini.** An agent cousin fine-tuned from Qwen3.5-35B-A3B for long tasks with files, code and tools, with parallel tool calling and about 3B active parameters. It needs 32 GB for Apodex's GGUF, NVFP4 or GPTQ-Int4 builds or about 86 GB in BF16, and Apodex's API serves it too. The main risks are no safety results and no replies from the publisher.\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## Before you call either\n\n### Apodex-1.1-mini\n\n1. Serve it with `vllm serve apodex/Apodex-1.1-mini --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser qwen3`, or the same parsers in SGLang, so tool calls and reasoning come back parsed\n2. For llama.cpp, `llama-server -hf apodex/Apodex-1.1-mini-GGUF:Q4_K_M --jinja --reasoning-format deepseek`, and the card passes the repository's chat_template.jinja with --chat-template-file\n3. Pass tools through the API's tools parameter rather than in the system prompt, as the card asks\n4. The card recommends temperature 1.0, top_p 0.95, repetition_penalty 1.05 and max_tokens up to 32,768\n5. Send your own system prompt, or set identity_mode, since the template otherwise adds an Apodex identity block\n6. The template keeps all earlier reasoning in the context unless `preserve_thinking` is false, which grows long agent sessions\n7. Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n8. The card's Python example has a broken line where it runs the tool (available_toolsname), so fix it to `available_tools[name](**args)` before running\n9. Validate JSON output yourself, since structured output isn't documented\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## Questions\n\n### Which is better for AI agents, Apodex-1.1-mini or Laguna S 2.1?\n\nApodex-1.1-mini and Laguna S 2.1 score within a point of each other on agent readiness, 61 (C) and 61 (C).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-s-2-1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-s-2-1.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"apodex-1-1-mini\", \"b\": \"laguna-s-2-1\"}`. 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Spark-X2.6-4B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-spark-x2-6-4b.md)\n- [Laguna S 2.1 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-swift-1-5-qwen3-8-27b.md)\n- [Laguna S 2.1 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Laguna S 2.1 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-thinkingcap-qwen3-8-27b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-21", "license": "CC-BY-4.0", "method": "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": "Apodex-1.1-mini vs Laguna S 2.1", "url": "" } ], "description": "Apodex-1.1-mini and Laguna S 2.1 score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. 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