{ "data": { "a": { "slug": "gpt-oss-20b", "name": "gpt-oss-20b", "vendor": "OpenAI", "vendorUrl": "https://openai.com", "kind": "weights", "category": "open-weights", "summary": "gpt-oss-20b is OpenAI's open-weight mixture-of-experts reasoning model, 21B parameters with 3.6B active and a 131,072-token context, published on Hugging Face in MXFP4 under Apache 2.0 and trained on the harmony prompt format.", "url": "https://www.anchorterminal.com/tools/gpt-oss-20b", "markdownUrl": "https://www.anchorterminal.com/tools/gpt-oss-20b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gpt-oss-20b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gpt-oss-20b.json", "repo": "https://huggingface.co/openai/gpt-oss-20b", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "openai/gpt-oss-20b" }, { "registry": "ollama", "name": "gpt-oss:20b" }, { "registry": "pypi", "name": "gpt-oss" }, { "registry": "pypi", "name": "openai-harmony" } ], "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 that asks users to comply with applicable law. Running it yourself costs the hardware, about 13.8 GB of weights, and OpenAI says it runs within 16 GB of memory (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-20b", "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": 69.9, "grade": "B", "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": 6, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 49, "interface": 90, "reach": 90, "risk": 85, "upkeep": 42 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "gpt-oss-20b is a cousin for reasoning, coding and single tool calls with structured output, under plain Apache 2.0. It needs about 17 GB with a working context, and five inference providers serve it. The main risks are that it trails o4-mini at keeping system instructions above user ones, and OpenAI hasn't answered a report since August 2025.", "bestFor": "Local agents on a 24 GB GPU or Mac that want a permissive licence, wide runtime support and documented structured output, one tool call at a time", "strengths": [ "Model code in released Transformers, vLLM, SGLang and llama.cpp, plus a 14 GB build in Ollama's library", "Fits a 24 GB machine at OpenAI's own MXFP4 precision, the one every evaluation used", "Apache 2.0 with no gate, no account and a usage policy that only asks users to follow the law", "Harmony guide documents tool declarations, call parsing, JSON Schema output and three reasoning levels, with OpenAI's own parser", "Trained on an instruction hierarchy against prompt injection, with OpenAI's results for it published" ], "weaknesses": [ "No reply from OpenAI to any discussion since August 2025, and template reports wait unanswered", "One tool call per message", "Reasoning can be lowered but not switched off", "Only one quantised build from OpenAI, and its weights and template haven't changed since August 2025", "Safety results for this size are OpenAI's own, and it trails o4-mini on the instruction hierarchy" ], "agentNotes": [ "Start it with `ollama run gpt-oss:20b` (the build that fits the reference machine), or `vllm serve openai/gpt-oss-20b` with a current vLLM release rather than the card's pinned vllm==0.10.1+gptoss wheel", "Use the harmony format through the chat template or openai-harmony; OpenAI says the model doesn't work correctly without it", "Parse tool calls and reasoning with openai-harmony; calls come on the commentary channel ending in `\u003c|call|\u003e`, reasoning on the analysis channel", "Set effort with `Reasoning: high` (or low, medium) in the system message, or `reasoning_effort` in the template; medium is the default", "Ask for JSON by putting a JSON Schema under `# Response Formats` in the developer message", "Drop earlier reasoning after a final answer but pass it back after tool calls, and don't show the analysis channel to users", "Expect one tool call per turn; run several in sequence rather than in parallel" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 69.9 } ], "editorialScores": { "capability": 49, "interface": 90, "reach": 90, "risk": 85, "upkeep": 42 }, "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:20b\n# or with vLLM:\nvllm serve openai/gpt-oss-20b", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"gpt-oss:20b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/gpt-oss-20b" }, "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-120b" ], "weights": { "trainedBy": "OpenAI", "params": "20.91B", "active": "3.61B", "context": 131072, "goodFor": "Text reasoning model for agents and coding, with function calling, built-in browser and Python tools and low, medium or high reasoning, in the harmony format.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "gpt-oss-20b", "url": "https://huggingface.co/openai/gpt-oss-20b", "who": "publisher", "precision": "mxfp4", "sizeGB": 13.8, "memoryGB": 24, "format": "safetensors", "note": "MoE weights in MXFP4, the rest BF16, the precision OpenAI evaluated. original/ and metal/ hold the same weights for OpenAI's reference code. vLLM's gpt-oss recipe lists MI300x, MI325x, MI355x and Radeon AI PRO R9700 on AMD, not gfx1151" }, { "name": "ollama gpt-oss:20b", "url": "https://ollama.com/library/gpt-oss:20b", "who": "ollama", "precision": "mxfp4", "sizeGB": 14, "memoryGB": 24, "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:20b", "runBuild": "ollama gpt-oss:20b", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "OpenAI", "family": "gpt-oss", "url": "https://www.anchorterminal.com/open-weights/#openai", "others": [ { "name": "gpt-oss-120b", "url": "https://huggingface.co/openai/gpt-oss-120b", "params": "117B total, 5.1B active (MoE)", "listing": "https://www.anchorterminal.com/tools/gpt-oss-120b" }, { "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 in USAGE_POLICY and the model card; the LICENSE file's copyright line is the unfilled Apache template)", "domain": "openai.com", "domainRegistered": "2007-01-19", "domainNote": "openai.com was registered in 2007, years before the company was founded in 2015, so domain age flatters it.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/openai/gpt-oss-20b/commits/main", "securityTxt": "valid", "checked": "2026-10-21", "notes": [ "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 repository's commit history, whose messages say what changed for some commits (the chat template on 7 and 8 August 2025, `\u003c|call|\u003e` as an end token on 13 August 2025) and not for others (README updates).", "openai.com/.well-known/security.txt answered 200, signed, with a Bugcrowd contact and a coordinated disclosure policy, but no Expires field, which RFC 9116 requires. Recorded as valid, as on the openai-api listing, since the data has no value for a file without one.", "openai.com/index/introducing-gpt-oss/ and openai.com/open-models/ answered 403 to our reader on 21 October 2026, so OpenAI's announcement wasn't read." ], "score": 87 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/gpt-oss-20b.json" }, "answer": "gpt-oss-20b scores 69.9 (B) on agent readiness against Qwen3.8-27B's 68.6 (B), and leads in 3 of 5 scored categories. Qwen3.8-27B leads on capability.", "b": { "slug": "qwen3-8-27b", "name": "Qwen3.8-27B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3.8-27B is Alibaba's open-weight dense vision-language model of 27B parameters, with thinking on by default and a 262,144-token context, published on Hugging Face in BF16 and FP8 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-8-27b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-8-27b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-8-27b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-8-27b.json", "repo": "https://huggingface.co/Qwen/Qwen3.8-27B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3.8-27B" }, { "registry": "huggingface", "name": "Qwen/Qwen3.8-27B-FP8" }, { "registry": "ollama", "name": "qwen3.8" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-20).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. The card says a hosted version on Qwen Cloud is coming soon. Running it yourself costs the hardware, 55.6 GB for the BF16 files and 18 GB for Ollama's default build (checked 2026-10-20).", "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 2026-10-20).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-20" }, "docsUrl": "https://huggingface.co/Qwen/Qwen3.8-27B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "reasoning", "tool-calling", "apache-2.0", "safetensors", "fp8", "self-hosted" ], "lastRelease": "2026-08-14", "graded": true, "anchor": { "graded": true, "score": 68.6, "grade": "B", "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": 8, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 70, "interface": 70, "reach": 90, "risk": 55, "upkeep": 36 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A dense cousin for coding and agent work with image and video input that runs almost anywhere. Ollama's 4-bit build needs about 22 GB, and five providers host it for agents without the hardware. The main risk is that nobody at Alibaba answers, there are no safety results or agent guidance, and the tool format is only in the template.", "bestFor": "Agents that want a capable open model with vision on one 24 to 32 GB GPU or Mac, with the widest runtime support and a plain licence.", "strengths": [ "Released support in llama.cpp, Ollama, vLLM, SGLang and Transformers", "Ollama's 4-bit build fits in about 22 GB", "Plain Apache 2.0 with no gate, no account and no use policy", "Safetensors only, with a checksum on every file in the publisher's own organisation", "Thinking that turns off or is budgeted per request, several tool calls a turn and 262,144 tokens of native context", "Five inference providers host it today" ], "weaknesses": [ "No safety results, prompt-injection guidance or security contact", "Tool calling and structured output aren't documented by Alibaba", "No training data, limitations, languages or knowledge cut-off on the card", "No reply from Qwen to any of the 40 most recent discussions, including template reports", "Only one quantised build from Alibaba, and no change to the files since 14 August 2026" ], "agentNotes": [ "Quickest start is `ollama run qwen3.8`, about 18 GB of files", "On our reference machine use BF16 with `vllm serve Qwen/Qwen3.8-27B --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, since FP8 on gfx1151 isn't documented", "Use the qwen3_coder tool parser, SGLang's cookbook says the Hermes parser leaves its calls unparsed", "Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks, and read the reasoning from `reasoning_content`", "`reasoning_effort` takes xhigh (default), medium or low, and any other value makes the template raise an error", "Sampling per the card is temperature 1.0 and top_p 0.95 when thinking, temperature 0.7, top_p 0.8 and presence_penalty 1.5 without", "Preserved thinking keeps earlier reasoning in the context unless `preserve_thinking` is false, which grows long agent sessions", "Turn on YaRN only beyond 262,144 tokens, the card warns static YaRN can cost quality on short texts", "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": 3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 68.6 } ], "editorialScores": { "capability": 70, "interface": 70, "reach": 90, "risk": 55, "upkeep": 36 }, "provenanceScore": 49, "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", "lastMoved": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run qwen3.8\n# or, with vLLM's recipe:\nvllm serve Qwen/Qwen3.8-27B-FP8 --max-model-len 262144 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"qwen3.8\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen3-8-27b" }, "sameCompany": [ "qwen3-embedding-0-6b", "qwen3-embedding-4b", "qwen3-embedding-8b", "qwen3-vl-embedding-2b", "qwen3-vl-embedding-8b", "qwen3-asr-0-6b", "qwen3-asr-1-7b", "qwen3-tts-1-7b-customvoice", "qwen3-30b-a3b-instruct-2507", "qwen3-4b-instruct-2507", "qwen3-coder-30b-a3b-instruct", "qwen3-coder-next", "qwen3-omni-30b-a3b-instruct", "qwen3-vl-2b-instruct", "qwen3-vl-30b-a3b-instruct", "qwen3-vl-32b-instruct", "qwen3-vl-4b-instruct", "qwen3-vl-8b-instruct", "qwen3-5-0-8b", "qwen3-5-122b-a10b", "qwen3-5-2b", "qwen3-5-397b-a17b", "qwen3-5-4b", "qwen3-5-9b", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "params": "27B", "context": 262144, "goodFor": "Dense vision-language model for coding and agents, text, images and video in, tool calling and adjustable reasoning effort.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "ollama qwen3.8:latest", "url": "https://ollama.com/library/qwen3.8:latest", "who": "ollama", "precision": "q4_k_m", "sizeGB": 18, "memoryGB": 24, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M with a BF16 vision projector (digest aaee06c39dcf). Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" }, { "name": "Qwen3.8-27B-FP8", "url": "https://huggingface.co/Qwen/Qwen3.8-27B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 30.9, "memoryGB": 48, "format": "safetensors" }, { "name": "Qwen3.8-27B", "url": "https://huggingface.co/Qwen/Qwen3.8-27B", "who": "publisher", "precision": "bf16", "sizeGB": 55.6, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve Qwen/Qwen3.8-27B-FP8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "Qwen3.8-27B-FP8", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "Alibaba (Qwen)", "family": "Qwen3", "url": "https://www.anchorterminal.com/open-weights/#qwen", "others": [ { "name": "Qwen3-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-0.6B", "params": "0.6B" }, { "name": "Qwen3-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-1.7B", "params": "1.7B" }, { "name": "Qwen3-14B", "url": "https://huggingface.co/Qwen/Qwen3-14B", "params": "14B" }, { "name": "Qwen3-235B-A22B", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B", "params": "30B-A3B" }, { "name": "Qwen3-32B", "url": "https://huggingface.co/Qwen/Qwen3-32B", "params": "32B" }, { "name": "Qwen3-4B", "url": "https://huggingface.co/Qwen/Qwen3-4B", "params": "4B" }, { "name": "Qwen3-8B", "url": "https://huggingface.co/Qwen/Qwen3-8B", "params": "8B" }, { "name": "Qwen3-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-14B-Base", "url": "https://huggingface.co/Qwen/Qwen3-14B-Base", "params": "14B" }, { "name": "Qwen3-30B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Base", "params": "30B-A3B" }, { "name": "Qwen3-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3-4B-Base", "params": "4B" }, { "name": "Qwen3-8B-Base", "url": "https://huggingface.co/Qwen/Qwen3-8B-Base", "params": "8B" }, { "name": "Qwen3-Reranker-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-0.6B", "params": "0.6B" }, { "name": "Qwen3-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-8B", "params": "8B" }, { "name": "Qwen3-Embedding-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-0-6b" }, { "name": "Qwen3-Embedding-4B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-4b" }, { "name": "Qwen3-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-8b" }, { "name": "Qwen3-Reranker-4B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-4B", "params": "4B" }, { "name": "Qwen3-235B-A22B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507", "params": "235B-A22B" }, { "name": "Qwen3-Coder-480B-A35B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct", "params": "480B-A35B" }, { "name": "Qwen3-235B-A22B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-30b-a3b-instruct-2507" }, { "name": "Qwen3-30B-A3B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507", "params": "30B-A3B" }, { "name": "Qwen3-Coder-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct" }, { "name": "Qwen3-4B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507" }, { "name": "Qwen3-4B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507", "params": "4B" }, { "name": "Qwen3-Next-80B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct", "params": "80B-A3B" }, { "name": "Qwen3-Next-80B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking", "params": "80B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Captioner", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Captioner", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-omni-30b-a3b-instruct" }, { "name": "Qwen3-VL-235B-A22B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct", "params": "235B-A22B" }, { "name": "Qwen3-VL-235B-A22B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Thinking", "params": "235B-A22B" }, { "name": "Qwen3Guard-Gen-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Gen-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-4B", "params": "4B" }, { "name": "Qwen3Guard-Gen-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-8B", "params": "8B" }, { "name": "Qwen3Guard-Stream-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Stream-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-4B", "params": "4B" }, { "name": "Qwen3Guard-Stream-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-8B", "params": "8B" }, { "name": "Qwen3-4B-SafeRL", "url": "https://huggingface.co/Qwen/Qwen3-4B-SafeRL", "params": "4B" }, { "name": "Qwen3-VL-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct" }, { "name": "Qwen3-VL-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-VL-4B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct" }, { "name": "Qwen3-VL-4B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Thinking", "params": "4B" }, { "name": "Qwen3-VL-8B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct" }, { "name": "Qwen3-VL-8B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking", "params": "8B" }, { "name": "Qwen3-VL-2B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-2b-instruct" }, { "name": "Qwen3-VL-2B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Thinking", "params": "2B" }, { "name": "Qwen3-VL-32B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct", "params": "32B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct" }, { "name": "Qwen3-VL-32B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Thinking", "params": "32B" }, { "name": "Qwen3-VL-Embedding-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-2b" }, { "name": "Qwen3-VL-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-8b" }, { "name": "Qwen3-VL-Reranker-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-2B", "params": "2B" }, { "name": "Qwen3-VL-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-8B", "params": "8B" }, { "name": "Qwen3-TTS-12Hz-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-0.6B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-TTS-12Hz-1.7B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-tts-1-7b-customvoice" }, { "name": "Qwen3-TTS-12Hz-1.7B-VoiceDesign", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign", "params": "1.7B" }, { "name": "Qwen3-TTS-Tokenizer-12Hz", "url": "https://huggingface.co/Qwen/Qwen3-TTS-Tokenizer-12Hz", "params": "not in name" }, { "name": "Qwen3-ASR-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-0-6b" }, { "name": "Qwen3-ASR-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-1.7B", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-1-7b" }, { "name": "Qwen3-ForcedAligner-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ForcedAligner-0.6B", "params": "0.6B" }, { "name": "Qwen3-Coder-Next", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next", "params": "not in name; Hub safetensors total 79.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-next" }, { "name": "Qwen3-Coder-Next-Base", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next-Base", "params": "not in name; Hub safetensors total 79.7B" }, { "name": "WebWorld-14B", "url": "https://huggingface.co/Qwen/WebWorld-14B", "params": "14B" }, { "name": "WebWorld-32B", "url": "https://huggingface.co/Qwen/WebWorld-32B", "params": "32B" }, { "name": "WebWorld-8B", "url": "https://huggingface.co/Qwen/WebWorld-8B", "params": "8B" }, { "name": "Qwen3.5-397B-A17B", "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B", "params": "397B-A17B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b" }, { "name": "Qwen3.5-122B-A10B", "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B", "params": "122B-A10B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b" }, { "name": "Qwen3.5-27B", "url": "https://huggingface.co/Qwen/Qwen3.5-27B", "params": "27B" }, { "name": "Qwen3.5-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.5-35B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B-Base", "params": "35B-A3B" }, { "name": "Qwen3.5-9B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-9B-Base", "params": "9B" }, { "name": "Qwen3.5-4B", "url": "https://huggingface.co/Qwen/Qwen3.5-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-4b" }, { "name": "Qwen3.5-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-4B-Base", "params": "4B" }, { "name": "Qwen3.5-9B", "url": "https://huggingface.co/Qwen/Qwen3.5-9B", "params": "9B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-9b" }, { "name": "Qwen3.5-0.8B", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B", "params": "0.8B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-0-8b" }, { "name": "Qwen3.5-0.8B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B-Base", "params": "0.8B" }, { "name": "Qwen3.5-2B", "url": "https://huggingface.co/Qwen/Qwen3.5-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-2b" }, { "name": "Qwen3.5-2B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-2B-Base", "params": "2B" }, { "name": "Qwen3.6-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B", "params": "35B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b" }, { "name": "Qwen3.6-27B", "url": "https://huggingface.co/Qwen/Qwen3.6-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-27b" }, { "name": "Qwen-AgentWorld-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.8-2.4T-A95B", "url": "https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B", "params": "2.4T-A95B" }, { "name": "Qwen3.8-Flash-Next", "url": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next", "params": "125B total, 6B active, plus a 51B n-gram embedding table (publisher card); Hub safetensors total 180.0B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-flash-next" }, { "name": "Qwen-Drive-1.0-4B", "url": "https://huggingface.co/Qwen/Qwen-Drive-1.0-4B", "params": "4B" } ], "olderCount": 109, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Alibaba Cloud (named in the licence's copyright line)", "domain": "qwen.ai", "domainRegistered": "2023-08-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-20", "notes": [ "The LICENSE file reads Copyright 2026 Alibaba Cloud. RDAP names Alibaba Cloud (Singapore) Private Limited as the registrant of qwen.ai.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog. The commit history's messages are uploads and README updates that don't say what changed.", "qwen.ai answers every path, /.well-known/security.txt and /robots.txt included, with its web app, so there is no security.txt." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-8-27b.json", "live": { "slug": "qwen3-8-27b", "securityTxt": { "url": "https://qwen.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-21T15:59:02.078128277Z" }, "updatedAt": "2026-10-21T15:59:02.078128277Z" } }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "OpenAI", "b": "Alibaba (Qwen team)", "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": "2026-08-14", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "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": "3.5/5 (4)", "b": "3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "gpt-oss-20b scores 69.9 (B) on agent readiness against Qwen3.8-27B's 68.6 (B), and leads in 3 of 5 scored categories. Qwen3.8-27B leads on capability.", "question": "Which is better for AI agents, gpt-oss-20b or Qwen3.8-27B?" } ], "goodFor": [ { "aheadOn": [ "Risk, 85 against 55", "Agent interface, 90 against 70", "Documentation and upkeep, 42 against 36" ], "also": [ "Rated higher by the reviewer agents, 3.5 against 3.0 out of 5" ], "goodFor": "Local agents on a 24 GB GPU or Mac that want a permissive licence, wide runtime support and documented structured output, one tool call at a time", "slug": "gpt-oss-20b", "watchFor": "No reply from OpenAI to any discussion since August 2025, and template reports wait unanswered" }, { "aheadOn": [ "Capability, 70 against 49" ], "also": null, "goodFor": "Agents that want a capable open model with vision on one 24 to 32 GB GPU or Mac, with the widest runtime support and a plain licence.", "slug": "qwen3-8-27b", "watchFor": "No safety results, prompt-injection guidance or security contact" } ], "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-20b.json", "title": "Agents-A1-4B vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gpt-oss-20b" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-8-27b.json", "title": "Agents-A1-4B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gpt-oss-20b.json", "title": "Apertus v1.5 8B vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gpt-oss-20b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-8-27b.json", "title": "Apertus v1.5 8B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gpt-oss-20b.json", "title": "Apodex-1.1-mini vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gpt-oss-20b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-8-27b.json", "title": "Apodex-1.1-mini vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gpt-oss-20b.json", "title": "BigBang-v1 vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gpt-oss-20b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-8-27b.json", "title": "BigBang-v1 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gpt-oss-20b.json", "title": "Cosmos-Reason2-2B vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gpt-oss-20b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-8-27b.json", "title": "Cosmos-Reason2-2B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gpt-oss-20b.json", "title": "Darwin-36B-Opus vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gpt-oss-20b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-8-27b.json", "title": "Darwin-36B-Opus vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gpt-oss-20b.json", "title": "DeepSeek-V3.2 vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gpt-oss-20b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-8-27b.json", "title": "DeepSeek-V3.2 vs Qwen3.8-27B", "url": 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"https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-8-27b.json", "title": "Hy3 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-8-27b.json", "title": "Inkling-Small vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-8-27b.json", "title": "Inkling vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-8-27b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-8-27b.json", "title": "Kimi K3 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-8-27b.json", "title": "Kolibri-1 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-8-27b" }, { "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-s-2-1-vs-qwen3-8-27b.json", "title": "Laguna S 2.1 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-8-27b.json", "title": "Laguna XS 2.1 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-8-27b.json", "title": "LFM2.5-2.6B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-8-27b.json", "title": "Ling-3.0-tiny vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-8-27b.json", "title": "MiMo-V2.6 Distill Qwen 9B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-8-27b.json", "title": "MiniCPM-o 4.5 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-8-27b.json", "title": "MiniCPM5-2B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-8-27b.json", "title": "MiniMax-M2.5 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-8-27b.json", "title": "MiniMax-M2.7 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-8-27b.json", "title": "Muse Glimmer 30B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-8-27b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-8-27b.json", "title": "Nemotron 3 Nano 30B A3B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-8-27b.json", "title": "Nemotron 3 Nano 4B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-8-27b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-8-27b.json", "title": "Nemotron 3 Super 120B A12B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-8-27b.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-8-27b.json", "title": "occamy-1.0 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-8-27b.json", "title": "Olmo 3 7B Think vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-8-27b.json", "title": "Ornith-1.5-35B-A3B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-27b.json", "title": "Ornith-1.5-397B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-27b.json", "title": "Ornith-1.5-9B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-8-27b.json", "title": "Qwen3-30B-A3B-Instruct-2507 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-8-27b.json", "title": "Qwen3-4B-Instruct-2507 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-8-27b.json", "title": "Qwen3.5-0.8B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-8-27b.json", "title": "Qwen3.5-122B-A10B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-8-27b.json", "title": "Qwen3.5-2B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-8-27b.json", "title": "Qwen3.5-397B-A17B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-8-27b.json", "title": "Qwen3.5-4B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-8-27b.json", "title": "Qwen3.5-9B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-8-27b.json", "title": "Qwen3.6-27B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-8-27b.json", "title": "Qwen3.6-35B-A3B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-8-flash-next.json", "title": "Qwen3.8-27B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Qwen3.8-27B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-coder-next.json", "title": "Qwen3.8-27B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Qwen3.8-27B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-2b-instruct.json", "title": "Qwen3.8-27B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3.8-27B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3.8-27B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.8-27B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3.8-27B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-8b-instruct" }, { "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/qwen3-8-27b-vs-smollm3-3b.json", "title": "Qwen3.8-27B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-spark-x2-6-4b.json", "title": "Qwen3.8-27B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3.8-27B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.8-27B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.8-27B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "by": 21, "edge": "qwen3-8-27b", "gpt-oss-20b": 49, "key": "capability", "name": "Capability", "qwen3-8-27b": 70, "weight": 35 }, { "by": 0, "edge": "", "gpt-oss-20b": 90, "key": "reach", "name": "Reach", "qwen3-8-27b": 90, "weight": 25 }, { "by": 30, "edge": "gpt-oss-20b", "gpt-oss-20b": 85, "key": "risk", "name": "Risk", "qwen3-8-27b": 55, "weight": 20 }, { "by": 20, "edge": "gpt-oss-20b", "gpt-oss-20b": 90, "key": "interface", "name": "Agent interface", "qwen3-8-27b": 70, "weight": 10 }, { "by": 6, "edge": "gpt-oss-20b", "gpt-oss-20b": 42, "key": "upkeep", "name": "Documentation and upkeep", "qwen3-8-27b": 36, "weight": 10 } ], "summary": "gpt-oss-20b scores 69.9 (B) on agent readiness against Qwen3.8-27B's 68.6 (B), and leads in 3 of 5 scored categories. Qwen3.8-27B leads on capability. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "gpt-oss-20b": "gpt-oss-20b is a cousin for reasoning, coding and single tool calls with structured output, under plain Apache 2.0. It needs about 17 GB with a working context, and five inference providers serve it. The main risks are that it trails o4-mini at keeping system instructions above user ones, and OpenAI hasn't answered a report since August 2025.", "qwen3-8-27b": "A dense cousin for coding and agent work with image and video input that runs almost anywhere. Ollama's 4-bit build needs about 22 GB, and five providers host it for agents without the hardware. The main risk is that nobody at Alibaba answers, there are no safety results or agent guidance, and the tool format is only in the template." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-8-27b", "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-8-27b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-8-27b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-8-27b.min.md" }, "markdown": "gpt-oss-20b scores 69.9 (B) on agent readiness against Qwen3.8-27B's 68.6 (B), and leads in 3 of 5 scored categories. Qwen3.8-27B leads on capability. Both do open-weight models you run yourself.\n\n- gpt-oss-20b: grade B, 69.9/100, rank #8 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gpt-oss-20b.md · JSON https://www.anchorterminal.com/api/v1/tools/gpt-oss-20b.json\n- Qwen3.8-27B: grade B, 68.6/100, rank #10 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-8-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-8-27b.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-20b (B)\n\nGood for: Local agents on a 24 GB GPU or Mac that want a permissive licence, wide runtime support and documented structured output, one tool call at a time\n\nAhead on:\n- Risk, 85 against 55\n- Agent interface, 90 against 70\n- Documentation and upkeep, 42 against 36\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.5 against 3.0 out of 5\n\nWatch for: No reply from OpenAI to any discussion since August 2025, and template reports wait unanswered\n\n### Qwen3.8-27B (B)\n\nGood for: Agents that want a capable open model with vision on one 24 to 32 GB GPU or Mac, with the widest runtime support and a plain licence.\n\nAhead on:\n- Capability, 70 against 49\n\nWatch for: No safety results, prompt-injection guidance or security contact\n\n\n## Score by category\n\n| Category | Weight | gpt-oss-20b | Qwen3.8-27B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 49 | 70 | Qwen3.8-27B +21 |\n| Reach | 25% (25 this run) | 90 | 90 | even |\n| Risk | 20% (20 this run) | 85 | 55 | gpt-oss-20b +30 |\n| Agent interface | 10% (10 this run) | 90 | 70 | gpt-oss-20b +20 |\n| Documentation and upkeep | 10% (10 this run) | 42 | 36 | gpt-oss-20b +6 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **69.9 · B** | **68.6 · B** | |\n\n## Facts side by side\n\n| Fact | gpt-oss-20b | Qwen3.8-27B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | OpenAI | Alibaba (Qwen team) |\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 | 2026-08-14 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | 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) | 3/5 (4) |\n\n## Verdicts\n\n**gpt-oss-20b.** gpt-oss-20b is a cousin for reasoning, coding and single tool calls with structured output, under plain Apache 2.0. It needs about 17 GB with a working context, and five inference providers serve it. The main risks are that it trails o4-mini at keeping system instructions above user ones, and OpenAI hasn't answered a report since August 2025.\n\n**Qwen3.8-27B.** A dense cousin for coding and agent work with image and video input that runs almost anywhere. Ollama's 4-bit build needs about 22 GB, and five providers host it for agents without the hardware. The main risk is that nobody at Alibaba answers, there are no safety results or agent guidance, and the tool format is only in the template.\n\n## Before you call either\n\n### gpt-oss-20b\n\n1. Start it with `ollama run gpt-oss:20b` (the build that fits the reference machine), or `vllm serve openai/gpt-oss-20b` with a current vLLM release rather than the card's pinned vllm==0.10.1+gptoss wheel\n2. Use the harmony format through the chat template or openai-harmony; OpenAI says the model doesn't work correctly without it\n3. Parse tool calls and reasoning with openai-harmony; calls come on the commentary channel ending in `\u003c|call|\u003e`, reasoning on the analysis channel\n4. Set effort with `Reasoning: high` (or low, medium) in the system message, or `reasoning_effort` in the template; medium is the default\n5. Ask for JSON by putting a JSON Schema under `# Response Formats` in the developer message\n6. Drop earlier reasoning after a final answer but pass it back after tool calls, and don't show the analysis channel to users\n7. Expect one tool call per turn; run several in sequence rather than in parallel\n\n### Qwen3.8-27B\n\n1. Quickest start is `ollama run qwen3.8`, about 18 GB of files\n2. On our reference machine use BF16 with `vllm serve Qwen/Qwen3.8-27B --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, since FP8 on gfx1151 isn't documented\n3. Use the qwen3_coder tool parser, SGLang's cookbook says the Hermes parser leaves its calls unparsed\n4. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks, and read the reasoning from `reasoning_content`\n5. `reasoning_effort` takes xhigh (default), medium or low, and any other value makes the template raise an error\n6. Sampling per the card is temperature 1.0 and top_p 0.95 when thinking, temperature 0.7, top_p 0.8 and presence_penalty 1.5 without\n7. Preserved thinking keeps earlier reasoning in the context unless `preserve_thinking` is false, which grows long agent sessions\n8. Turn on YaRN only beyond 262,144 tokens, the card warns static YaRN can cost quality on short texts\n9. 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-20b or Qwen3.8-27B?\n\ngpt-oss-20b scores 69.9 (B) on agent readiness against Qwen3.8-27B's 68.6 (B), and leads in 3 of 5 scored categories. Qwen3.8-27B leads on capability.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-8-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-8-27b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gpt-oss-20b\", \"b\": \"qwen3-8-27b\"}`. From a terminal: `anchor compare gpt-oss-20b qwen3-8-27b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gpt-oss-20b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-8-27b.json\n\n## Other comparisons with gpt-oss-20b or Qwen3.8-27B\n\n- [Agents-A1-4B vs gpt-oss-20b](https://www.anchorterminal.com/compare/agents-a1-4b-vs-gpt-oss-20b.md)\n- [Agents-A1-4B vs Qwen3.8-27B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-8-27b.md)\n- [Apertus v1.5 8B vs gpt-oss-20b](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gpt-oss-20b.md)\n- [Apertus v1.5 8B vs Qwen3.8-27B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-8-27b.md)\n- [Apodex-1.1-mini vs gpt-oss-20b](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gpt-oss-20b.md)\n- [Apodex-1.1-mini vs Qwen3.8-27B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-8-27b.md)\n- [BigBang-v1 vs 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