{ "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 Swift 1.5 Qwen3.8-27B's 61 (C), and leads in 3 of 4 scored categories.", "b": { "slug": "swift-1-5-qwen3-8-27b", "name": "Swift 1.5 Qwen3.8-27B", "vendor": "UkisAI", "vendorUrl": "https://ukisai.com", "kind": "weights", "category": "open-weights", "summary": "Swift 1.5 Qwen3.8-27B is UkisAI's post-trained Qwen3.8-27B that aims to think in fewer tokens, a 27B dense vision-language model with the base's 262,144-token context, published in BF16 with GGUF, AWQ, NVFP4, FP8 and MLX builds under UkisAI's own licence.", "url": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-27b", "markdownUrl": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-27b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-27b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-27b.json", "repo": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27b", "license": "Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue with a separate Swift Enterprise License above it. Built on Qwen3.8-27B, whose Apache 2.0 still governs the base", "transports": [], "packages": [ { "registry": "huggingface", "name": "ukisai/Swift-1.5-Qwen3.8-27b" }, { "registry": "huggingface", "name": "ukisai/Swift-1.5-Qwen3.8-27B-GGUF" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports the repository as not gated, though the card's metadata says gated true (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use. Commercial use is free for individuals and organisations whose gross annual revenue, affiliates included, is below US$1,000,000, and needs a separate Swift Enterprise License above it. The base Qwen3.8-27B stays under Apache 2.0. UkisAI's card also lists a free API for research purposes at ukisai.com/api/swift/v1. Running it yourself costs the hardware, 55.6 GB in BF16 and 15.9 to 18.4 GB for the 4-bit builds by the Hub's file lists (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/ukisai/Swift-1.5-Qwen3.8-27b", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "reasoning", "tool-calling", "fine-tune", "custom-licence", "safetensors", "gguf", "self-hosted" ], "lastRelease": "2026-09-24", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 42, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 60, "reach": 80, "risk": 50, "upkeep": 43 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A Qwen3.8-27B cousin post-trained to think in fewer tokens, for coding and agent work with images in, that fits a 24 GB machine with UkisAI's own 4-bit builds. Commercial use is free only below US$1M revenue. The main risks are that nobody at UkisAI answers its discussions, and that the shorter thinking rests on UkisAI's own benchmarks.", "bestFor": "Agents on a 24 to 32 GB GPU or Mac that want Qwen3.8-27B's skills with shorter thinking, for teams under US$1M revenue or research.", "strengths": [ "UkisAI's own 4-bit builds fit in about 20 GB, with GGUF, INT4, NVFP4, FP8 and MLX choices", "Runs in released vLLM, SGLang, Transformers and llama.cpp as Qwen3.8-27B does, with the same template and 262,144-token context", "Safetensors only, in the account UkisAI's site links to, with a checksum on every file", "Several tool calls a turn and post-training aimed at agent and coding tasks", "Ungated, with no account or terms to accept" ], "weaknesses": [ "Commercial use above US$1M gross annual revenue needs a separate licence", "No reply from UkisAI in any discussion, including reports that the model is broken", "No safety results, agent guidance or security contact", "The card doesn't say how to set reasoning effort, turn thinking off or ask for JSON", "Training data described only in general terms, and the efficiency claims are UkisAI's own runs" ], "agentNotes": [ "Quickest start is `llama-server -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M --jinja`, about 18.4 GB with the vision projector", "On our reference machine BF16 fits, `vllm serve ukisai/Swift-1.5-Qwen3.8-27b --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`", "The template is Qwen3.8-27B's, so `chat_template_kwargs` with `reasoning_effort` low, medium or xhigh (default) and `enable_thinking` false work as on the base", "UkisAI benchmarked it at temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 0, which is a reasonable starting point", "The MLX 4-bit build needs UkisAI's patched MLX-LM and has no image or video chat", "Check the licence before commercial use, it stops at US$1M gross annual revenue including affiliates", "MTP self-speculative decoding is available with the base's head, flags on the card" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.3, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 60, "reach": 80, "risk": 50, "upkeep": 43 }, "provenanceScore": 13, "checked": { "categories": { "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "vllm serve ukisai/Swift-1.5-Qwen3.8-27b --max-model-len 262144 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or the 4-bit GGUF with its vision projector:\nllama-server -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M --jinja", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"ukisai/Swift-1.5-Qwen3.8-27b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello, Swift.\"}], \"chat_template_kwargs\": {\"reasoning_effort\": \"low\"}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/swift-1-5-qwen3-8-27b" }, "sameCompany": [ "swift-1-5-qwen3-8-flash-next" ], "weights": { "trainedBy": "UkisAI, post-training Alibaba's Qwen3.8-27B", "builtOn": "Qwen/Qwen3.8-27B (fine-tune by way of Swift 1.0, Apache 2.0)", "params": "27B", "context": 262144, "goodFor": "A Qwen3.8-27B that thinks in fewer tokens, for coding and agent work with images in, tool calling and adjustable reasoning effort.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Swift-1.5-Qwen3.8-27B-GGUF Q4_K_M", "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27B-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 18.4, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "Swift-1.5-Qwen3.8-27B-Q4_K_M.gguf (17.44 GB) plus mmproj-Swift-1.5-Qwen3.8-27B-F16.gguf (0.93 GB), summed from the Hub's file list" }, { "name": "Swift-1.5-Qwen3.8-27B-GGUF IQ4_XS", "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27B-GGUF", "who": "publisher", "precision": "iq4_xs", "sizeGB": 16.4, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "The smallest 4-bit GGUF, 15.48 GB plus the 0.93 GB vision projector. The repository also has 2- and 3-bit files down to 8.9 GB, and a GSQ-RCO GGUF repository has 2- and 3-bit files from 8.4 GB" }, { "name": "Swift-1.5-4bit-MLX", "url": "https://huggingface.co/ukisai/Swift-1.5-4bit-MLX", "who": "publisher", "precision": "4bit", "sizeGB": 15.9, "memoryGB": 24, "format": "mlx", "runsOn": [ "MLX" ], "note": "Weights with the vision tensors, configs and tokenizer, 15.85 GB. Its card says stock MLX-LM 0.32.0 can't load it and ships a patch, and image and video chat aren't implemented" }, { "name": "Swift-1.5-Qwen3.8-27b-W4A16-AWQ", "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27b-W4A16-AWQ", "who": "publisher", "precision": "int4", "sizeGB": 19.6, "memoryGB": 24, "format": "safetensors", "runsOn": [ "vLLM" ], "note": "compressed-tensors W4A16. UkisAI also publishes AutoRound INT4 (19.6 GB) and AWQ plus GPTQ INT4 (19.5 GB)" }, { "name": "Swift-1.5-Qwen3.8-27b-NVFP4", "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27b-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 21.9, "memoryGB": 32, "format": "safetensors", "note": "For NVIDIA Blackwell GPUs" }, { "name": "Swift-1.5-Qwen3.8-27b-Quark-FP8-dynamic-AMD", "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27b-Quark-FP8-dynamic-AMD", "who": "publisher", "precision": "fp8", "sizeGB": 31.3, "memoryGB": 48, "format": "safetensors", "note": "AMD Quark W8A8, with Python loading helpers in the repository" }, { "name": "Swift-1.5-Qwen3.8-27b", "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27b", "who": "publisher", "precision": "bf16", "sizeGB": 55.6, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve ukisai/Swift-1.5-Qwen3.8-27b --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "Swift-1.5-Qwen3.8-27b", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "UkisAI (ukisai)", "family": "Swift 1", "url": "https://www.anchorterminal.com/open-weights/#ukisai", "others": [ { "name": "Swift-Qwen3.8-27b", "url": "https://huggingface.co/ukisai/Swift-Qwen3.8-27b", "params": "27B (27,781,427,952 by the Hub's safetensors count)" }, { "name": "Swift1.5-Qwen3.8-Flash-Next", "url": "https://huggingface.co/ukisai/Swift1.5-Qwen3.8-Flash-Next", "params": "125B (Qwen3.8-Flash-Next's stated size; MoE; 179,999,981,459 by the Hub's safetensors count)", "listing": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-flash-next" }, { "name": "Swift-1.5-Ornith-9B", "url": "https://huggingface.co/ukisai/Swift-1.5-Ornith-9B", "params": "9B (9,653,104,368 by the Hub's safetensors count)" }, { "name": "Swift-Bonsai-2-GGUF", "url": "https://huggingface.co/ukisai/Swift-Bonsai-2-GGUF", "params": "27B (26,895,998,464 by the Hub's GGUF count)" } ], "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "", "domain": "ukisai.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27b/blob/main/LICENSE", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The licence names UkisAI as licensor with no legal entity. ukisai.com describes a Europe-based applied AI lab in Belgrade, Eindhoven and Wilmington. RDAP wasn't queried.", "The weights are published from the Hugging Face user account ukisai (not an organisation), which ukisai.com's Swift page links to.", "The Swift Open License stands in for terms of service for the weights. The research API isn't graded, so no privacy policy is scored.", "No changelog. The repository's history was squashed to two commits.", "ukisai.com/.well-known/security.txt is 404, and ukisai/.github has no SECURITY.md." ], "score": 13 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-27b.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "OpenAI", "b": "UkisAI", "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": "Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue with a separate Swift Enterprise License above it. Built on Qwen3.8-27B, whose Apache 2.0 still governs the base", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2025-08-26", "b": "2026-09-24", "name": "Last release" }, { "a": "no document linked", "b": "", "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": "2.3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "gpt-oss-20b scores 69.9 (B) on agent readiness against Swift 1.5 Qwen3.8-27B's 61 (C), and leads in 3 of 4 scored categories.", "question": 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"https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-4b-instruct-2507.json", "title": "gpt-oss-20b vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-5-0-8b.json", "title": "gpt-oss-20b vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-5-122b-a10b.json", "title": "gpt-oss-20b vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-5-2b.json", "title": "gpt-oss-20b vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-5-397b-a17b.json", "title": "gpt-oss-20b vs Qwen3.5-397B-A17B", "url": 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Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-8-flash-next.json", "title": "gpt-oss-20b vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "gpt-oss-20b vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-coder-next.json", "title": "gpt-oss-20b vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "gpt-oss-20b vs Qwen3-Omni-30B-A3B-Instruct", "url": 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"https://www.anchorterminal.com/compare/gpt-oss-20b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "gpt-oss-20b vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-thinkingcap-qwen3-8-27b.json", "title": "gpt-oss-20b vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-swift-1-5-qwen3-8-27b.json", "title": "Granite 4.2 3B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-swift-1-5-qwen3-8-27b.json", "title": "Hy3 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/hy3-vs-swift-1-5-qwen3-8-27b" }, { "json": 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3 7B Think vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-swift-1-5-qwen3-8-27b.json", "title": "Ornith-1.5-35B-A3B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-27b.json", "title": "Ornith-1.5-397B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-27b.json", "title": "Ornith-1.5-9B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3-30B-A3B-Instruct-2507 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3-4B-Instruct-2507 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3.5-0.8B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3.5-122B-A10B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3.5-2B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3.5-397B-A17B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3.5-4B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3.5-9B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3.6-27B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-swift-1-5-qwen3-8-27b" }, { "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/qwen3-8-27b-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-flash-next-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3.8-Flash-Next vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3-Coder-Next vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3-VL-2B-Instruct vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3-VL-32B-Instruct vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3-VL-4B-Instruct vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3-VL-8B-Instruct vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwopus3.8-27B-Flash-V2 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-swift-1-5-qwen3-8-27b.json", "title": "SmolLM3-3B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-swift-1-5-qwen3-8-27b.json", "title": "Spark-X2.6-4B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Swift 1.5 Qwen3.8-27B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-thinkingcap-qwen3-8-27b.json", "title": "Swift 1.5 Qwen3.8-27B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 10, "edge": "gpt-oss-20b", "gpt-oss-20b": 90, "key": "reach", "name": "Reach", "swift-1-5-qwen3-8-27b": 80, "weight": 25 }, { "by": 35, "edge": "gpt-oss-20b", "gpt-oss-20b": 85, "key": "risk", "name": "Risk", "swift-1-5-qwen3-8-27b": 50, "weight": 20 }, { "by": 30, "edge": "gpt-oss-20b", "gpt-oss-20b": 90, "key": "interface", "name": "Agent interface", "swift-1-5-qwen3-8-27b": 60, "weight": 10 }, { "by": 1, "edge": "swift-1-5-qwen3-8-27b", "gpt-oss-20b": 42, "key": "upkeep", "name": "Documentation and upkeep", "swift-1-5-qwen3-8-27b": 43, "weight": 10 } ], "summary": "gpt-oss-20b scores 69.9 (B) on agent readiness against Swift 1.5 Qwen3.8-27B's 61 (C), and leads in 3 of 4 scored categories. 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.", "swift-1-5-qwen3-8-27b": "A Qwen3.8-27B cousin post-trained to think in fewer tokens, for coding and agent work with images in, that fits a 24 GB machine with UkisAI's own 4-bit builds. Commercial use is free only below US$1M revenue. The main risks are that nobody at UkisAI answers its discussions, and that the shorter thinking rests on UkisAI's own benchmarks." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-swift-1-5-qwen3-8-27b", "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-swift-1-5-qwen3-8-27b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-swift-1-5-qwen3-8-27b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-swift-1-5-qwen3-8-27b.min.md" }, "markdown": "gpt-oss-20b scores 69.9 (B) on agent readiness against Swift 1.5 Qwen3.8-27B's 61 (C), and leads in 3 of 4 scored categories. 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- Swift 1.5 Qwen3.8-27B: grade C, 61/100, rank #49 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/swift-1-5-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- Reach, 90 against 80\n- Risk, 85 against 50\n- Agent interface, 90 against 60\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.5 against 2.3 out of 5\n\nWatch for: No reply from OpenAI to any discussion since August 2025, and template reports wait unanswered\n\n### Swift 1.5 Qwen3.8-27B (C)\n\nGood for: Agents on a 24 to 32 GB GPU or Mac that want Qwen3.8-27B's skills with shorter thinking, for teams under US$1M revenue or research.\n\nWatch for: Commercial use above US$1M gross annual revenue needs a separate licence\n\n\n## Score by category\n\n| Category | Weight | gpt-oss-20b | Swift 1.5 Qwen3.8-27B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 80 | gpt-oss-20b +10 |\n| Risk | 20% (20 this run) | 85 | 50 | gpt-oss-20b +35 |\n| Agent interface | 10% (10 this run) | 90 | 60 | gpt-oss-20b +30 |\n| Documentation and upkeep | 10% (10 this run) | 42 | 43 | Swift 1.5 Qwen3.8-27B +1 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **69.9 · B** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | gpt-oss-20b | Swift 1.5 Qwen3.8-27B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | OpenAI | UkisAI |\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 | Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue with a separate Swift Enterprise License above it. Built on Qwen3.8-27B, whose Apache 2.0 still governs the base |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2025-08-26 | 2026-09-24 |\n| Terms last updated | 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) | 2.3/5 (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**Swift 1.5 Qwen3.8-27B.** A Qwen3.8-27B cousin post-trained to think in fewer tokens, for coding and agent work with images in, that fits a 24 GB machine with UkisAI's own 4-bit builds. Commercial use is free only below US$1M revenue. The main risks are that nobody at UkisAI answers its discussions, and that the shorter thinking rests on UkisAI's own benchmarks.\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### Swift 1.5 Qwen3.8-27B\n\n1. Quickest start is `llama-server -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M --jinja`, about 18.4 GB with the vision projector\n2. On our reference machine BF16 fits, `vllm serve ukisai/Swift-1.5-Qwen3.8-27b --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`\n3. The template is Qwen3.8-27B's, so `chat_template_kwargs` with `reasoning_effort` low, medium or xhigh (default) and `enable_thinking` false work as on the base\n4. UkisAI benchmarked it at temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 0, which is a reasonable starting point\n5. The MLX 4-bit build needs UkisAI's patched MLX-LM and has no image or video chat\n6. Check the licence before commercial use, it stops at US$1M gross annual revenue including affiliates\n7. MTP self-speculative decoding is available with the base's head, flags on the card\n\n## Questions\n\n### Which is better for AI agents, gpt-oss-20b or Swift 1.5 Qwen3.8-27B?\n\ngpt-oss-20b scores 69.9 (B) on agent readiness against Swift 1.5 Qwen3.8-27B's 61 (C), and leads in 3 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gpt-oss-20b-vs-swift-1-5-qwen3-8-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gpt-oss-20b-vs-swift-1-5-qwen3-8-27b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gpt-oss-20b\", \"b\": \"swift-1-5-qwen3-8-27b\"}`. From a terminal: `anchor compare gpt-oss-20b swift-1-5-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/swift-1-5-qwen3-8-27b.json\n\n## Other comparisons with gpt-oss-20b or Swift 1.5 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 Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-swift-1-5-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 Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-swift-1-5-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 Swift 1.5 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"url": "https://www.anchorterminal.com/compare/" }, { "name": "gpt-oss-20b vs Swift 1.5 Qwen3.8-27B", "url": "" } ], "description": "gpt-oss-20b scores 69.9 (B) to Swift 1.5 Qwen3.8-27B's 61 (C) for open-weight models you run yourself. 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