{ "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": "SmolLM3-3B scores 61 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in every scored category.", "b": { "slug": "smollm3-3b", "name": "SmolLM3-3B", "vendor": "Hugging Face", "vendorUrl": "https://huggingface.co", "kind": "weights", "category": "open-weights", "summary": "SmolLM3-3B is Hugging Face's open-weight dense 3B instruct model with switchable reasoning, tool calling and a 65,536-token native context, published in BF16 safetensors under Apache 2.0 with its training data and recipes.", "url": "https://www.anchorterminal.com/tools/smollm3-3b", "markdownUrl": "https://www.anchorterminal.com/tools/smollm3-3b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/smollm3-3b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/smollm3-3b.json", "dns": "smollm3-3b.grade.anchorterminal.com", "repo": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "HuggingFaceTB/SmolLM3-3B" }, { "registry": "huggingface", "name": "HuggingFaceTB/SmolLM3-3B-ONNX" } ], "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. Running it yourself costs the hardware, 6.15 GB for the BF16 files (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": "2026-11-03" }, "docsUrl": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "small", "reasoning", "tool-calling", "multilingual", "apache-2.0", "safetensors", "onnx", "self-hosted" ], "lastRelease": "2025-09-10", "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": 1518, "categoryRank": 45, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 80, "reach": 85, "risk": 65, "upkeep": 74 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-03" }, "negative": 0, "verdict": "A tiny, fully open cousin with documented tool calling and switchable thinking, for local subtasks and routing on laptops and CPUs. It needs about 8 GB in BF16, or under 3 GB in Hugging Face's 4-bit ONNX build. The main risks are a 64K native context, no safety results and no staff reply in its discussions since November 2025.", "bestFor": "Agents that want a small, fully transparent tool-calling cousin on a laptop, CPU or edge device", "strengths": [ "Training data, mixture weights, configs and intermediate checkpoints all published", "Plain Apache 2.0, no gate, no account", "About 7.4 GB in BF16, with Hugging Face's own ONNX builds down to 4-bit for ONNX Runtime", "Tool-call format and vLLM parser documented on the card", "Thinking turns off per request", "Safetensors only, with a SHA-256 for each shard and a Jinja template" ], "weaknesses": [ "65,536 tokens trained; 131,072 needs a YaRN edit to config.json", "The main repository hasn't changed since 10 September 2025, and no staff reply in its discussions since November 2025", "No safety evaluation or agent-use guidance", "No structured-output documentation", "No Ollama or LM Studio catalogue entry and no hosted provider", "The chat template renders tool results as user turns and drops tool_calls from earlier assistant messages" ], "agentNotes": [ "Start it with `vllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes`, or tool calls come back as text", "Pass `chat_template_kwargs` with `enable_thinking` false for short tasks, since thinking is on by default; a `/think` or `/no_think` flag in the system prompt overrides the kwarg", "Put earlier tool calls in the assistant message content, since the template ignores a `tool_calls` field when it renders history", "Add the YaRN `rope_scaling` block (factor 2.0, original 65536) to config.json before sending more than 65,536 tokens", "Use temperature 0.6 and top_p 0.95, the card's recommended sampling", "Needs Transformers v4.53.0 or later; for llama.cpp use a GGUF from ggml-org or another converter, since Hugging Face publishes none", "Validate JSON output yourself or use the runtime's guided decoding" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.3, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 80, "reach": 85, "risk": 65, "upkeep": 74 }, "provenanceScore": 72, "checked": { "categories": { "interface": "2026-11-03", "reach": "2026-11-03", "risk": "2026-11-03", "upkeep": "2026-11-03" }, "last": "2026-11-03", "lastMoved": "2026-11-03", "oldest": "2026-11-03", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install -U transformers vllm\nvllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes\n# or, with llama.cpp and llama.cpp's own GGUF:\nllama-server -hf ggml-org/SmolLM3-3B-GGUF:Q4_K_M --jinja", "http": "curl http://localhost:8000/v1/chat/completions -H \"Content-Type: application/json\" -d '{\n \"model\": \"HuggingFaceTB/SmolLM3-3B\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Give me a brief explanation of gravity in simple terms.\"}],\n \"temperature\": 0.6,\n \"top_p\": 0.95,\n \"max_tokens\": 16384,\n \"chat_template_kwargs\": {\"enable_thinking\": false}\n}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/smollm3-3b" }, "sameCompany": [ "hugging-face-inference-providers", "hugging-face-inference-endpoints", "smolagents", "carbon-x" ], "weights": { "trainedBy": "Hugging Face", "builtOn": "HuggingFaceTB/SmolLM3-3B-Base", "params": "3.08B", "context": 65536, "goodFor": "Small fully open instruct model with switchable reasoning and tool calling, text only, multilingual.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "SmolLM3-3B", "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B", "who": "publisher", "precision": "bf16", "sizeGB": 6.2, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] }, { "name": "SmolLM3-3B-ONNX fp16", "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B-ONNX/blob/main/onnx/model_fp16.onnx_data", "who": "publisher", "precision": "fp16", "sizeGB": 6.2, "memoryGB": 8, "format": "onnx", "note": "ONNX for Transformers.js and ONNX Runtime, none of the runtimes listed here" }, { "name": "SmolLM3-3B-ONNX int8", "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B-ONNX/blob/main/onnx/model_int8.onnx_data", "who": "publisher", "precision": "int8", "sizeGB": 3.1, "memoryGB": 8, "format": "onnx", "note": "ONNX for Transformers.js and ONNX Runtime, none of the runtimes listed here" }, { "name": "SmolLM3-3B-ONNX uint8", "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B-ONNX/blob/main/onnx/model_uint8.onnx_data", "who": "publisher", "precision": "uint8", "sizeGB": 3.1, "memoryGB": 8, "format": "onnx", "note": "ONNX for Transformers.js and ONNX Runtime, none of the runtimes listed here" }, { "name": "SmolLM3-3B-ONNX q4", "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B-ONNX/blob/main/onnx/model_q4.onnx_data", "who": "publisher", "precision": "q4", "sizeGB": 2.8, "memoryGB": 8, "format": "onnx", "note": "ONNX for Transformers.js and ONNX Runtime, none of the runtimes listed here" }, { "name": "SmolLM3-3B-ONNX bnb4", "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B-ONNX/blob/main/onnx/model_bnb4.onnx_data", "who": "publisher", "precision": "bnb4", "sizeGB": 2.7, "memoryGB": 8, "format": "onnx", "note": "ONNX for Transformers.js and ONNX Runtime, none of the runtimes listed here" }, { "name": "SmolLM3-3B-ONNX q4f16", "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B-ONNX/blob/main/onnx/model_q4f16.onnx_data", "who": "publisher", "precision": "q4f16", "sizeGB": 2.1, "memoryGB": 8, "format": "onnx", "note": "ONNX for Transformers.js and ONNX Runtime, none of the runtimes listed here" }, { "name": "ggml-org SmolLM3-3B-GGUF (Q4_K_M)", "url": "https://huggingface.co/ggml-org/SmolLM3-3B-GGUF", "who": "llama.cpp", "precision": "q4_k_m", "sizeGB": 1.92, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From ggml-org, the llama.cpp project's organisation. 1.92 GB of Q4_K_M weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet" }, { "name": "ggml-org SmolLM3-3B-GGUF (Q8_0)", "url": "https://huggingface.co/ggml-org/SmolLM3-3B-GGUF", "who": "llama.cpp", "precision": "q8_0", "sizeGB": 3.28, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From ggml-org, the llama.cpp project's organisation. 3.28 GB of Q8_0 weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet" } ], "run": "vllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes", "runBuild": "SmolLM3-3B", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "Hugging Face (SmolLM)", "family": "SmolLM3", "url": "https://www.anchorterminal.com/open-weights/#huggingface", "others": [ { "name": "SmolLM3-3B-Base", "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B-Base", "params": "3B" }, { "name": "SmolLM3-3B-checkpoints", "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B-checkpoints", "params": "3B" }, { "name": "SmolLM3-3B-GSM8K-SFT", "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B-GSM8K-SFT", "params": "3B" } ], "olderCount": 20, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Hugging Face, Inc.", "domain": "huggingface.co", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B/commits/main", "securityTxt": "valid", "checked": "2026-10-21", "notes": [ "The model is published in HuggingFaceTB (Hugging Face Smol Models Research), the organisation Hugging Face's own launch post at huggingface.co/blog/smollm3 links to. Hugging Face's Terms of Service name Hugging Face, Inc.", "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. A few messages say what changed (an EOS token ID update on 8 July 2025, a template fix for vLLM on 9 July 2025); most read Update README.md.", "huggingface.co/.well-known/security.txt is valid, with a contact and an expiry of 1 July 2030.", "rdap.org has no RDAP service for .co domains, so the registration date wasn't checked." ], "score": 72 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/smollm3-3b.json", "live": { "slug": "smollm3-3b", "securityTxt": { "url": "https://huggingface.co/.well-known/security.txt", "state": "valid", "expires": "2030-07-01T08:42:00.000Z", "checkedAt": "2026-11-10T16:08:41.928222854Z" }, "domain": { "domain": "huggingface.co", "checkedAt": "2026-11-08T14:53:11.106908704Z" }, "pages": [ { "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:22:14.51569963Z", "changedAt": "2026-11-10T16:22:14.51569963Z", "fingerprint": "76fa4caf8fdc" } ], "updatedAt": "2026-11-10T16:22:14.51569963Z" } }, "edges": [ { "by": 35, "category": "reach", "leads": "smollm3-3b", "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 30, the smallest 4-bit build Hugging Face publishes is its own ONNX q4f16 at 2.1 GB, about 2.5 GB with the fifth added, and the full BF16 files that the counted runtimes load come to about 7.4 GB, both under 16 GB. Runtimes 20, released Transformers carries the model code, llama.cpp knows the architecture, and vLLM and SGLang run it through their Transformers backends in released versions; Ollama has no library entry and LM Studio's catalogue has none, so those two earn nothing. Builds 10, Hugging Face's ONNX repository has 4-bit builds, one of the checklist's kinds, and int8 builds, which are neither FP8 nor GGUF. They run in ONNX Runtime and Transformers.js rather than the runtimes above. Terms 15, plain Apache 2.0, scored by its terms since the repository holds no LICENSE file. Download 10, not gated. Hosted 0, no inference provider is listed for it on the Hub and none of the provider lists checked carries it." }, "scores": { "a": 50, "b": 85 } }, { "by": 20, "category": "interface", "leads": "smollm3-3b", "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, a Jinja file in the repository. Tool-calling format 25, the card says how tools are declared, how calls come back and which vLLM parser to use. Trained tool calling 20, the template invites one or more calls per turn, each in its own block. Structured output 0, nothing documents asking the weights for JSON. Reasoning control 10, thinking turns off per request through the kwarg or a system-prompt flag, and the reasoning comes back in think tags. Context 5, trained at 65,536 tokens, with 131,072 only through YaRN." }, "scores": { "a": 60, "b": 80 } }, { "by": 11, "category": "upkeep", "leads": "smollm3-3b", "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 14, it covers limitations, architecture, parameters, context and languages but has no intended-use section and leaves the June 2025 cut-off to the chat template. Training data 20, the datasets, mixture weights and post-training set are published with sources and sizes. Last change 20, Hugging Face's ONNX build changed its tokenizer config on 28 October 2026, though the main repository hasn't changed since 10 September 2025. Responsiveness 10, staff answered 14 of 36 discussions, none since November 2025. Version policy 5, a first release never replaced, with no dated notes. File health 5, template and tokenizer reports wait with no reply." }, "scores": { "a": 63, "b": 74 } }, { "by": 10, "category": "risk", "leads": "smollm3-3b", "name": "Risk", "reasons": { "a": "File safety 25, safetensors only and no remote code. Provenance 15, Nous Research's own organisation, linked from its blog, with a hash on every file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 15, plain Apache 2.0. Security programme 0, no way to report a flaw found. 25 + 15 + 0 + 0 + 15 + 0 = 55.", "b": "File safety 25, safetensors only and nothing from the repository runs on load or to build prompts. Provenance 15, the HuggingFaceTB organisation linked from Hugging Face's launch post, with a SHA-256 for each shard. Safety evaluation 0, none published. Agent guidance 5, only the general cautions in the card's Limitations section. Licence risk 15, Apache 2.0 with nothing added and a working licence link. Security programme 5, huggingface.co has a valid security.txt and Transformers has a security policy; whether advisories are handled in public couldn't be checked from our session, so that half is scored as absent." }, "scores": { "a": 55, "b": 65 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Nous Research", "b": "Hugging Face", "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": "2025-09-10", "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": "3.3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "SmolLM3-3B scores 61 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in every scored category.", "question": "Which is better for AI agents, Arete-27B or SmolLM3-3B?" } ], "goodFor": [ { "aheadOn": null, "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, 85 against 50", "Risk, 65 against 55", "Agent interface, 80 against 60", "Documentation and upkeep, 74 against 63" ], "also": null, "goodFor": "Agents that want a small, fully transparent tool-calling cousin on a laptop, CPU or edge device", "slug": "smollm3-3b", "watchFor": "65,536 tokens trained; 131,072 needs a YaRN edit to config.json" } ], "history": { "arete-27b": null, "smollm3-3b": [ { "causes": [ "product" ], "date": "2026-11-03", "delta": 0, "from": 61, "fromGrade": "C", "to": 61, "toGrade": "C" } ] }, "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-smollm3-3b.json", "title": "Agents-A1-4B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-smollm3-3b" }, { "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": 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"https://www.anchorterminal.com/compare/minimax-m2-5-vs-smollm3-3b.json", "title": "MiniMax-M2.5 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-smollm3-3b.json", "title": "MiniMax-M2.7 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-smollm3-3b.json", "title": "MiniMax-M3.1-Flash vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-smollm3-3b.json", "title": "Mistral Large 4 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-smollm3-3b.json", "title": "Muse Glimmer 30B vs SmolLM3-3B", "url": 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"json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-smollm3-3b.json", "title": "Nemotron 3 Nano 30B A3B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-smollm3-3b.json", "title": "Nemotron 3 Nano 4B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-smollm3-3b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-smollm3-3b.json", "title": "Nemotron 3 Super 120B A12B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-smollm3-3b.json", "title": "Nemotron 3 Ultra 550B A55B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-smollm3-3b.json", "title": "North Mini Code 1.0 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-smollm3-3b.json", "title": "occamy-1.0 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-smollm3-3b.json", "title": "Olmo 3 7B Think vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-smollm3-3b.json", "title": "Ornith-1.5-122B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-smollm3-3b.json", "title": "Ornith-1.5-35B-A3B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-smollm3-3b.json", "title": "Ornith-1.5-397B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-smollm3-3b.json", "title": "Ornith-1.5-9B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-smollm3-3b.json", "title": "OTel-2.0-LLM-31B-IT vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-smollm3-3b.json", "title": "Qwen-AgentWorld-35B-A3B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-smollm3-3b.json", "title": "Qwen3-30B-A3B-Instruct-2507 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-smollm3-3b.json", "title": "Qwen3-4B-Instruct-2507 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-smollm3-3b.json", "title": "Qwen3.5-0.8B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-smollm3-3b.json", "title": 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"https://www.anchorterminal.com/compare/qwen3-6-27b-vs-smollm3-3b.json", "title": "Qwen3.6-27B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-smollm3-3b.json", "title": "Qwen3.6-35B-A3B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-smollm3-3b.json", "title": "Qwen3.8-27B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-smollm3-3b.json", "title": "Qwen3.8-Flash-Next vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-smollm3-3b" }, { "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-next-vs-smollm3-3b.json", "title": "Qwen3-Coder-Next vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-smollm3-3b.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-smollm3-3b.json", "title": "Qwen3-VL-2B-Instruct vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-smollm3-3b.json", "title": "Qwen3-VL-30B-A3B-Instruct vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-smollm3-3b.json", "title": "Qwen3-VL-32B-Instruct vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-smollm3-3b.json", "title": "Qwen3-VL-4B-Instruct vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-smollm3-3b.json", "title": "Qwen3-VL-8B-Instruct vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-smollm3-3b.json", "title": "Qwopus3.8-27B-Flash-V2 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-smollm3-3b.json", "title": "Recursive Flint vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-smollm3-3b.json", "title": "Ripple-65B Preview vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-smollm3-3b.json", "title": "SenseNova-Loop-9B-Preview vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-spark-x2-6-4b.json", "title": "SmolLM3-3B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-step-3-7-flash.json", "title": "SmolLM3-3B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-step-3-7-flash" }, { "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/smollm3-3b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "SmolLM3-3B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-thinkingcap-qwen3-8-27b.json", "title": "SmolLM3-3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-unlimited-ocr-2.json", "title": "SmolLM3-3B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-2b.json", "title": "SmolLM3-3B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-4b.json", "title": "SmolLM3-3B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-zaya1-8b.json", "title": "SmolLM3-3B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "arete-27b": 50, "by": 35, "edge": "smollm3-3b", "key": "reach", "name": "Reach", "smollm3-3b": 85, "weight": 25 }, { "arete-27b": 55, "by": 10, "edge": "smollm3-3b", "key": "risk", "name": "Risk", "smollm3-3b": 65, "weight": 20 }, { "arete-27b": 60, "by": 20, "edge": "smollm3-3b", "key": "interface", "name": "Agent interface", "smollm3-3b": 80, "weight": 10 }, { "arete-27b": 63, "by": 11, "edge": "smollm3-3b", "key": "upkeep", "name": "Documentation and upkeep", "smollm3-3b": 74, "weight": 10 } ], "summary": "SmolLM3-3B scores 61 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in every scored category. 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.", "smollm3-3b": "A tiny, fully open cousin with documented tool calling and switchable thinking, for local subtasks and routing on laptops and CPUs. It needs about 8 GB in BF16, or under 3 GB in Hugging Face's 4-bit ONNX build. The main risks are a 64K native context, no safety results and no staff reply in its discussions since November 2025." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/arete-27b-vs-smollm3-3b", "json": "https://www.anchorterminal.com/compare/arete-27b-vs-smollm3-3b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/arete-27b-vs-smollm3-3b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/arete-27b-vs-smollm3-3b.min.md" }, "markdown": "SmolLM3-3B scores 61 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in every scored category. 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- SmolLM3-3B: grade C, 61/100, rank #55 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/smollm3-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/smollm3-3b.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\nWatch for: No quantised build from Nous Research, so the GGUF route depends on a third party\n\n### SmolLM3-3B (C)\n\nGood for: Agents that want a small, fully transparent tool-calling cousin on a laptop, CPU or edge device\n\nAhead on:\n- Reach, 85 against 50\n- Risk, 65 against 55\n- Agent interface, 80 against 60\n- Documentation and upkeep, 74 against 63\n\nWatch for: 65,536 tokens trained; 131,072 needs a YaRN edit to config.json\n\n\n## Why the edge\n\n### Reach: SmolLM3-3B +35\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- SmolLM3-3B (85): Memory 30, the smallest 4-bit build Hugging Face publishes is its own ONNX q4f16 at 2.1 GB, about 2.5 GB with the fifth added, and the full BF16 files that the counted runtimes load come to about 7.4 GB, both under 16 GB. Runtimes 20, released Transformers carries the model code, llama.cpp knows the architecture, and vLLM and SGLang run it through their Transformers backends in released versions; Ollama has no library entry and LM Studio's catalogue has none, so those two earn nothing. Builds 10, Hugging Face's ONNX repository has 4-bit builds, one of the checklist's kinds, and int8 builds, which are neither FP8 nor GGUF. They run in ONNX Runtime and Transformers.js rather than the runtimes above. Terms 15, plain Apache 2.0, scored by its terms since the repository holds no LICENSE file. Download 10, not gated. Hosted 0, no inference provider is listed for it on the Hub and none of the provider lists checked carries it.\n\n### Agent interface: SmolLM3-3B +20\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- SmolLM3-3B (80): Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card says how tools are declared, how calls come back and which vLLM parser to use. Trained tool calling 20, the template invites one or more calls per turn, each in its own block. Structured output 0, nothing documents asking the weights for JSON. Reasoning control 10, thinking turns off per request through the kwarg or a system-prompt flag, and the reasoning comes back in think tags. Context 5, trained at 65,536 tokens, with 131,072 only through YaRN.\n\n### Documentation and upkeep: SmolLM3-3B +11\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- SmolLM3-3B (74): Model card 14, it covers limitations, architecture, parameters, context and languages but has no intended-use section and leaves the June 2025 cut-off to the chat template. Training data 20, the datasets, mixture weights and post-training set are published with sources and sizes. Last change 20, Hugging Face's ONNX build changed its tokenizer config on 28 October 2026, though the main repository hasn't changed since 10 September 2025. Responsiveness 10, staff answered 14 of 36 discussions, none since November 2025. Version policy 5, a first release never replaced, with no dated notes. File health 5, template and tokenizer reports wait with no reply.\n\n### Risk: SmolLM3-3B +10\n\n- Arete-27B (55): File safety 25, safetensors only and no remote code. Provenance 15, Nous Research's own organisation, linked from its blog, with a hash on every file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 15, plain Apache 2.0. Security programme 0, no way to report a flaw found. 25 + 15 + 0 + 0 + 15 + 0 = 55.\n- SmolLM3-3B (65): File safety 25, safetensors only and nothing from the repository runs on load or to build prompts. Provenance 15, the HuggingFaceTB organisation linked from Hugging Face's launch post, with a SHA-256 for each shard. Safety evaluation 0, none published. Agent guidance 5, only the general cautions in the card's Limitations section. Licence risk 15, Apache 2.0 with nothing added and a working licence link. Security programme 5, huggingface.co has a valid security.txt and Transformers has a security policy; whether advisories are handled in public couldn't be checked from our session, so that half is scored as absent.\n\n## Score by category\n\n| Category | Weight | Arete-27B | SmolLM3-3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 50 | 85 | SmolLM3-3B +35 |\n| Risk | 20% (20 this run) | 55 | 65 | SmolLM3-3B +10 |\n| Agent interface | 10% (10 this run) | 60 | 80 | SmolLM3-3B +20 |\n| Documentation and upkeep | 10% (10 this run) | 63 | 74 | SmolLM3-3B +11 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **55.1 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Arete-27B | SmolLM3-3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Nous Research | Hugging Face |\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 | 2025-09-10 |\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 | 3.3/5 (4) |\n\n## How the scores have moved\n\n### Arete-27B\n\nNo score change since it was listed.\n\n### SmolLM3-3B\n\n- no change on 3 November 2026: product\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**SmolLM3-3B.** A tiny, fully open cousin with documented tool calling and switchable thinking, for local subtasks and routing on laptops and CPUs. It needs about 8 GB in BF16, or under 3 GB in Hugging Face's 4-bit ONNX build. The main risks are a 64K native context, no safety results and no staff reply in its discussions since November 2025.\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### SmolLM3-3B\n\n1. Start it with `vllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes`, or tool calls come back as text\n2. Pass `chat_template_kwargs` with `enable_thinking` false for short tasks, since thinking is on by default; a `/think` or `/no_think` flag in the system prompt overrides the kwarg\n3. Put earlier tool calls in the assistant message content, since the template ignores a `tool_calls` field when it renders history\n4. Add the YaRN `rope_scaling` block (factor 2.0, original 65536) to config.json before sending more than 65,536 tokens\n5. Use temperature 0.6 and top_p 0.95, the card's recommended sampling\n6. Needs Transformers v4.53.0 or later; for llama.cpp use a GGUF from ggml-org or another converter, since Hugging Face publishes none\n7. Validate JSON output yourself or use the runtime's guided decoding\n\n## Questions\n\n### Which is better for AI agents, Arete-27B or SmolLM3-3B?\n\nSmolLM3-3B scores 61 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/arete-27b-vs-smollm3-3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/arete-27b-vs-smollm3-3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"arete-27b\", \"b\": \"smollm3-3b\"}`. From a terminal: `anchor compare arete-27b smollm3-3b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/arete-27b.json and https://www.anchorterminal.com/api/v1/tools/smollm3-3b.json\n\n## Other comparisons with Arete-27B or SmolLM3-3B\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 SmolLM3-3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-smollm3-3b.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 SmolLM3-3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-smollm3-3b.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 SmolLM3-3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-smollm3-3b.md)\n- [Arete-27B vs BigBang-v1](https://www.anchorterminal.com/compare/arete-27b-vs-bigbang-v1.md)\n- [Arete-27B vs 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}, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Arete-27B vs SmolLM3-3B", "url": "" } ], "description": "SmolLM3-3B scores 61 (C) to Arete-27B's 55.1 (C) for open-weight models you run yourself. 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