{ "data": { "a": { "slug": "laguna-m-1", "name": "Laguna M.1", "vendor": "Poolside", "vendorUrl": "https://poolside.ai", "kind": "weights", "category": "open-weights", "summary": "Laguna M.1 is Poolside's first-generation open-weight Mixture-of-Experts model for agentic coding, 225B total and 23B active parameters, with switchable reasoning and a 262,144-token context, published on Hugging Face under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/laguna-m-1", "markdownUrl": "https://www.anchorterminal.com/tools/laguna-m-1.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/laguna-m-1.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/laguna-m-1.json", "repo": "https://huggingface.co/poolside/Laguna-M.1", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "poolside/Laguna-M.1" }, { "registry": "huggingface", "name": "poolside/Laguna-M.1-FP8" }, { "registry": "huggingface", "name": "poolside/Laguna-M.1-NVFP4" } ], "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 both builds, 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 Apache 2.0. Running it yourself costs the hardware, 451.6 GB for the BF16 files and 139.3 GB for the NVFP4 build. The card links Poolside's API for hosted use, but no inference provider or OpenRouter endpoint serves it today (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-M.1", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "coding", "reasoning", "tool-calling", "apache-2.0", "safetensors", "fp8", "nvfp4", "self-hosted" ], "lastRelease": "2026-07-14", "graded": true, "anchor": { "graded": true, "score": 55.5, "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": 64, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 46, "interface": 85, "reach": 65, "risk": 55, "upkeep": 36 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A large coding and agent cousin under plain Apache 2.0, for agents with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine, and no local runtime library packages it. The main risks are no upkeep since July, no hosted fallback shown today, no safety results, and repository code on the SGLang route.", "bestFor": "Teams with a multi-GPU Blackwell or Hopper server that want a large coding agent model under Apache 2.0.", "strengths": [ "Plain Apache 2.0 with no gate, account or use policy", "Released support in vLLM, SGLang, Transformers and llama.cpp", "Poolside's own FP8 and NVFP4 builds", "Tool calling and the poolside_v1 parser documented on the card, several calls a turn", "Thinking on or off per request, with the reasoning returned in reasoning_content" ], "weaknesses": [ "The smallest build needs about 167 GB, more than a 128 GB machine", "No Ollama, LM Studio or publisher GGUF build", "No change to the files since 14 July 2026 and no reply to its one discussion", "No safety results and only general cautions for agent use", "No hosting shown today, OpenRouter has dropped it and no inference provider is mapped" ], "agentNotes": [ "Run the NVFP4 build on Blackwell GPUs with `vllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on vLLM 0.21.0 or later", "Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{\"enable_thinking\": true}'`, which Poolside recommends for agentic coding", "Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped", "Sampling per the card is temperature 1.0 and top_k 20", "The FP8 build's Docker image and serve.sh predate upstream support and set a 4,096-token context, so prefer a released vLLM", "It doesn't fit our 128 GB reference machine at any published precision", "Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 55.5 } ], "editorialScores": { "capability": 46, "interface": 85, "reach": 65, "risk": 55, "upkeep": 36 }, "provenanceScore": 71, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install 'vllm\u003e=0.21.0'\nvllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --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:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"poolside/Laguna-M.1-NVFP4\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/laguna-m-1" }, "sameCompany": [ "laguna-s-2-1", "laguna-xs-2-1" ], "weights": { "trainedBy": "Poolside", "params": "225.8B total, 23B active", "context": 262144, "goodFor": "Agentic coding on a multi-GPU server, with tool calling and reasoning that turns on or off per request, under Apache 2.0.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Laguna-M.1-NVFP4", "url": "https://huggingface.co/poolside/Laguna-M.1-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 139.3, "memoryGB": 192, "format": "safetensors", "note": "For NVIDIA Blackwell GPUs" }, { "name": "Laguna-M.1-FP8", "url": "https://huggingface.co/poolside/Laguna-M.1-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 238.6, "memoryGB": 512, "format": "safetensors", "note": "238.6 GB summed from the file list, of which 9.5 GB is a Docker image (image/laguna-vllm-v0.19.0-overlay.tar.gz) and 229.1 GB the weights. Either way 512 GB" }, { "name": "Laguna-M.1", "url": "https://huggingface.co/poolside/Laguna-M.1", "who": "publisher", "precision": "bf16", "sizeGB": 451.6, "memoryGB": 1024, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ] } ], "run": "vllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice --default-chat-template-kwargs '{\"enable_thinking\": true}'", "runBuild": "Laguna-M.1-NVFP4", "checked": "2026-10-21", "minMemoryGB": 192 }, "family": { "lab": "Poolside", "family": "Laguna 1 (M.1)", "url": "https://www.anchorterminal.com/open-weights/#poolside", "others": [ { "name": "Laguna-M.1-base", "url": "https://huggingface.co/poolside/Laguna-M.1-base", "params": "225.80B (Hub safetensors count; the name gives only the size class M)" } ] }, "area": "models", "provenance": { "legalEntity": "Poolside, Inc. (named in Poolside's Terms of Use; the LICENSE.md copyright line reads Poolside)", "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 M.1 blog post links the repository.", "Nothing is hosted for this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "Poolside's docs keep dated model release notes, with an April 2026 entry for M.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-m-1.json" }, "answer": "Qwopus3.8-27B-Flash-V2 scores 61 (C) on agent readiness against Laguna M.1's 55.5 (C), and leads in 2 of 4 scored categories. Laguna M.1 leads on risk and agent interface.", "b": { "slug": "qwopus3-8-27b-flash-v2", "name": "Qwopus3.8-27B-Flash-V2", "vendor": "Jackrong", "vendorUrl": "https://huggingface.co/Jackrong", "kind": "weights", "category": "open-weights", "summary": "Qwopus3.8-27B-Flash-V2 is Jackrong's open-weight 27B fine-tune of Qwen3.8-27B, by way of his Qwopus3.8-27B-Flash, trained for shorter reasoning in agent loops, published in BF16 and GGUF with a multi-token prediction head under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwopus3-8-27b-flash-v2", "markdownUrl": "https://www.anchorterminal.com/tools/qwopus3-8-27b-flash-v2.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwopus3-8-27b-flash-v2.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwopus3-8-27b-flash-v2.json", "repo": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2", "license": "Apache-2.0 (metadata only, no LICENSE file); built on Qwen3.8-27B under Apache 2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Jackrong/Qwopus3.8-27B-Flash-V2" }, { "registry": "huggingface", "name": "Jackrong/Qwopus3.8-27B-Flash-V2-GGUF" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use under Apache 2.0, the same licence as the Qwen3.8-27B base. Nothing is hosted. Running it yourself costs the hardware, 55.6 GB for the BF16 files and 15.4 GB for the smallest 4-bit 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/Jackrong/Qwopus3.8-27B-Flash-V2", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "reasoning", "tool-calling", "derivative", "fine-tune", "apache-2.0", "safetensors", "gguf", "mtp", "self-hosted" ], "lastRelease": "2026-09-21", "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": 39, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 60, "reach": 85, "risk": 50, "upkeep": 56 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A Qwen3.8-27B cousin tuned to think less per turn in long agent loops, with an MTP head for speculative decoding in llama.cpp. It fits 24 GB with Jackrong's 4-bit GGUF. The risks are one person's experimental release with no safety work, training traces taken from Claude and GPT, and a tool format that differs from Qwen's.", "bestFor": "Hobbyist and research agents that want a faster-finishing Qwen3.8-27B for local coding loops on a 24 GB GPU.", "strengths": [ "Jackrong's 4-bit GGUFs fit 24 GB, with many precisions up to BF16", "The base's architecture, so released vLLM, SGLang, Transformers and llama.cpp run it", "Apache 2.0 through the whole chain, with no gate or account", "Safetensors and GGUF only, with a checksum on every file", "Bundled MTP head for self-speculative decoding, and reasoning effort set per request", "A card that states its limitations and how its own tests were run" ], "weaknesses": [ "Published from a personal account, with no organisation, security contact or safety evaluation", "Training data includes traces derived from Claude and GPT, by the card's own account", "Tool calls switch to a Hermes-style JSON format that the card doesn't document", "Reasoning output location, context and cut-off aren't documented", "Reports of looping and a grammar failure wait without a reply" ], "agentNotes": [ "Quickest start is `llama-server -hf Jackrong/Qwopus3.8-27B-Flash-V2-GGUF:Q4_K_M --jinja`, about 17 GB, and add mmproj-F32.gguf for images", "The card's tests used Q5_K_M with the bundled MTP head and `--spec-type draft-mtp` in llama.cpp", "The template asks for tool calls as JSON inside tool_call tags, so use a Hermes-style parser (vLLM's hermes), not Qwen3.8's qwen3_coder", "For coding the card recommends enable_thinking true, reasoning_effort xhigh and temperature 0.3, which you must pass yourself, since generation_config.json says 1.0", "reasoning_effort takes xhigh (default), medium or low, and any other value makes the template raise an error", "Earlier reasoning stays in the context unless preserve_thinking is false", "Cap max_tokens, since a report says it loops more than V1" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "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": 60, "reach": 85, "risk": 50, "upkeep": 56 }, "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 Jackrong/Qwopus3.8-27B-Flash-V2-GGUF:Q4_K_M --jinja -c 65536\n# or, with vLLM:\nvllm serve Jackrong/Qwopus3.8-27B-Flash-V2 --max-model-len 131072 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser hermes", "http": "curl http://localhost:8080/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"temperature\": 0.3, \"chat_template_kwargs\": {\"enable_thinking\": true, \"reasoning_effort\": \"xhigh\"}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwopus3-8-27b-flash-v2" }, "weights": { "trainedBy": "Jackrong", "builtOn": "Jackrong/Qwopus3.8-27B-Flash (fine-tune, Apache 2.0), itself a fine-tune of Qwen/Qwen3.8-27B (Apache 2.0); declared in the card's metadata", "params": "27.8B", "context": 262144, "goodFor": "A 27B vision-language fine-tune for long agent loops and coding, trained to reason less per turn, with an MTP head for speculative decoding.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Qwopus3.8-27B-Flash-V2-MTP-IQ4_XS.gguf", "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-IQ4_XS.gguf", "who": "publisher", "precision": "iq4_xs", "sizeGB": 15.42, "memoryGB": 24, "format": "gguf", "note": "Vision needs mmproj-F32.gguf as well (1.84 GB)" }, { "name": "Qwopus3.8-27B-Flash-V2-MTP-Q4_K_S.gguf", "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-Q4_K_S.gguf", "who": "publisher", "precision": "q4_k_s", "sizeGB": 15.83, "memoryGB": 24, "format": "gguf" }, { "name": "Qwopus3.8-27B-Flash-V2-MTP-Q4_K_M.gguf", "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 16.81, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp", "Ollama" ] }, { "name": "Qwopus3.8-27B-Flash-V2-MTP-Q5_K_M.gguf", "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-Q5_K_M.gguf", "who": "publisher", "precision": "q5_k_m", "sizeGB": 19.54, "memoryGB": 24, "format": "gguf", "note": "The quantisation the card's own tests used, with --spec-type draft-mtp" }, { "name": "Qwopus3.8-27B-Flash-V2-MTP-Q6_K.gguf", "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 22.43, "memoryGB": 32, "format": "gguf" }, { "name": "Qwopus3.8-27B-Flash-V2-MTP-Q8_0.gguf", "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 29.05, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "Qwopus3.8-27B-Flash-V2", "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2", "who": "publisher", "precision": "bf16", "sizeGB": 55.58, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ] }, { "name": "Qwopus3.8-27B-Flash-V2-MTP-BF16.gguf", "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-BF16.gguf", "who": "publisher", "precision": "bf16", "sizeGB": 54.66, "memoryGB": 96, "format": "gguf", "note": "Jackrong also publishes Q3_K_L, Q3_K_M, Q3_K_S and Q2_K files (10.9 to 14.6 GB), below 4-bit precision" } ], "run": "llama-server -hf Jackrong/Qwopus3.8-27B-Flash-V2-GGUF:Q8_0 --jinja -c 131072", "runBuild": "Qwopus3.8-27B-Flash-V2-MTP-Q8_0.gguf", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "Jackrong", "family": "Qwopus3", "url": "https://www.anchorterminal.com/open-weights/#jackrong", "others": [ { "name": "Qwopus3.5-9B-v3", "url": "https://huggingface.co/Jackrong/Qwopus3.5-9B-v3", "params": "9B (9,653,104,368 by the Hub's safetensors count)" }, { "name": "Qwopus3.5-27B-v3", "url": "https://huggingface.co/Jackrong/Qwopus3.5-27B-v3", "params": "27B (27,356,728,560 by the Hub's safetensors count)" }, { "name": "Qwopus3.5-4B-v3", "url": "https://huggingface.co/Jackrong/Qwopus3.5-4B-v3", "params": "4B (4,659,865,088 by the Hub's safetensors count)" }, { "name": "Qwopus3.5-0.8B-v3", "url": "https://huggingface.co/Jackrong/Qwopus3.5-0.8B-v3", "params": "0.8B (873,438,784 by the Hub's safetensors count)" }, { "name": "Qwopus3.5-27B-v3.5", "url": "https://huggingface.co/Jackrong/Qwopus3.5-27B-v3.5", "params": "27B (27,356,728,560 by the Hub's safetensors count)" }, { "name": "Qwopus3.5-2B-v3", "url": "https://huggingface.co/Jackrong/Qwopus3.5-2B-v3", "params": "2B (2,274,069,824 by the Hub's safetensors count)" }, { "name": "Qwopus3.5-9B-v3.5", "url": "https://huggingface.co/Jackrong/Qwopus3.5-9B-v3.5", "params": "9B (9,653,104,368 by the Hub's safetensors count)" }, { "name": "Qwopus-GLM-18B-Healed", "url": "https://huggingface.co/Jackrong/Qwopus-GLM-18B-Healed", "params": "18B (16,572,666,096 by the Hub's safetensors count)" }, { "name": "Qwopus3.6-27B-v1-preview", "url": "https://huggingface.co/Jackrong/Qwopus3.6-27B-v1-preview", "params": "27B (27,781,427,952 by the Hub's safetensors count)" }, { "name": "Qwopus3.6-35B-A3B-v1", "url": "https://huggingface.co/Jackrong/Qwopus3.6-35B-A3B-v1", "params": "35B total, 3B active (MoE; 35,951,822,704 by the Hub's safetensors count)" }, { "name": "Qwopus3.6-27B-v2", "url": "https://huggingface.co/Jackrong/Qwopus3.6-27B-v2", "params": "27B (27,781,427,952 by the Hub's safetensors count)" }, { "name": "Qwopus3.5-9B-Coder", "url": "https://huggingface.co/Jackrong/Qwopus3.5-9B-Coder", "params": "9B (9,653,104,368 by the Hub's safetensors count)" }, { "name": "Qwopus3.5-4B-Coder", "url": "https://huggingface.co/Jackrong/Qwopus3.5-4B-Coder", "params": "4B (4,659,865,088 by the Hub's safetensors count)" }, { "name": "Qwopus3.6-27B-Coder", "url": "https://huggingface.co/Jackrong/Qwopus3.6-27B-Coder", "params": "27B (27,781,427,952 by the Hub's safetensors count)" }, { "name": "Qwopus3.6-35B-A3B-Coder", "url": "https://huggingface.co/Jackrong/Qwopus3.6-35B-A3B-Coder", "params": "35B total, 3B active (MoE; 35,951,822,704 by the Hub's safetensors count)" }, { "name": "Qwopus3.6-27B-Fusion-BF16", "url": "https://huggingface.co/Jackrong/Qwopus3.6-27B-Fusion-BF16", "params": "27B (27,320,697,856 by the Hub's GGUF count)" }, { "name": "Qwopus3.8-27B-Flash", "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash", "params": "27B (27,781,427,952 by the Hub's safetensors count)" }, { "name": "Qwopus3-5-9B-Coder-Flash", "url": "https://huggingface.co/Jackrong/Qwopus3-5-9B-Coder-Flash", "params": "9B (9,653,104,368 by the Hub's safetensors count)" } ], "olderCount": 53, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Jackrong, an individual Hugging Face account", "domain": "", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "Jackrong is a personal account (158 models, 5,852 followers), not an organisation, and no website or GitHub link is on the card. The card sends questions to a tester's X account.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.", "No changelog. The repository has one commit (21 September 2026); the card describes what changed from the first Flash release.", "No security contact or disclosure policy was found." ], "score": 40 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwopus3-8-27b-flash-v2.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Poolside", "b": "Jackrong", "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": "Apache-2.0 (metadata only, no LICENSE file); built on Qwen3.8-27B under Apache 2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-07-14", "b": "2026-09-21", "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": "2.5/5 (4)", "b": "2.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Qwopus3.8-27B-Flash-V2 scores 61 (C) on agent readiness against Laguna M.1's 55.5 (C), and leads in 2 of 4 scored categories. Laguna M.1 leads on risk and agent interface.", "question": "Which is better for AI agents, Laguna M.1 or Qwopus3.8-27B-Flash-V2?" } ], "goodFor": [ { "aheadOn": [ "Risk, 55 against 50", "Agent interface, 85 against 60" ], "also": null, "goodFor": "Teams with a multi-GPU Blackwell or Hopper server that want a large coding agent model under Apache 2.0.", "slug": "laguna-m-1", "watchFor": "The smallest build needs about 167 GB, more than a 128 GB machine" }, { "aheadOn": [ "Reach, 85 against 65", "Documentation and upkeep, 56 against 36" ], "also": null, "goodFor": "Hobbyist and research agents that want a faster-finishing Qwen3.8-27B for local coding loops on a 24 GB GPU.", "slug": "qwopus3-8-27b-flash-v2", "watchFor": "Published from a personal account, with no organisation, security contact or safety evaluation" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-m-1.json", "title": "Agents-A1-4B vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwopus3-8-27b-flash-v2.json", "title": "Agents-A1-4B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-m-1.json", "title": "Apertus v1.5 8B vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwopus3-8-27b-flash-v2.json", "title": "Apertus v1.5 8B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-m-1.json", "title": "Apodex-1.1-mini vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwopus3-8-27b-flash-v2.json", "title": "Apodex-1.1-mini vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-m-1.json", "title": "BigBang-v1 vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwopus3-8-27b-flash-v2.json", "title": "BigBang-v1 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-laguna-m-1.json", "title": "Cosmos-Reason2-2B vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwopus3-8-27b-flash-v2.json", "title": "Cosmos-Reason2-2B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-laguna-m-1.json", "title": "Darwin-36B-Opus vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwopus3-8-27b-flash-v2.json", "title": "Darwin-36B-Opus vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-m-1.json", "title": "DeepSeek-V3.2 vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwopus3-8-27b-flash-v2.json", "title": "DeepSeek-V3.2 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-m-1.json", "title": "DeepSeek V4.1 Flash vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwopus3-8-27b-flash-v2.json", "title": "DeepSeek V4.1 Flash vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-m-1.json", "title": "DeepSeek-V4-Flash-0731 vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwopus3-8-27b-flash-v2.json", "title": "DeepSeek-V4-Flash-0731 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-laguna-m-1.json", "title": "DiffusionGemma 26B A4B IT vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwopus3-8-27b-flash-v2.json", "title": "DiffusionGemma 26B A4B IT vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-m-1.json", "title": "Gemma 4 12B IT vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwopus3-8-27b-flash-v2.json", "title": "Gemma 4 12B IT vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-m-1.json", "title": "Gemma 4 26B A4B IT vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwopus3-8-27b-flash-v2.json", "title": "Gemma 4 26B A4B IT vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-m-1.json", "title": "Gemma 4 31B IT vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwopus3-8-27b-flash-v2.json", "title": "Gemma 4 31B IT vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-m-1.json", "title": "Gemma 4 E2B IT vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwopus3-8-27b-flash-v2.json", "title": "Gemma 4 E2B IT vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-laguna-m-1.json", "title": "Gemma 4 E4B IT vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwopus3-8-27b-flash-v2.json", "title": "Gemma 4 E4B IT vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwopus3-8-27b-flash-v2" }, { "json": 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"https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwopus3-8-27b-flash-v2.json", "title": "GLM-5.3-Flash vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-laguna-m-1.json", "title": "GLM-5.3 vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwopus3-8-27b-flash-v2.json", "title": "GLM-5.3 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-laguna-m-1.json", "title": "gpt-oss-120b vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-laguna-m-1" }, { "json": 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"https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-m-1.json", "title": "Kimi K3 vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwopus3-8-27b-flash-v2.json", "title": "Kimi K3 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-laguna-m-1.json", "title": "Kolibri-1 vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwopus3-8-27b-flash-v2.json", "title": "Kolibri-1 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-laguna-s-2-1.json", "title": "Laguna M.1 vs Laguna S 2.1", "url": 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"https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwopus3-8-27b-flash-v2.json", "title": "Ornith-1.5-35B-A3B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-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-1-5-9b-vs-qwopus3-8-27b-flash-v2.json", "title": "Ornith-1.5-9B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-30B-A3B-Instruct-2507 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-4B-Instruct-2507 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.5-0.8B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.5-122B-A10B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.5-2B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.5-397B-A17B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.5-4B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.5-9B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.6-27B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.6-35B-A3B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.8-27B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.8-Flash-Next vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-Coder-Next vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-VL-2B-Instruct vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-VL-32B-Instruct vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-VL-4B-Instruct vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-VL-8B-Instruct vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-smollm3-3b.json", "title": "Qwopus3.8-27B-Flash-V2 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-spark-x2-6-4b.json", "title": "Qwopus3.8-27B-Flash-V2 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwopus3.8-27B-Flash-V2 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwopus3.8-27B-Flash-V2 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwopus3.8-27B-Flash-V2 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 20, "edge": "qwopus3-8-27b-flash-v2", "key": "reach", "laguna-m-1": 65, "name": "Reach", "qwopus3-8-27b-flash-v2": 85, "weight": 25 }, { "by": 5, "edge": "laguna-m-1", "key": "risk", "laguna-m-1": 55, "name": "Risk", "qwopus3-8-27b-flash-v2": 50, "weight": 20 }, { "by": 25, "edge": "laguna-m-1", "key": "interface", "laguna-m-1": 85, "name": "Agent interface", "qwopus3-8-27b-flash-v2": 60, "weight": 10 }, { "by": 20, "edge": "qwopus3-8-27b-flash-v2", "key": "upkeep", "laguna-m-1": 36, "name": "Documentation and upkeep", "qwopus3-8-27b-flash-v2": 56, "weight": 10 } ], "summary": "Qwopus3.8-27B-Flash-V2 scores 61 (C) on agent readiness against Laguna M.1's 55.5 (C), and leads in 2 of 4 scored categories. Laguna M.1 leads on risk and agent interface. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "laguna-m-1": "A large coding and agent cousin under plain Apache 2.0, for agents with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine, and no local runtime library packages it. The main risks are no upkeep since July, no hosted fallback shown today, no safety results, and repository code on the SGLang route.", "qwopus3-8-27b-flash-v2": "A Qwen3.8-27B cousin tuned to think less per turn in long agent loops, with an MTP head for speculative decoding in llama.cpp. It fits 24 GB with Jackrong's 4-bit GGUF. The risks are one person's experimental release with no safety work, training traces taken from Claude and GPT, and a tool format that differs from Qwen's." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwopus3-8-27b-flash-v2", "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwopus3-8-27b-flash-v2.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwopus3-8-27b-flash-v2.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwopus3-8-27b-flash-v2.min.md" }, "markdown": "Qwopus3.8-27B-Flash-V2 scores 61 (C) on agent readiness against Laguna M.1's 55.5 (C), and leads in 2 of 4 scored categories. Laguna M.1 leads on risk and agent interface. Both do open-weight models you run yourself.\n\n- Laguna M.1: grade C, 55.5/100, rank #79 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-m-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-m-1.json\n- Qwopus3.8-27B-Flash-V2: grade C, 61/100, rank #46 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwopus3-8-27b-flash-v2.md · JSON https://www.anchorterminal.com/api/v1/tools/qwopus3-8-27b-flash-v2.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 M.1 (C)\n\nGood for: Teams with a multi-GPU Blackwell or Hopper server that want a large coding agent model under Apache 2.0.\n\nAhead on:\n- Risk, 55 against 50\n- Agent interface, 85 against 60\n\nWatch for: The smallest build needs about 167 GB, more than a 128 GB machine\n\n### Qwopus3.8-27B-Flash-V2 (C)\n\nGood for: Hobbyist and research agents that want a faster-finishing Qwen3.8-27B for local coding loops on a 24 GB GPU.\n\nAhead on:\n- Reach, 85 against 65\n- Documentation and upkeep, 56 against 36\n\nWatch for: Published from a personal account, with no organisation, security contact or safety evaluation\n\n\n## Score by category\n\n| Category | Weight | Laguna M.1 | Qwopus3.8-27B-Flash-V2 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 85 | Qwopus3.8-27B-Flash-V2 +20 |\n| Risk | 20% (20 this run) | 55 | 50 | Laguna M.1 +5 |\n| Agent interface | 10% (10 this run) | 85 | 60 | Laguna M.1 +25 |\n| Documentation and upkeep | 10% (10 this run) | 36 | 56 | Qwopus3.8-27B-Flash-V2 +20 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **55.5 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Laguna M.1 | Qwopus3.8-27B-Flash-V2 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Poolside | Jackrong |\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 | Apache-2.0 (metadata only, no LICENSE file); built on Qwen3.8-27B under Apache 2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-14 | 2026-09-21 |\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 | 2.5/5 (4) | 2.5/5 (4) |\n\n## Verdicts\n\n**Laguna M.1.** A large coding and agent cousin under plain Apache 2.0, for agents with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine, and no local runtime library packages it. The main risks are no upkeep since July, no hosted fallback shown today, no safety results, and repository code on the SGLang route.\n\n**Qwopus3.8-27B-Flash-V2.** A Qwen3.8-27B cousin tuned to think less per turn in long agent loops, with an MTP head for speculative decoding in llama.cpp. It fits 24 GB with Jackrong's 4-bit GGUF. The risks are one person's experimental release with no safety work, training traces taken from Claude and GPT, and a tool format that differs from Qwen's.\n\n## Before you call either\n\n### Laguna M.1\n\n1. Run the NVFP4 build on Blackwell GPUs with `vllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on vLLM 0.21.0 or later\n2. Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{\"enable_thinking\": true}'`, which Poolside recommends for agentic coding\n3. Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped\n4. Sampling per the card is temperature 1.0 and top_k 20\n5. The FP8 build's Docker image and serve.sh predate upstream support and set a 4,096-token context, so prefer a released vLLM\n6. It doesn't fit our 128 GB reference machine at any published precision\n7. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented\n\n### Qwopus3.8-27B-Flash-V2\n\n1. Quickest start is `llama-server -hf Jackrong/Qwopus3.8-27B-Flash-V2-GGUF:Q4_K_M --jinja`, about 17 GB, and add mmproj-F32.gguf for images\n2. The card's tests used Q5_K_M with the bundled MTP head and `--spec-type draft-mtp` in llama.cpp\n3. The template asks for tool calls as JSON inside tool_call tags, so use a Hermes-style parser (vLLM's hermes), not Qwen3.8's qwen3_coder\n4. For coding the card recommends enable_thinking true, reasoning_effort xhigh and temperature 0.3, which you must pass yourself, since generation_config.json says 1.0\n5. reasoning_effort takes xhigh (default), medium or low, and any other value makes the template raise an error\n6. Earlier reasoning stays in the context unless preserve_thinking is false\n7. Cap max_tokens, since a report says it loops more than V1\n\n## Questions\n\n### Which is better for AI agents, Laguna M.1 or Qwopus3.8-27B-Flash-V2?\n\nQwopus3.8-27B-Flash-V2 scores 61 (C) on agent readiness against Laguna M.1's 55.5 (C), and leads in 2 of 4 scored categories. Laguna M.1 leads on risk and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/laguna-m-1-vs-qwopus3-8-27b-flash-v2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/laguna-m-1-vs-qwopus3-8-27b-flash-v2.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"laguna-m-1\", \"b\": \"qwopus3-8-27b-flash-v2\"}`. From a terminal: `anchor compare laguna-m-1 qwopus3-8-27b-flash-v2`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/laguna-m-1.json and https://www.anchorterminal.com/api/v1/tools/qwopus3-8-27b-flash-v2.json\n\n## Other comparisons with Laguna M.1 or Qwopus3.8-27B-Flash-V2\n\n- [Agents-A1-4B vs Laguna M.1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-m-1.md)\n- [Agents-A1-4B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwopus3-8-27b-flash-v2.md)\n- [Apertus v1.5 8B vs Laguna M.1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-m-1.md)\n- [Apertus v1.5 8B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwopus3-8-27b-flash-v2.md)\n- [Apodex-1.1-mini vs Laguna M.1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-m-1.md)\n- [Apodex-1.1-mini vs 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"Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Laguna M.1 vs Qwopus3.8-27B-Flash-V2", "url": "" } ], "description": "Qwopus3.8-27B-Flash-V2 scores 61 (C) to Laguna M.1's 55.5 (C) for open-weight models you run yourself. 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