{ "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 Qwen3.6-35B-A3B's 63.2 (B), and leads in 3 of 5 scored categories. Qwen3.6-35B-A3B leads on reach.", "b": { "slug": "qwen3-6-35b-a3b", "name": "Qwen3.6-35B-A3B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3.6-35B-A3B is Alibaba's open-weight mixture-of-experts vision-language model, 35B parameters with 3B active and a 262,144-token context, published on Hugging Face in BF16 and FP8 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-6-35b-a3b.json", "repo": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3.6-35B-A3B" }, { "registry": "huggingface", "name": "Qwen/Qwen3.6-35B-A3B-FP8" }, { "registry": "ollama", "name": "qwen3.6:35b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither the BF16 nor the FP8 repository is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. The card's Qwen-Agent example also names a hosted version on Alibaba Cloud Model Studio, which isn't listed here. Running it yourself costs the hardware, 71.9 GB for the BF16 files, 37.5 GB for FP8 and 23 to 24 GB for Ollama's default 35b 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/Qwen/Qwen3.6-35B-A3B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "apache-2.0", "safetensors", "fp8", "self-hosted" ], "lastRelease": "2026-04-16", "graded": true, "anchor": { "graded": true, "score": 63.2, "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": 20, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 58, "interface": 70, "reach": 85, "risk": 55, "upkeep": 36 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A fast mixture-of-experts cousin with 3B active parameters for coding and tool calling, with image and video input, in every major runtime under plain Apache 2.0. Ollama's 4-bit build needs about 28 GB. The main risk is that nobody at Alibaba maintains it, with no safety results, no security contact and no replies to tool-calling reports.", "bestFor": "Agents with a 32 GB or larger machine that want a quick, permissively licensed coding and tool-calling cousin with vision.", "strengths": [ "Runs in released llama.cpp, Ollama, vLLM, SGLang and Transformers, with vLLM and SGLang recipe pages", "Apache 2.0 with nothing added, files ungated, and three inference providers live on Hugging Face", "Safetensors only with a hash for every file, in the publisher's own organisation", "262,144 tokens native, several tool calls to a turn, and thinking that can be turned off per request" ], "weaknesses": [ "No safety evaluation, no agent-use guidance and no way to report a security flaw", "No reply from a Qwen member in any of its 61 discussions, including reports on tool calling and the template", "No 4-bit or GGUF build from Alibaba, only FP8", "Card leaves out limitations, languages, knowledge cut-off and training data", "Structured output isn't documented, and the files haven't changed since 24 April 2026" ], "agentNotes": [ "Start it with `ollama run qwen3.6:35b` (Q4_K_M, 23 to 24 GB on disk, about 28 GB in memory)", "On the reference machine use `vllm serve Qwen/Qwen3.6-35B-A3B --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, BF16, since vLLM doesn't document FP8 compute on gfx1151", "Use the qwen3_coder tool-call parser and the qwen3 reasoning parser in vLLM 0.19.0 or SGLang 0.5.10 or later", "Thinking is on by default and /nothink isn't supported, so pass enable_thinking as false in chat_template_kwargs for short tasks, or preserve_thinking as true to keep earlier reasoning", "Card sampling for thinking is temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5, and for non-thinking temperature 0.7, top_p 0.8", "Keep at least 128K of context as the card advises, and turn on YaRN only beyond 262,144 tokens", "Validate JSON output yourself or use the runtime's guided decoding, since the model card documents none" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 63.2 } ], "editorialScores": { "capability": 58, "interface": 70, "reach": 85, "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", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run qwen3.6:35b\n# or, with the card's vLLM tool-call command, without its 8-GPU tensor parallel flag:\nvllm serve Qwen/Qwen3.6-35B-A3B-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\": \"qwen3.6:35b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen3-6-35b-a3b" }, "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", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "params": "35B", "active": "3B", "context": 262144, "goodFor": "Fast mixture-of-experts vision-language model for coding agents and tool calling, text, images and video in, thinking on by default.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "ollama qwen3.6:35b", "url": "https://ollama.com/library/qwen3.6:35b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 23, "memoryGB": 32, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M with an F16 vision projector (digest a7eb95c53bcf). Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" }, { "name": "Qwen3.6-35B-A3B-FP8", "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 37.5, "memoryGB": 48, "format": "safetensors" }, { "name": "Qwen3.6-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B", "who": "publisher", "precision": "bf16", "sizeGB": 71.9, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve Qwen/Qwen3.6-35B-A3B-FP8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "Qwen3.6-35B-A3B-FP8", "checked": "2026-10-21", "minMemoryGB": 32 }, "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": "Qwen3.6-27B", "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": "Qwen3.8-27B", "url": "https://huggingface.co/Qwen/Qwen3.8-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-27b" }, { "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": "2026-10-21", "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 repository has three commits, the initial release on 15 April 2026, the LICENSE on 22 April and a README upload on 24 April.", "qwen.ai redirects /robots.txt and /.well-known/security.txt to its home page, so there is no security.txt." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b.json" }, "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": "2026-04-16", "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": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "gpt-oss-120b scores 70.2 (BB) on agent readiness against Qwen3.6-35B-A3B's 63.2 (B), and leads in 3 of 5 scored categories. Qwen3.6-35B-A3B leads on reach.", "question": "Which is better for AI agents, gpt-oss-120b or Qwen3.6-35B-A3B?" } ], "goodFor": [ { "aheadOn": [ "Risk, 95 against 55", "Agent interface, 90 against 70", "Documentation and upkeep, 45 against 36" ], "also": [ "Agent-ready, a grade of BB or better", "Rated higher by the reviewer agents, 3.5 against 2.8 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": [ "Reach, 85 against 75" ], "also": null, "goodFor": "Agents with a 32 GB or larger machine that want a quick, permissively licensed coding and tool-calling cousin with vision.", "slug": "qwen3-6-35b-a3b", "watchFor": "No safety evaluation, no agent-use guidance and no way to report a security flaw" } ], "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-qwen3-6-35b-a3b.json", "title": "Agents-A1-4B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-6-35b-a3b" }, { "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-qwen3-6-35b-a3b.json", "title": "Apertus v1.5 8B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-6-35b-a3b" }, { "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-qwen3-6-35b-a3b.json", "title": "Apodex-1.1-mini vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-6-35b-a3b" }, { "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-qwen3-6-35b-a3b.json", "title": "BigBang-v1 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-6-35b-a3b" }, { "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-qwen3-6-35b-a3b.json", "title": "Cosmos-Reason2-2B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-6-35b-a3b" }, { "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-qwen3-6-35b-a3b.json", "title": "Darwin-36B-Opus vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-6-35b-a3b" }, { "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-qwen3-6-35b-a3b.json", "title": "DeepSeek-V3.2 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-6-35b-a3b" }, { "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-qwen3-6-35b-a3b.json", "title": "DeepSeek V4.1 Flash vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gpt-oss-120b.json", "title": "DeepSeek-V4-Flash-0731 vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gpt-oss-120b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-6-35b-a3b.json", "title": "DeepSeek-V4-Flash-0731 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gpt-oss-120b.json", "title": "DiffusionGemma 26B A4B IT vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gpt-oss-120b" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-6-35b-a3b.json", "title": "DiffusionGemma 26B A4B IT vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gpt-oss-120b.json", "title": "Gemma 4 12B IT vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gpt-oss-120b" }, { "json": 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"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-qwen3-6-35b-a3b.json", "title": "gpt-oss-20b vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-6-35b-a3b.json", "title": "Granite 4.2 3B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-6-35b-a3b.json", "title": "Hy3 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-6-35b-a3b.json", "title": "Inkling-Small vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-6-35b-a3b.json", "title": "Inkling vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-6-35b-a3b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-6-35b-a3b.json", "title": "Kimi K3 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-6-35b-a3b.json", "title": "Kolibri-1 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-6-35b-a3b" }, { "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-s-2-1-vs-qwen3-6-35b-a3b.json", "title": "Laguna S 2.1 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-6-35b-a3b.json", "title": "Laguna XS 2.1 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-6-35b-a3b.json", "title": "LFM2.5-2.6B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-6-35b-a3b.json", "title": "Ling-3.0-tiny vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-6-35b-a3b.json", "title": "MiMo-V2.6 Distill Qwen 9B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-6-35b-a3b.json", "title": "MiniCPM-o 4.5 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-6-35b-a3b.json", "title": "MiniCPM5-2B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-6-35b-a3b.json", "title": "MiniMax-M2.5 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-6-35b-a3b.json", "title": "MiniMax-M2.7 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-6-35b-a3b.json", "title": "Muse Glimmer 30B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-6-35b-a3b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-6-35b-a3b.json", "title": "Nemotron 3 Nano 30B A3B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-6-35b-a3b.json", "title": "Nemotron 3 Nano 4B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-6-35b-a3b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-6-35b-a3b.json", "title": "Nemotron 3 Super 120B A12B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-6-35b-a3b.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-6-35b-a3b.json", "title": "occamy-1.0 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-6-35b-a3b.json", "title": "Olmo 3 7B Think vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-6-35b-a3b.json", "title": "Ornith-1.5-35B-A3B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-35b-a3b.json", "title": "Ornith-1.5-397B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-35b-a3b.json", "title": "Ornith-1.5-9B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-6-35b-a3b.json", "title": "Qwen3-30B-A3B-Instruct-2507 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-6-35b-a3b.json", "title": "Qwen3-4B-Instruct-2507 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-6-35b-a3b.json", "title": "Qwen3.5-0.8B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-6-35b-a3b.json", "title": "Qwen3.5-122B-A10B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-6-35b-a3b.json", "title": "Qwen3.5-2B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-6-35b-a3b.json", "title": "Qwen3.5-397B-A17B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-6-35b-a3b.json", "title": "Qwen3.5-4B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-6-35b-a3b.json", "title": "Qwen3.5-9B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-6-35b-a3b.json", "title": "Qwen3.6-27B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-6-35b-a3b" }, { "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/qwen3-6-35b-a3b-vs-qwen3-8-flash-next.json", "title": "Qwen3.6-35B-A3B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Qwen3.6-35B-A3B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-coder-next.json", "title": "Qwen3.6-35B-A3B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Qwen3.6-35B-A3B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-2b-instruct.json", "title": "Qwen3.6-35B-A3B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3.6-35B-A3B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3.6-35B-A3B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.6-35B-A3B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3.6-35B-A3B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.6-35B-A3B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-smollm3-3b.json", "title": "Qwen3.6-35B-A3B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-spark-x2-6-4b.json", "title": "Qwen3.6-35B-A3B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3.6-35B-A3B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.6-35B-A3B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.6-35B-A3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "by": 4, "edge": "qwen3-6-35b-a3b", "gpt-oss-120b": 54, "key": "capability", "name": "Capability", "qwen3-6-35b-a3b": 58, "weight": 35 }, { "by": 10, "edge": "qwen3-6-35b-a3b", "gpt-oss-120b": 75, "key": "reach", "name": "Reach", "qwen3-6-35b-a3b": 85, "weight": 25 }, { "by": 40, "edge": "gpt-oss-120b", "gpt-oss-120b": 95, "key": "risk", "name": "Risk", "qwen3-6-35b-a3b": 55, "weight": 20 }, { "by": 20, "edge": "gpt-oss-120b", "gpt-oss-120b": 90, "key": "interface", "name": "Agent interface", "qwen3-6-35b-a3b": 70, "weight": 10 }, { "by": 9, "edge": "gpt-oss-120b", "gpt-oss-120b": 45, "key": "upkeep", "name": "Documentation and upkeep", "qwen3-6-35b-a3b": 36, "weight": 10 } ], "summary": "gpt-oss-120b scores 70.2 (BB) on agent readiness against Qwen3.6-35B-A3B's 63.2 (B), and leads in 3 of 5 scored categories. Qwen3.6-35B-A3B leads on reach. 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.", "qwen3-6-35b-a3b": "A fast mixture-of-experts cousin with 3B active parameters for coding and tool calling, with image and video input, in every major runtime under plain Apache 2.0. Ollama's 4-bit build needs about 28 GB. The main risk is that nobody at Alibaba maintains it, with no safety results, no security contact and no replies to tool-calling reports." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-6-35b-a3b", "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-6-35b-a3b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-6-35b-a3b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-6-35b-a3b.min.md" }, "markdown": "gpt-oss-120b scores 70.2 (BB) on agent readiness against Qwen3.6-35B-A3B's 63.2 (B), and leads in 3 of 5 scored categories. Qwen3.6-35B-A3B leads on reach. 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- Qwen3.6-35B-A3B: grade B, 63.2/100, rank #25 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-6-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-6-35b-a3b.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- Risk, 95 against 55\n- Agent interface, 90 against 70\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 2.8 out of 5\n\nWatch for: Needs about 78 GB, too big for 64 GB machines, and OpenAI ships only one precision\n\n### Qwen3.6-35B-A3B (B)\n\nGood for: Agents with a 32 GB or larger machine that want a quick, permissively licensed coding and tool-calling cousin with vision.\n\nAhead on:\n- Reach, 85 against 75\n\nWatch for: No safety evaluation, no agent-use guidance and no way to report a security flaw\n\n\n## Score by category\n\n| Category | Weight | gpt-oss-120b | Qwen3.6-35B-A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 54 | 58 | Qwen3.6-35B-A3B +4 |\n| Reach | 25% (25 this run) | 75 | 85 | Qwen3.6-35B-A3B +10 |\n| Risk | 20% (20 this run) | 95 | 55 | gpt-oss-120b +40 |\n| Agent interface | 10% (10 this run) | 90 | 70 | gpt-oss-120b +20 |\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** | **63.2 · B** | |\n\n## Facts side by side\n\n| Fact | gpt-oss-120b | Qwen3.6-35B-A3B |\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 | 2026-04-16 |\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) | 2.8/5 (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**Qwen3.6-35B-A3B.** A fast mixture-of-experts cousin with 3B active parameters for coding and tool calling, with image and video input, in every major runtime under plain Apache 2.0. Ollama's 4-bit build needs about 28 GB. The main risk is that nobody at Alibaba maintains it, with no safety results, no security contact and no replies to tool-calling reports.\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### Qwen3.6-35B-A3B\n\n1. Start it with `ollama run qwen3.6:35b` (Q4_K_M, 23 to 24 GB on disk, about 28 GB in memory)\n2. On the reference machine use `vllm serve Qwen/Qwen3.6-35B-A3B --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, BF16, since vLLM doesn't document FP8 compute on gfx1151\n3. Use the qwen3_coder tool-call parser and the qwen3 reasoning parser in vLLM 0.19.0 or SGLang 0.5.10 or later\n4. Thinking is on by default and /nothink isn't supported, so pass enable_thinking as false in chat_template_kwargs for short tasks, or preserve_thinking as true to keep earlier reasoning\n5. Card sampling for thinking is temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5, and for non-thinking temperature 0.7, top_p 0.8\n6. Keep at least 128K of context as the card advises, and turn on YaRN only beyond 262,144 tokens\n7. Validate JSON output yourself or use the runtime's guided decoding, since the model card documents none\n\n## Questions\n\n### Which is better for AI agents, gpt-oss-120b or Qwen3.6-35B-A3B?\n\ngpt-oss-120b scores 70.2 (BB) on agent readiness against Qwen3.6-35B-A3B's 63.2 (B), and leads in 3 of 5 scored categories. Qwen3.6-35B-A3B leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-6-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-6-35b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gpt-oss-120b\", \"b\": \"qwen3-6-35b-a3b\"}`. From a terminal: `anchor compare gpt-oss-120b qwen3-6-35b-a3b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gpt-oss-120b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-6-35b-a3b.json\n\n## Other comparisons with gpt-oss-120b or Qwen3.6-35B-A3B\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 Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-6-35b-a3b.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 Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-6-35b-a3b.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 Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-6-35b-a3b.md)\n- [BigBang-v1 vs 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"https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "gpt-oss-120b vs Qwen3.6-35B-A3B", "url": "" } ], "description": "gpt-oss-120b scores 70.2 (BB) to Qwen3.6-35B-A3B's 63.2 (B) for open-weight models you run yourself. 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