{ "data": { "a": { "slug": "arete-27b", "name": "Arete-27B", "vendor": "Nous Research", "vendorUrl": "https://nousresearch.com", "kind": "weights", "category": "open-weights", "summary": "Arete-27B is Nous Research's fine-tune of Qwen3.8-27B, trained on 28,886 published examples so that its answers on politically sensitive topics stop following the Chinese state's account, with the base's tools, images and 262K context, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/arete-27b", "markdownUrl": "https://www.anchorterminal.com/tools/arete-27b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/arete-27b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/arete-27b.json", "dns": "arete-27b.grade.anchorterminal.com", "repo": "https://huggingface.co/NousResearch/Arete-27B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "NousResearch/Arete-27B" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, in the card's metadata and as a LICENSE file in the repository. Running it yourself costs the hardware, 54.7 GB for the BF16 files, or 18.1 GB for bartowski's Q4_K_M GGUF with its projector (checked 2026-11-10).", "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 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://huggingface.co/NousResearch/Arete-27B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "fine-tune", "qwen", "multimodal", "vision", "reasoning", "tool-calling", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-11-06", "graded": true, "anchor": { "graded": true, "score": 55.1, "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": 1518, "categoryRank": 92, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 60, "reach": 50, "risk": 55, "upkeep": 63 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.", "bestFor": "Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine", "strengths": [ "The training pairs and the evaluation set are both published", "Apache 2.0, ungated, safetensors only, no remote code", "The base's architecture, so vLLM, SGLang and Transformers run it from their releases", "Fits a 128 GB machine in BF16, and 24 GB from bartowski's 4-bit GGUF", "Nous Research reports no meaningful loss against the base on its agent benchmarks" ], "weaknesses": [ "No quantised build from Nous Research, so the GGUF route depends on a third party", "The card doesn't document tool calling, structured output or how to switch thinking", "No limitations section, safety results or security contact", "None of the 13 discussions has a reply from Nous Research", "The results are Nous Research's own evaluation and nobody else has rerun it yet" ], "agentNotes": [ "Start it with `llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja`, a third party's conversion, or serve the BF16 files with vLLM", "Use the base's parsers in vLLM, `--reasoning-parser qwen3 --tool-call-parser qwen3_coder`, and check tool calls first, since the template differs slightly from Qwen3.8-27B's", "Thinking switches with `enable_thinking` in the template. The card's own evaluation ran with it off", "Expect answers on politically sensitive topics to differ from Qwen3.8-27B's. That is what the fine-tune is for", "Validate JSON output yourself, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 55.1 } ], "editorialScores": { "interface": 60, "reach": 50, "risk": 55, "upkeep": 63 }, "provenanceScore": 61, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "vllm serve NousResearch/Arete-27B --max-model-len 262144 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or, with llama.cpp and bartowski's GGUF, a third party's conversion:\nllama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"NousResearch/Arete-27B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/arete-27b" }, "sameCompany": [ "hermes-agent" ], "weights": { "trainedBy": "Nous Research", "builtOn": "Qwen3.8-27B, declared as base_model in the card's metadata. Supervised fine-tuning with Inkling as the teacher", "params": "27.4B", "context": 262144, "goodFor": "Qwen3.8-27B for coding and agent work, text, images and video in, with its answers on politically sensitive topics retrained away from the Chinese state's account.", "runtimes": [ "vLLM", "SGLang", "Transformers", "llama.cpp" ], "builds": [ { "name": "bartowski NousResearch_Arete-27B-GGUF (Q4_K_M)", "url": "https://huggingface.co/bartowski/NousResearch_Arete-27B-GGUF", "who": "bartowski", "precision": "q4_k_m", "sizeGB": 18.13, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not Nous Research's. 17.20 GB of Q4_K_M weights plus the 0.93 GB F16 projector, from the Hub's model information on 10 November 2026. Reach doesn't count it" }, { "name": "bartowski NousResearch_Arete-27B-GGUF (Q8_0)", "url": "https://huggingface.co/bartowski/NousResearch_Arete-27B-GGUF", "who": "bartowski", "precision": "q8_0", "sizeGB": 29.6, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not Nous Research's. 28.67 GB of Q8_0 weights plus the 0.93 GB F16 projector, from the Hub's model information on 10 November 2026. Reach doesn't count it" }, { "name": "Arete-27B", "url": "https://huggingface.co/NousResearch/Arete-27B", "who": "publisher", "precision": "bf16", "sizeGB": 54.7, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ], "note": "The only build Nous Research publishes" } ], "run": "llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja", "runBuild": "bartowski NousResearch_Arete-27B-GGUF (Q4_K_M)", "checked": "2026-11-10", "minMemoryGB": 24 }, "family": { "lab": "Nous Research (NousResearch)", "family": "Arete", "url": "https://www.anchorterminal.com/open-weights/#nous-research" }, "area": "models", "provenance": { "legalEntity": "Nous Research, Inc. (named in the footer of nousresearch.com)", "domain": "nousresearch.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/NousResearch/Arete-27B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "Nous Research, Inc. is the name in the site's footer, as the Hermes Agent listing records.", "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, eight commits on 6 November 2026, the upload and updates to the card.", "The Hermes Agent listing found no security.txt on nousresearch.com. unchecked on 10 November.", "unchecked: the domain's registration date." ], "score": 61 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/arete-27b.json" }, "answer": "Qwen3-Coder-30B-A3B-Instruct and Arete-27B score within a point of each other on agent readiness, 55.7 (C) and 55.1 (C). Arete-27B leads on documentation and upkeep.", "b": { "slug": "qwen3-coder-30b-a3b-instruct", "name": "Qwen3-Coder-30B-A3B-Instruct", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3-Coder-30B-A3B-Instruct is Alibaba's open-weight mixture-of-experts coding model, 30.5B parameters with 3.3B active, for agentic coding and tool use, with a 262,144-token context, published in BF16 and FP8 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-coder-30b-a3b-instruct.json", "dns": "qwen3-coder-30b-a3b-instruct.grade.anchorterminal.com", "repo": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3-Coder-30B-A3B-Instruct" }, { "registry": "huggingface", "name": "Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8" }, { "registry": "ollama", "name": "qwen3-coder:30b" } ], "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. Running it yourself costs the hardware, 61.1 GB for the BF16 files, 31.2 GB for Alibaba's FP8 build and 19 GB for Ollama's 4-bit 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-Coder-30B-A3B-Instruct", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "coding", "tool-calling", "long-context", "apache-2.0", "safetensors", "fp8", "self-hosted" ], "lastRelease": "2025-12-03", "graded": true, "anchor": { "graded": true, "score": 55.7, "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": 1518, "categoryRank": 90, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 33, "interface": 75, "reach": 90, "risk": 55, "upkeep": 31 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-04" }, "negative": 0, "verdict": "A coding model with 3.3B of 30.5B parameters active per token, for agentic coding tools, repository reading and fill-in-the-middle without a thinking step. It needs about 21 to 23 GB at 4 bits under Apache 2.0, and several providers host it. It runs commands for agents with no safety results or prompt-injection guidance, and Alibaba hasn't answered its discussions.", "bestFor": "Coding agents and local coding tools that want an open model on one 24 GB GPU or a 32 GB Mac, with a plain licence.", "strengths": [ "Only 3.3B of its 30.5B parameters are active per token", "4-bit builds from Ollama and LM Studio fit 24 GB", "Its own qwen3_coder tool parser shipped in the repository and carried by released vLLM and SGLang", "Plain Apache 2.0 with no gate, no account and no use policy, and a checksum on every file", "262,144 tokens of native context and fill-in-the-middle support" ], "weaknesses": [ "No safety results, prompt-injection guidance or security contact, though it's meant to run commands", "config.json was corrected in place in December 2025 with no note, and older GGUF copies may predate it", "No reply from Alibaba in 26 discussions, and parser reports wait", "No thinking mode and no documented structured output", "Alibaba's only quantised build is FP8" ], "agentNotes": [ "Quickest start is `ollama run qwen3-coder:30b`, 19 GB", "On our reference machine use BF16 with `vllm serve Qwen/Qwen3-Coder-30B-A3B-Instruct --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, 61 GB of weights fits the GPU share", "Use the qwen3_coder parser in vLLM or SGLang, not hermes, since calls come back as function and parameter blocks rather than JSON", "Pass tool-call arguments in the history as objects, the template iterates them as a mapping", "Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and repetition_penalty 1.05, with up to 65,536 output tokens", "For code completion use the fill-in-the-middle tokens `\u003c|fim_prefix|\u003e`, `\u003c|fim_suffix|\u003e` and `\u003c|fim_middle|\u003e` from Qwen's README", "Sandbox the commands and files it touches, since nothing from Alibaba covers prompt injection" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 55.7 } ], "editorialScores": { "capability": 33, "interface": 75, "reach": 90, "risk": 55, "upkeep": 31 }, "provenanceScore": 54, "checked": { "categories": { "capability": "2026-11-04", "interface": "2026-11-04", "reach": "2026-11-04", "risk": "2026-11-04", "upkeep": "2026-11-04" }, "last": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run qwen3-coder:30b\n# or, with vLLM:\nvllm serve Qwen/Qwen3-Coder-30B-A3B-Instruct --max-model-len 262144 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"qwen3-coder:30b\", \"messages\": [{\"role\": \"user\", \"content\": \"Write a quick sort in Go.\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen3-coder-30b-a3b-instruct" }, "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", "qwen-agentworld-35b-a3b", "qwen3-30b-a3b-instruct-2507", "qwen3-4b-instruct-2507", "qwen3-coder-next", "qwen3-omni-30b-a3b-instruct", "qwen3-vl-2b-instruct", "qwen3-vl-30b-a3b-instruct", "qwen3-vl-32b-instruct", "qwen3-vl-4b-instruct", "qwen3-vl-8b-instruct", "qwen3-5-0-8b", "qwen3-5-122b-a10b", "qwen3-5-2b", "qwen3-5-397b-a17b", "qwen3-5-4b", "qwen3-5-9b", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "params": "30.5B (3.3B active)", "context": 262144, "goodFor": "Mixture-of-experts coding model with 3.3B of 30.5B parameters active per token, for agentic coding tools, repository-scale code reading, tool calls and fill-in-the-middle, with no thinking step.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "ollama qwen3-coder:30b", "url": "https://ollama.com/library/qwen3-coder:30b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 19, "memoryGB": 24, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Digest 06c1097efce0, also tagged latest and 30b-a3b-q4_K_M. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB" }, { "name": "LM Studio qwen/qwen3-coder-30b (Q4_K_M GGUF)", "url": "https://huggingface.co/lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-GGUF", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 18.63, "memoryGB": 24, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "LM Studio's catalogue entry qwen/qwen3-coder-30b draws on this repository (Q3_K_L, Q4_K_M, Q6_K and Q8_0) and on MLX builds at 4 to 8 bits. The MLX 4-bit repository is 17.19 GB, also in the 24 GB band" }, { "name": "Qwen3-Coder-30B-A3B-Instruct-FP8", "url": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 31.2, "memoryGB": 48, "format": "safetensors", "note": "Block-wise FP8 with 128-wide blocks, per its card" }, { "name": "Qwen3-Coder-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct", "who": "publisher", "precision": "bf16", "sizeGB": 61.08, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve Qwen/Qwen3-Coder-30B-A3B-Instruct --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "Qwen3-Coder-30B-A3B-Instruct", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "Alibaba (Qwen)", "family": "Qwen3", "url": "https://www.anchorterminal.com/open-weights/#qwen", "others": [ { "name": "Qwen3-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-0.6B", "params": "0.6B" }, { "name": "Qwen3-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-1.7B", "params": "1.7B" }, { "name": "Qwen3-14B", "url": "https://huggingface.co/Qwen/Qwen3-14B", "params": "14B" }, { "name": "Qwen3-235B-A22B", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B", "params": "30B-A3B" }, { "name": "Qwen3-32B", "url": "https://huggingface.co/Qwen/Qwen3-32B", "params": "32B" }, { "name": "Qwen3-4B", "url": "https://huggingface.co/Qwen/Qwen3-4B", "params": "4B" }, { "name": "Qwen3-8B", "url": "https://huggingface.co/Qwen/Qwen3-8B", "params": "8B" }, { "name": "Qwen3-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-14B-Base", "url": "https://huggingface.co/Qwen/Qwen3-14B-Base", "params": "14B" }, { "name": "Qwen3-30B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Base", "params": "30B-A3B" }, { "name": "Qwen3-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3-4B-Base", "params": "4B" }, { "name": "Qwen3-8B-Base", "url": "https://huggingface.co/Qwen/Qwen3-8B-Base", "params": "8B" }, { "name": "Qwen3-Reranker-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-0.6B", "params": "0.6B" }, { "name": "Qwen3-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-8B", "params": "8B" }, { "name": "Qwen3-Embedding-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-0-6b" }, { "name": "Qwen3-Embedding-4B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-4b" }, { "name": "Qwen3-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-8b" }, { "name": "Qwen3-Reranker-4B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-4B", "params": "4B" }, { "name": "Qwen3-235B-A22B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507", "params": "235B-A22B" }, { "name": "Qwen3-Coder-480B-A35B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct", "params": "480B-A35B" }, { "name": "Qwen3-235B-A22B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-30b-a3b-instruct-2507" }, { "name": "Qwen3-30B-A3B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507", "params": "30B-A3B" }, { "name": "Qwen3-4B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507" }, { "name": "Qwen3-4B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507", "params": "4B" }, { "name": "Qwen3-Next-80B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct", "params": "80B-A3B" }, { "name": "Qwen3-Next-80B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking", "params": "80B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Captioner", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Captioner", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-omni-30b-a3b-instruct" }, { "name": "Qwen3-VL-235B-A22B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct", "params": "235B-A22B" }, { "name": "Qwen3-VL-235B-A22B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Thinking", "params": "235B-A22B" }, { "name": "Qwen3Guard-Gen-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Gen-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-4B", "params": "4B" }, { "name": "Qwen3Guard-Gen-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-8B", "params": "8B" }, { "name": "Qwen3Guard-Stream-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Stream-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-4B", "params": "4B" }, { "name": "Qwen3Guard-Stream-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-8B", "params": "8B" }, { "name": "Qwen3-4B-SafeRL", "url": "https://huggingface.co/Qwen/Qwen3-4B-SafeRL", "params": "4B" }, { "name": "Qwen3-VL-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct" }, { "name": "Qwen3-VL-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-VL-4B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct" }, { "name": "Qwen3-VL-4B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Thinking", "params": "4B" }, { "name": "Qwen3-VL-8B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct" }, { "name": "Qwen3-VL-8B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking", "params": "8B" }, { "name": "Qwen3-VL-2B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-2b-instruct" }, { "name": "Qwen3-VL-2B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Thinking", "params": "2B" }, { "name": "Qwen3-VL-32B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct", "params": "32B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct" }, { "name": "Qwen3-VL-32B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Thinking", "params": "32B" }, { "name": "Qwen3-VL-Embedding-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-2b" }, { "name": "Qwen3-VL-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-8b" }, { "name": "Qwen3-VL-Reranker-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-2B", "params": "2B" }, { "name": "Qwen3-VL-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-8B", "params": "8B" }, { "name": "Qwen3-TTS-12Hz-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-0.6B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-TTS-12Hz-1.7B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-tts-1-7b-customvoice" }, { "name": "Qwen3-TTS-12Hz-1.7B-VoiceDesign", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign", "params": "1.7B" }, { "name": "Qwen3-TTS-Tokenizer-12Hz", "url": "https://huggingface.co/Qwen/Qwen3-TTS-Tokenizer-12Hz", "params": "not in name" }, { "name": "Qwen3-ASR-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-0-6b" }, { "name": "Qwen3-ASR-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-1.7B", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-1-7b" }, { "name": "Qwen3-ForcedAligner-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ForcedAligner-0.6B", "params": "0.6B" }, { "name": "Qwen3-Coder-Next", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next", "params": "not in name; Hub safetensors total 79.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-next" }, { "name": "Qwen3-Coder-Next-Base", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next-Base", "params": "not in name; Hub safetensors total 79.7B" }, { "name": "WebWorld-14B", "url": "https://huggingface.co/Qwen/WebWorld-14B", "params": "14B" }, { "name": "WebWorld-32B", "url": "https://huggingface.co/Qwen/WebWorld-32B", "params": "32B" }, { "name": "WebWorld-8B", "url": "https://huggingface.co/Qwen/WebWorld-8B", "params": "8B" }, { "name": "Qwen3.5-397B-A17B", "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B", "params": "397B-A17B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b" }, { "name": "Qwen3.5-122B-A10B", "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B", "params": "122B-A10B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b" }, { "name": "Qwen3.5-27B", "url": "https://huggingface.co/Qwen/Qwen3.5-27B", "params": "27B" }, { "name": "Qwen3.5-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.5-35B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B-Base", "params": "35B-A3B" }, { "name": "Qwen3.5-9B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-9B-Base", "params": "9B" }, { "name": "Qwen3.5-4B", "url": "https://huggingface.co/Qwen/Qwen3.5-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-4b" }, { "name": "Qwen3.5-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-4B-Base", "params": "4B" }, { "name": "Qwen3.5-9B", "url": "https://huggingface.co/Qwen/Qwen3.5-9B", "params": "9B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-9b" }, { "name": "Qwen3.5-0.8B", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B", "params": "0.8B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-0-8b" }, { "name": "Qwen3.5-0.8B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B-Base", "params": "0.8B" }, { "name": "Qwen3.5-2B", "url": "https://huggingface.co/Qwen/Qwen3.5-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-2b" }, { "name": "Qwen3.5-2B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-2B-Base", "params": "2B" }, { "name": "Qwen3.6-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B", "params": "35B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b" }, { "name": "Qwen3.6-27B", "url": "https://huggingface.co/Qwen/Qwen3.6-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-27b" }, { "name": "Qwen-AgentWorld-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.8-27B", "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 2024 Alibaba Cloud. The registration date of qwen.ai is carried from the 20 October check for qwen3-8-27b.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog. Commit titles name the template, parser and config updates of 7 and 21 August and 3 December 2025, without saying what changed.", "qwen.ai/.well-known/security.txt redirects to the home page, so there is no security.txt." ], "score": 54 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct.json", "live": { "slug": "qwen3-coder-30b-a3b-instruct", "securityTxt": { "url": "https://qwen.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:09:18.413964975Z" }, "domain": { "domain": "qwen.ai", "registered": "2023-08-27", "source": "https://rdap.identitydigital.services/rdap/domain/qwen.ai", "checkedAt": "2026-11-08T15:07:12.468678256Z" }, "updatedAt": "2026-11-10T16:09:18.413964975Z" } }, "edges": [ { "by": 40, "category": "reach", "leads": "qwen3-coder-30b-a3b-instruct", "name": "Reach", "reasons": { "a": "Memory 10, the only build from Nous Research is the 54.7 GB BF16 release, which needs about 66 GB and fits the reference machine. Runtimes 15, vLLM, SGLang and Transformers run the base's architecture from those files, and llama.cpp isn't credited because Nous Research publishes no GGUF. Builds 0, no quantised build of its own. Terms 15, Apache 2.0. Download 10, not gated. Hosted 0, no provider. 10 + 15 + 0 + 15 + 10 + 0 = 50.", "b": "Memory 25 of 30, LM Studio's MLX 4-bit build is 17.2 GB and Ollama's 4-bit build 19 GB, 21 to 23 GB with a fifth added, which fits 24 GB. Runtimes 25, released Transformers, vLLM, SGLang and llama.cpp run the qwen3_moe architecture, vLLM and SGLang carry a qwen3_coder parser, Ollama's released build pins a llama.cpp that has qwen3moe, and LM Studio's catalogue carries it. Builds 10 of 15, Alibaba's only quantised build is FP8. Terms 15, Apache 2.0 with nothing added. Download 10, neither repository is gated. Hosted 5, Featherless AI on Hugging Face (live on 4 November 2026) plus SiliconFlow and Amazon Bedrock on OpenRouter (per 21 October, not re-read) serve it. 25 + 25 + 10 + 15 + 10 + 5 = 90." }, "scores": { "a": 50, "b": 90 } }, { "by": 32, "category": "upkeep", "leads": "arete-27b", "name": "Documentation and upkeep", "reasons": { "a": "Model card 8, no limitations, context length, languages or cut-off. Training data 20, the training pairs and the evaluation set are published with their counts. Last change 20, a commit on 6 November 2026. Responsiveness 0, none of 13 discussions has a reply from Nous Research. Version policy 5, a first release. File health 10, the files agree. 8 + 20 + 20 + 0 + 5 + 10 = 63.", "b": "Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, languages and a knowledge cut-off aren't. Training data 5, the blog describes the mix in general terms without a cut-off. Last change 5, config.json was corrected on 3 December 2025, 336 days before the 4 November 2026 check. Responsiveness 0, counted by script on 4 November, none of 26 discussions has a reply from Alibaba's Qwen staff, and the one Hub-flagged member who replied is the independent quantiser bartowski. Version policy 5, a first release. File health 5, the files now agree with the card, but reports about the parser and an attention error wait without a reply. 11 + 5 + 5 + 0 + 5 + 5 = 31." }, "scores": { "a": 63, "b": 31 } }, { "by": 15, "category": "interface", "leads": "qwen3-coder-30b-a3b-instruct", "name": "Agent interface", "reasons": { "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 10, only the template says it. Trained tool calling 20, several calls a turn and agentic work as an intended use. Structured output 0 and reasoning control 0, neither is documented on the card. Context 10, 262,144 tokens native. 20 + 10 + 20 + 0 + 0 + 10 = 60.", "b": "Chat template 20, chat_template.jinja in the repository. Tool-calling format 25, the card shows how tools are declared, the README names the parser for SGLang and vLLM, and the repository ships it. Trained tool calling 20, the template renders several calls a turn and the training used agent reinforcement learning. Structured output 0, not documented. Reasoning control 0, the model has no thinking. Context 10, 262,144 tokens native, with the YaRN extension not counted. Template, parser and config unchanged on 4 November. 20 + 25 + 20 + 0 + 0 + 10 = 75." }, "scores": { "a": 60, "b": 75 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Nous Research", "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": "2026-11-06", "b": "2025-12-03", "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": "none", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Qwen3-Coder-30B-A3B-Instruct and Arete-27B score within a point of each other on agent readiness, 55.7 (C) and 55.1 (C). Arete-27B leads on documentation and upkeep.", "question": "Which is better for AI agents, Arete-27B or Qwen3-Coder-30B-A3B-Instruct?" } ], "goodFor": [ { "aheadOn": [ "Documentation and upkeep, 63 against 31" ], "also": null, "goodFor": "Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine", "slug": "arete-27b", "watchFor": "No quantised build from Nous Research, so the GGUF route depends on a third party" }, { "aheadOn": [ "Reach, 90 against 50", "Agent interface, 75 against 60" ], "also": null, "goodFor": "Coding agents and local coding tools that want an open model on one 24 GB GPU or a 32 GB Mac, with a plain licence.", "slug": "qwen3-coder-30b-a3b-instruct", "watchFor": "No safety results, prompt-injection guidance or security contact, though it's meant to run commands" } ], "history": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-arete-27b.json", "title": "Agents-A1-4B vs Arete-27B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-arete-27b" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Agents-A1-4B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-arete-27b.json", "title": "Apertus v1.5 8B vs Arete-27B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-arete-27b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Apertus v1.5 8B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": 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{ "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Nemotron 3 Super 120B A12B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-coder-30b-a3b-instruct.json", "title": "North Mini Code 1.0 vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-coder-30b-a3b-instruct.json", "title": "occamy-1.0 vs 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"https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-2b-instruct.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-recursive-flint.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-sensenova-loop-9b-preview.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-smollm3-3b.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-spark-x2-6-4b.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-step-3-7-flash.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-step-3-7-flash" }, { "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-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-unlimited-ocr-2.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-wevisone-vl-2b.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-wevisone-vl-4b.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-zaya1-8b.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "arete-27b": 50, "by": 40, "edge": "qwen3-coder-30b-a3b-instruct", "key": "reach", "name": "Reach", "qwen3-coder-30b-a3b-instruct": 90, "weight": 25 }, { "arete-27b": 55, "by": 0, "edge": "", "key": "risk", "name": "Risk", "qwen3-coder-30b-a3b-instruct": 55, "weight": 20 }, { "arete-27b": 60, "by": 15, "edge": "qwen3-coder-30b-a3b-instruct", "key": "interface", "name": "Agent interface", "qwen3-coder-30b-a3b-instruct": 75, "weight": 10 }, { "arete-27b": 63, "by": 32, "edge": "arete-27b", "key": "upkeep", "name": "Documentation and upkeep", "qwen3-coder-30b-a3b-instruct": 31, "weight": 10 } ], "summary": "Qwen3-Coder-30B-A3B-Instruct and Arete-27B score within a point of each other on agent readiness, 55.7 (C) and 55.1 (C). Arete-27B leads on documentation and upkeep. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "arete-27b": "Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.", "qwen3-coder-30b-a3b-instruct": "A coding model with 3.3B of 30.5B parameters active per token, for agentic coding tools, repository reading and fill-in-the-middle without a thinking step. It needs about 21 to 23 GB at 4 bits under Apache 2.0, and several providers host it. It runs commands for agents with no safety results or prompt-injection guidance, and Alibaba hasn't answered its discussions." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-coder-30b-a3b-instruct", "json": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-coder-30b-a3b-instruct.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-coder-30b-a3b-instruct.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-coder-30b-a3b-instruct.min.md" }, "markdown": "Qwen3-Coder-30B-A3B-Instruct and Arete-27B score within a point of each other on agent readiness, 55.7 (C) and 55.1 (C). Arete-27B leads on documentation and upkeep. Both do open-weight models you run yourself.\n\n- Arete-27B: grade C, 55.1/100, rank #123 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/arete-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/arete-27b.json\n- Qwen3-Coder-30B-A3B-Instruct: grade C, 55.7/100, rank #119 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-coder-30b-a3b-instruct.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 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Arete-27B (C)\n\nGood for: Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine\n\nAhead on:\n- Documentation and upkeep, 63 against 31\n\nWatch for: No quantised build from Nous Research, so the GGUF route depends on a third party\n\n### Qwen3-Coder-30B-A3B-Instruct (C)\n\nGood for: Coding agents and local coding tools that want an open model on one 24 GB GPU or a 32 GB Mac, with a plain licence.\n\nAhead on:\n- Reach, 90 against 50\n- Agent interface, 75 against 60\n\nWatch for: No safety results, prompt-injection guidance or security contact, though it's meant to run commands\n\n\n## Why the edge\n\n### Reach: Qwen3-Coder-30B-A3B-Instruct +40\n\n- Arete-27B (50): Memory 10, the only build from Nous Research is the 54.7 GB BF16 release, which needs about 66 GB and fits the reference machine. Runtimes 15, vLLM, SGLang and Transformers run the base's architecture from those files, and llama.cpp isn't credited because Nous Research publishes no GGUF. Builds 0, no quantised build of its own. Terms 15, Apache 2.0. Download 10, not gated. Hosted 0, no provider. 10 + 15 + 0 + 15 + 10 + 0 = 50.\n- Qwen3-Coder-30B-A3B-Instruct (90): Memory 25 of 30, LM Studio's MLX 4-bit build is 17.2 GB and Ollama's 4-bit build 19 GB, 21 to 23 GB with a fifth added, which fits 24 GB. Runtimes 25, released Transformers, vLLM, SGLang and llama.cpp run the qwen3_moe architecture, vLLM and SGLang carry a qwen3_coder parser, Ollama's released build pins a llama.cpp that has qwen3moe, and LM Studio's catalogue carries it. Builds 10 of 15, Alibaba's only quantised build is FP8. Terms 15, Apache 2.0 with nothing added. Download 10, neither repository is gated. Hosted 5, Featherless AI on Hugging Face (live on 4 November 2026) plus SiliconFlow and Amazon Bedrock on OpenRouter (per 21 October, not re-read) serve it. 25 + 25 + 10 + 15 + 10 + 5 = 90.\n\n### Documentation and upkeep: Arete-27B +32\n\n- Arete-27B (63): Model card 8, no limitations, context length, languages or cut-off. Training data 20, the training pairs and the evaluation set are published with their counts. Last change 20, a commit on 6 November 2026. Responsiveness 0, none of 13 discussions has a reply from Nous Research. Version policy 5, a first release. File health 10, the files agree. 8 + 20 + 20 + 0 + 5 + 10 = 63.\n- Qwen3-Coder-30B-A3B-Instruct (31): Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, languages and a knowledge cut-off aren't. Training data 5, the blog describes the mix in general terms without a cut-off. Last change 5, config.json was corrected on 3 December 2025, 336 days before the 4 November 2026 check. Responsiveness 0, counted by script on 4 November, none of 26 discussions has a reply from Alibaba's Qwen staff, and the one Hub-flagged member who replied is the independent quantiser bartowski. Version policy 5, a first release. File health 5, the files now agree with the card, but reports about the parser and an attention error wait without a reply. 11 + 5 + 5 + 0 + 5 + 5 = 31.\n\n### Agent interface: Qwen3-Coder-30B-A3B-Instruct +15\n\n- Arete-27B (60): Chat template 20, a Jinja file in the repository. Tool-calling format 10, only the template says it. Trained tool calling 20, several calls a turn and agentic work as an intended use. Structured output 0 and reasoning control 0, neither is documented on the card. Context 10, 262,144 tokens native. 20 + 10 + 20 + 0 + 0 + 10 = 60.\n- Qwen3-Coder-30B-A3B-Instruct (75): Chat template 20, chat_template.jinja in the repository. Tool-calling format 25, the card shows how tools are declared, the README names the parser for SGLang and vLLM, and the repository ships it. Trained tool calling 20, the template renders several calls a turn and the training used agent reinforcement learning. Structured output 0, not documented. Reasoning control 0, the model has no thinking. Context 10, 262,144 tokens native, with the YaRN extension not counted. Template, parser and config unchanged on 4 November. 20 + 25 + 20 + 0 + 0 + 10 = 75.\n\n## Score by category\n\n| Category | Weight | Arete-27B | Qwen3-Coder-30B-A3B-Instruct | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 50 | 90 | Qwen3-Coder-30B-A3B-Instruct +40 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 60 | 75 | Qwen3-Coder-30B-A3B-Instruct +15 |\n| Documentation and upkeep | 10% (10 this run) | 63 | 31 | Arete-27B +32 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **55.1 · C** | **55.7 · C** | |\n\n## Facts side by side\n\n| Fact | Arete-27B | Qwen3-Coder-30B-A3B-Instruct |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Nous Research | 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 | 2026-11-06 | 2025-12-03 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 2.8/5 (4) |\n\n## Verdicts\n\n**Arete-27B.** Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.\n\n**Qwen3-Coder-30B-A3B-Instruct.** A coding model with 3.3B of 30.5B parameters active per token, for agentic coding tools, repository reading and fill-in-the-middle without a thinking step. It needs about 21 to 23 GB at 4 bits under Apache 2.0, and several providers host it. It runs commands for agents with no safety results or prompt-injection guidance, and Alibaba hasn't answered its discussions.\n\n## Before you call either\n\n### Arete-27B\n\n1. Start it with `llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja`, a third party's conversion, or serve the BF16 files with vLLM\n2. Use the base's parsers in vLLM, `--reasoning-parser qwen3 --tool-call-parser qwen3_coder`, and check tool calls first, since the template differs slightly from Qwen3.8-27B's\n3. Thinking switches with `enable_thinking` in the template. The card's own evaluation ran with it off\n4. Expect answers on politically sensitive topics to differ from Qwen3.8-27B's. That is what the fine-tune is for\n5. Validate JSON output yourself, since structured output isn't documented\n\n### Qwen3-Coder-30B-A3B-Instruct\n\n1. Quickest start is `ollama run qwen3-coder:30b`, 19 GB\n2. On our reference machine use BF16 with `vllm serve Qwen/Qwen3-Coder-30B-A3B-Instruct --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, 61 GB of weights fits the GPU share\n3. Use the qwen3_coder parser in vLLM or SGLang, not hermes, since calls come back as function and parameter blocks rather than JSON\n4. Pass tool-call arguments in the history as objects, the template iterates them as a mapping\n5. Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and repetition_penalty 1.05, with up to 65,536 output tokens\n6. For code completion use the fill-in-the-middle tokens `\u003c|fim_prefix|\u003e`, `\u003c|fim_suffix|\u003e` and `\u003c|fim_middle|\u003e` from Qwen's README\n7. Sandbox the commands and files it touches, since nothing from Alibaba covers prompt injection\n\n## Questions\n\n### Which is better for AI agents, Arete-27B or Qwen3-Coder-30B-A3B-Instruct?\n\nQwen3-Coder-30B-A3B-Instruct and Arete-27B score within a point of each other on agent readiness, 55.7 (C) and 55.1 (C). Arete-27B leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-coder-30b-a3b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-coder-30b-a3b-instruct.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"arete-27b\", \"b\": \"qwen3-coder-30b-a3b-instruct\"}`. From a terminal: `anchor compare arete-27b qwen3-coder-30b-a3b-instruct`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/arete-27b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-coder-30b-a3b-instruct.json\n\n## Other comparisons with Arete-27B or Qwen3-Coder-30B-A3B-Instruct\n\n- [Agents-A1-4B vs Arete-27B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-arete-27b.md)\n- [Agents-A1-4B vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Apertus v1.5 8B vs Arete-27B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-arete-27b.md)\n- [Apertus v1.5 8B vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Apodex-1.1-mini vs Arete-27B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-arete-27b.md)\n- [Apodex-1.1-mini vs 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