{ "data": { "a": { "slug": "gpt-oss-120b", "name": "gpt-oss-120b", "vendor": "OpenAI", "vendorUrl": "https://openai.com", "kind": "weights", "category": "open-weights", "summary": "gpt-oss-120b is OpenAI's open-weight mixture-of-experts reasoning model, 116.8B parameters with 5.1B active and a 131,072-token context, published on Hugging Face in MXFP4 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/gpt-oss-120b", "markdownUrl": "https://www.anchorterminal.com/tools/gpt-oss-120b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gpt-oss-120b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gpt-oss-120b.json", "repo": "https://huggingface.co/openai/gpt-oss-120b", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "openai/gpt-oss-120b" }, { "registry": "pypi", "name": "gpt-oss" }, { "registry": "pypi", "name": "openai-harmony" }, { "registry": "ollama", "name": "gpt-oss:120b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 with a one-line usage policy asking users to comply with applicable law. OpenAI's API page for the model shows no price and rate limits of 0 at every tier. Running it yourself costs the hardware, 65.2 GB for the MXFP4 files and 65 GB for Ollama's gpt-oss:120b build (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/openai/gpt-oss-120b", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "reasoning", "tool-calling", "apache-2.0", "safetensors", "mxfp4", "self-hosted" ], "lastRelease": "2025-08-26", "graded": true, "anchor": { "graded": true, "score": 70.2, "grade": "BB", "agentReady": true, "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": 4, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 54, "interface": 90, "reach": 75, "risk": 95, "upkeep": 45 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A reasoning and agent cousin that runs in most runtimes under Apache 2.0 with published safety work. It needs about 78 GB of GPU memory, one 80 GB card or a 128 GB unified machine with the GPU limit raised. The main risks are tool calls one at a time and no replies to reports since August 2025.", "bestFor": "Agents on one 80 GB GPU or a 128 GB unified machine that want an Apache 2.0 reasoning model with wide runtime support", "strengths": [ "Runs in Transformers, vLLM, SGLang, llama.cpp and Ollama, with LM Studio linked from the card", "Apache 2.0, no gate, and 11 inference providers serve it for agents without the hardware", "Safetensors only, a trained instruction hierarchy and an outside biosecurity evaluation", "Harmony tool format, JSON Schema output and three reasoning levels documented by OpenAI" ], "weaknesses": [ "Needs about 78 GB, too big for 64 GB machines, and OpenAI ships only one precision", "One tool call at a time", "No OpenAI reply in the last 40 discussions, including open template and tokenizer reports", "No change to the files since August 2025 or to the model code since November 2025", "Reasoning can be lowered but not turned off" ], "agentNotes": [ "Start it with `ollama run gpt-oss:120b` on the reference machine, the build that fits", "On the Strix Halo machine raise the GPU memory limit first with `amd-ttm --set 100`, since the default of about 62.8 GiB is below the 78 GB it needs", "With vLLM use `vllm serve openai/gpt-oss-120b --tool-call-parser openai --enable-auto-tool-choice` per vLLM's recipe, or parse raw output with openai-harmony", "Always use the harmony format through the chat template or openai-harmony, the card says it won't work correctly otherwise", "Set `Reasoning: low` in the system message for short tasks, medium is the default", "Pass the reasoning back after a tool call until the final answer, and keep the analysis channel away from end users", "Expect one tool call per turn and loop, don't wait for parallel calls" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "BB", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 70.2 } ], "editorialScores": { "capability": 54, "interface": 90, "reach": 75, "risk": 95, "upkeep": 45 }, "provenanceScore": 87, "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": "ollama run gpt-oss:120b\n# or, with vLLM's recipe:\nvllm serve openai/gpt-oss-120b --tool-call-parser openai --enable-auto-tool-choice", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"gpt-oss:120b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/gpt-oss-120b" }, "sameCompany": [ "openai-api", "openai-embeddings", "openai-guardrails", "openai-moderation", "openai-image-api", "openai-sora", "openai-speech-to-text", "gpt-live", "openai-realtime", "openai-agents-sdk", "openai-decisions-api", "openai-codex", "gpt-oss-20b" ], "weights": { "trainedBy": "OpenAI", "params": "116.83B", "active": "5.13B", "context": 131072, "goodFor": "Larger text reasoning model for agents and coding, with function calling, built-in browser and Python tools and three reasoning levels, in the harmony format.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "gpt-oss-120b", "url": "https://huggingface.co/openai/gpt-oss-120b", "who": "publisher", "precision": "mxfp4", "sizeGB": 65.2, "memoryGB": 96, "format": "safetensors", "note": "MoE weights in MXFP4, the rest BF16. original/ and metal/ duplicate the weights. vLLM's gpt-oss recipe doesn't list gfx1151" }, { "name": "ollama gpt-oss:120b", "url": "https://ollama.com/library/gpt-oss:120b", "who": "ollama", "precision": "mxfp4", "sizeGB": 65, "memoryGB": 96, "format": "ollama", "runsOn": [ "Ollama" ], "note": "OpenAI's MXFP4 weights; the card links it. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" } ], "run": "ollama run gpt-oss:120b", "runBuild": "ollama gpt-oss:120b", "checked": "2026-10-21", "minMemoryGB": 96 }, "family": { "lab": "OpenAI", "family": "gpt-oss", "url": "https://www.anchorterminal.com/open-weights/#openai", "others": [ { "name": "gpt-oss-20b", "url": "https://huggingface.co/openai/gpt-oss-20b", "params": "21B total, 3.6B active (MoE)", "listing": "https://www.anchorterminal.com/tools/gpt-oss-20b" }, { "name": "gpt-oss-safeguard-120b", "url": "https://huggingface.co/openai/gpt-oss-safeguard-120b", "params": "117B total, 5.1B active (MoE)" }, { "name": "gpt-oss-safeguard-20b", "url": "https://huggingface.co/openai/gpt-oss-safeguard-20b", "params": "21B total, 3.6B active (MoE)" } ], "olderCount": 6, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "OpenAI (named as publisher on the card; the openai-api listing's terms name OpenAI OpCo, LLC)", "domain": "openai.com", "domainRegistered": "2007-01-19", "domainNote": "openai.com was registered in 2007, before OpenAI existed.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/openai/gpt-oss/commits/main", "securityTxt": "valid", "checked": "2026-10-21", "notes": [ "The LICENSE file is Apache 2.0 with the template's placeholder copyright line, so the legal entity is the one OpenAI's own terms name, read for the openai-api listing.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the gpt-oss GitHub repository's commit history and tags (v0.0.1 to v0.0.9); the Hugging Face repository's commits stop on 26 August 2025.", "openai.com/.well-known/security.txt answered on 21 October 2026 with a Bugcrowd contact, an acknowledgements page and a coordinated vulnerability disclosure policy. It has no Expires line, which RFC 9116 requires." ], "score": 87 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/gpt-oss-120b.json" }, "answer": "gpt-oss-120b scores 70.2 (BB) on agent readiness against Laguna M.1's 55.5 (C), and leads in every scored category.", "b": { "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" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "OpenAI", "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": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2025-08-26", "b": "2026-07-14", "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" }, { "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": "3.5/5 (4)", "b": "2.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "gpt-oss-120b scores 70.2 (BB) on agent readiness against Laguna M.1's 55.5 (C), and leads in every scored category.", "question": "Which is better for AI agents, gpt-oss-120b or Laguna M.1?" } ], "goodFor": [ { "aheadOn": [ "Capability, 54 against 46", "Reach, 75 against 65", "Risk, 95 against 55", "Agent interface, 90 against 85", "Documentation and upkeep, 45 against 36" ], "also": [ "Agent-ready, a grade of BB or better", "Rated higher by the reviewer agents, 3.5 against 2.5 out of 5" ], "goodFor": "Agents on one 80 GB GPU or a 128 GB unified machine that want an Apache 2.0 reasoning model with wide runtime support", "slug": "gpt-oss-120b", "watchFor": "Needs about 78 GB, too big for 64 GB machines, and OpenAI ships only one precision" }, { "aheadOn": null, "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" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gpt-oss-120b.json", "title": "Agents-A1-4B vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gpt-oss-120b" }, { "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/apertus-v1-5-8b-vs-gpt-oss-120b.json", "title": "Apertus v1.5 8B vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gpt-oss-120b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-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/apodex-1-1-mini-vs-gpt-oss-120b.json", "title": "Apodex-1.1-mini vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gpt-oss-120b" }, { "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/bigbang-v1-vs-gpt-oss-120b.json", "title": "BigBang-v1 vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gpt-oss-120b" }, { "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/cosmos-reason2-2b-vs-gpt-oss-120b.json", "title": "Cosmos-Reason2-2B vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gpt-oss-120b" }, { "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/darwin-36b-opus-vs-gpt-oss-120b.json", "title": "Darwin-36B-Opus vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gpt-oss-120b" }, { "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/deepseek-v3-2-vs-gpt-oss-120b.json", "title": "DeepSeek-V3.2 vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gpt-oss-120b" }, { "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-v4-1-flash-vs-gpt-oss-120b.json", "title": "DeepSeek V4.1 Flash vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gpt-oss-120b" }, { "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": 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"https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwopus3-8-27b-flash-v2.json", "title": "gpt-oss-120b vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-smollm3-3b.json", "title": "gpt-oss-120b vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-spark-x2-6-4b.json", "title": "gpt-oss-120b vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-swift-1-5-qwen3-8-27b.json", "title": "gpt-oss-120b vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "gpt-oss-120b vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-thinkingcap-qwen3-8-27b.json", "title": "gpt-oss-120b vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-laguna-m-1.json", "title": "gpt-oss-20b vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-laguna-m-1.json", "title": "Granite 4.2 3B vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-laguna-m-1.json", "title": "Hy3 vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/hy3-vs-laguna-m-1" }, { "json": 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MiMo-V2.6 Distill Qwen 9B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-mimo-v2-6-distill-qwen-9b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minicpm-o-4-5.json", "title": "Laguna M.1 vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minicpm5-2b.json", "title": "Laguna M.1 vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minicpm5-2b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-5.json", "title": "Laguna M.1 vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-7.json", "title": "Laguna M.1 vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-muse-glimmer-30b.json", "title": "Laguna M.1 vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Laguna M.1 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-nano-30b-a3b.json", "title": "Laguna M.1 vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-nano-4b.json", "title": "Laguna M.1 vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Laguna M.1 vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-super-120b-a12b.json", "title": "Laguna M.1 vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-ultra-550b-a55b.json", "title": "Laguna M.1 vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-occamy-1-0.json", "title": "Laguna M.1 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-olmo-3-7b-think.json", "title": "Laguna M.1 vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-35b-a3b.json", "title": "Laguna M.1 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-397b.json", "title": "Laguna M.1 vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-9b.json", "title": "Laguna M.1 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Laguna M.1 vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-4b-instruct-2507.json", "title": "Laguna M.1 vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-0-8b.json", "title": "Laguna M.1 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-122b-a10b.json", "title": "Laguna M.1 vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-2b.json", "title": "Laguna M.1 vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-397b-a17b.json", "title": "Laguna M.1 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-4b.json", "title": "Laguna M.1 vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-9b.json", "title": "Laguna M.1 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-6-27b.json", "title": "Laguna M.1 vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-6-35b-a3b.json", "title": "Laguna M.1 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-8-27b.json", "title": "Laguna M.1 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-8-flash-next.json", "title": "Laguna M.1 vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Laguna M.1 vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-coder-next.json", "title": "Laguna M.1 vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Laguna M.1 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-2b-instruct.json", "title": "Laguna M.1 vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Laguna M.1 vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-32b-instruct.json", "title": "Laguna M.1 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-4b-instruct.json", "title": "Laguna M.1 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-8b-instruct.json", "title": "Laguna M.1 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwopus3-8-27b-flash-v2.json", "title": "Laguna M.1 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-smollm3-3b.json", "title": "Laguna M.1 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-spark-x2-6-4b.json", "title": "Laguna M.1 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-swift-1-5-qwen3-8-27b.json", "title": "Laguna M.1 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Laguna M.1 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-thinkingcap-qwen3-8-27b.json", "title": "Laguna M.1 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "by": 8, "edge": "gpt-oss-120b", "gpt-oss-120b": 54, "key": "capability", "laguna-m-1": 46, "name": "Capability", "weight": 35 }, { "by": 10, "edge": "gpt-oss-120b", "gpt-oss-120b": 75, "key": "reach", "laguna-m-1": 65, "name": "Reach", "weight": 25 }, { "by": 40, "edge": "gpt-oss-120b", "gpt-oss-120b": 95, "key": "risk", "laguna-m-1": 55, "name": "Risk", "weight": 20 }, { "by": 5, "edge": "gpt-oss-120b", "gpt-oss-120b": 90, "key": "interface", "laguna-m-1": 85, "name": "Agent interface", "weight": 10 }, { "by": 9, "edge": "gpt-oss-120b", "gpt-oss-120b": 45, "key": "upkeep", "laguna-m-1": 36, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "gpt-oss-120b scores 70.2 (BB) on agent readiness against Laguna M.1's 55.5 (C), and leads in every scored category. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "gpt-oss-120b": "A reasoning and agent cousin that runs in most runtimes under Apache 2.0 with published safety work. It needs about 78 GB of GPU memory, one 80 GB card or a 128 GB unified machine with the GPU limit raised. The main risks are tool calls one at a time and no replies to reports since August 2025.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-laguna-m-1", "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-laguna-m-1.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-laguna-m-1.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-laguna-m-1.min.md" }, "markdown": "gpt-oss-120b scores 70.2 (BB) on agent readiness against Laguna M.1's 55.5 (C), and leads in every scored category. Both do open-weight models you run yourself.\n\n- gpt-oss-120b: grade BB, 70.2/100, rank #6 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gpt-oss-120b.md · JSON https://www.anchorterminal.com/api/v1/tools/gpt-oss-120b.json\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- 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### gpt-oss-120b (BB)\n\nGood for: Agents on one 80 GB GPU or a 128 GB unified machine that want an Apache 2.0 reasoning model with wide runtime support\n\nAhead on:\n- Capability, 54 against 46\n- Reach, 75 against 65\n- Risk, 95 against 55\n- Agent interface, 90 against 85\n- Documentation and upkeep, 45 against 36\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- Rated higher by the reviewer agents, 3.5 against 2.5 out of 5\n\nWatch for: Needs about 78 GB, too big for 64 GB machines, and OpenAI ships only one precision\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\nWatch for: The smallest build needs about 167 GB, more than a 128 GB machine\n\n\n## Score by category\n\n| Category | Weight | gpt-oss-120b | Laguna M.1 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 54 | 46 | gpt-oss-120b +8 |\n| Reach | 25% (25 this run) | 75 | 65 | gpt-oss-120b +10 |\n| Risk | 20% (20 this run) | 95 | 55 | gpt-oss-120b +40 |\n| Agent interface | 10% (10 this run) | 90 | 85 | gpt-oss-120b +5 |\n| Documentation and upkeep | 10% (10 this run) | 45 | 36 | gpt-oss-120b +9 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **70.2 · BB** | **55.5 · C** | |\n\n## Facts side by side\n\n| Fact | gpt-oss-120b | Laguna M.1 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | OpenAI | 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 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2025-08-26 | 2026-07-14 |\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 | 3.5/5 (4) | 2.5/5 (4) |\n\n## Verdicts\n\n**gpt-oss-120b.** A reasoning and agent cousin that runs in most runtimes under Apache 2.0 with published safety work. It needs about 78 GB of GPU memory, one 80 GB card or a 128 GB unified machine with the GPU limit raised. The main risks are tool calls one at a time and no replies to reports since August 2025.\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## Before you call either\n\n### gpt-oss-120b\n\n1. Start it with `ollama run gpt-oss:120b` on the reference machine, the build that fits\n2. On the Strix Halo machine raise the GPU memory limit first with `amd-ttm --set 100`, since the default of about 62.8 GiB is below the 78 GB it needs\n3. With vLLM use `vllm serve openai/gpt-oss-120b --tool-call-parser openai --enable-auto-tool-choice` per vLLM's recipe, or parse raw output with openai-harmony\n4. Always use the harmony format through the chat template or openai-harmony, the card says it won't work correctly otherwise\n5. Set `Reasoning: low` in the system message for short tasks, medium is the default\n6. Pass the reasoning back after a tool call until the final answer, and keep the analysis channel away from end users\n7. Expect one tool call per turn and loop, don't wait for parallel calls\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## Questions\n\n### Which is better for AI agents, gpt-oss-120b or Laguna M.1?\n\ngpt-oss-120b scores 70.2 (BB) on agent readiness against Laguna M.1's 55.5 (C), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-laguna-m-1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-laguna-m-1.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gpt-oss-120b\", \"b\": \"laguna-m-1\"}`. From a terminal: `anchor compare gpt-oss-120b laguna-m-1`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gpt-oss-120b.json and https://www.anchorterminal.com/api/v1/tools/laguna-m-1.json\n\n## Other comparisons with gpt-oss-120b or Laguna M.1\n\n- [Agents-A1-4B vs gpt-oss-120b](https://www.anchorterminal.com/compare/agents-a1-4b-vs-gpt-oss-120b.md)\n- [Agents-A1-4B vs Laguna M.1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-m-1.md)\n- [Apertus v1.5 8B vs gpt-oss-120b](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gpt-oss-120b.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- [Apodex-1.1-mini vs gpt-oss-120b](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gpt-oss-120b.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- [BigBang-v1 vs 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"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": "gpt-oss-120b vs Laguna M.1", "url": "" } ], "description": "gpt-oss-120b scores 70.2 (BB) to Laguna M.1's 55.5 (C) for open-weight models you run yourself. 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