{ "data": { "a": { "slug": "granite-4-2-8b", "name": "Granite 4.2 8B", "vendor": "IBM", "vendorUrl": "https://www.ibm.com/granite", "kind": "weights", "category": "open-weights", "summary": "Granite 4.2 8B is IBM's open-weight dense reasoning model of 8.8B parameters, post-trained from Granite 4.1 8B Base, with switchable thinking, tool calling and a 131,072-token context, published in BF16, FP8, FP4, GGUF and MLX under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/granite-4-2-8b", "markdownUrl": "https://www.anchorterminal.com/tools/granite-4-2-8b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/granite-4-2-8b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/granite-4-2-8b.json", "dns": "granite-4-2-8b.grade.anchorterminal.com", "repo": "https://huggingface.co/ibm-granite/granite-4.2-8b", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "ibm-granite/granite-4.2-8b" }, { "registry": "huggingface", "name": "ibm-granite/granite-4.2-8b-GGUF" }, { "registry": "huggingface", "name": "ibm-granite/granite-4.2-8b-fp8" }, { "registry": "huggingface", "name": "ibm-granite/granite-4.2-8b-mxfp4" }, { "registry": "huggingface", "name": "ibm-granite/granite-4.2-8b-nvfp4" }, { "registry": "huggingface", "name": "ibm-granite/granite-4.2-8b-q4-mlx" }, { "registry": "ollama", "name": "granite4.2:8b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The BF16 repository and IBM's FP8, MXFP4, NVFP4, GGUF and MLX repositories are not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 17.6 GB for the BF16 files, 5.3 GB for IBM's Q4_K_M GGUF or 5.0 GB for its 4-bit MLX build (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/ibm-granite/granite-4.2-8b", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "reasoning", "tool-calling", "multilingual", "apache-2.0", "safetensors", "fp8", "fp4", "gguf", "mlx", "signed", "self-hosted" ], "lastRelease": "2026-10-20", "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": 53, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 85, "reach": 95, "risk": 55, "upkeep": 92 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "IBM's mid-size reasoning cousin, with the same strengths as the 3B. It runs in every runtime we count from IBM's own builds on a 16 GB machine, is signed, plain Apache 2.0, and comes with a disclosure of its training data. No safety results are published and structured output isn't documented.", "bestFor": "Agents that want a documented, signed, Apache 2.0 reasoning model with tool calling on a 16 GB machine", "strengths": [ "Runs in llama.cpp, Ollama, vLLM, SGLang and Transformers from IBM's own FP8, FP4, GGUF and MLX builds", "Plain Apache 2.0, ungated, signed with sigstore", "The card documents the tool-calling format, the parser to use and three thinking modes", "IBM publishes a machine-readable disclosure of the training datasets", "IBM answers most questions on the repository" ], "weaknesses": [ "No safety results published for it", "Structured output isn't documented", "The card's vLLM command still loads a Python plugin from the repository, which released vLLM no longer needs", "No knowledge cut-off on the card", "One inference provider, so an agent without the hardware has little choice" ], "agentNotes": [ "Start it with `ollama run granite4.2:8b`, or `llama-server -hf ibm-granite/granite-4.2-8b-GGUF:Q4_K_M --jinja`", "With vLLM 0.32.0 or later leave out `--reasoning-parser-plugin`, since the parser is built in, and use `--tool-call-parser qwen3_coder`", "Thinking is on by default. Pass `enable_thinking` false to turn it off or `low_effort` true for short reasoning", "The context is 131,072 tokens. The card's 512K extension isn't documented", "Validate JSON output yourself, since structured output isn't documented", "IBM recommends Granite Guardian beside it to flag risky inputs and outputs" ], "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": 61 } ], "editorialScores": { "interface": 85, "reach": 95, "risk": 55, "upkeep": 92 }, "provenanceScore": 88, "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": "ollama run granite4.2:8b\n# or, with llama.cpp and IBM's own GGUF:\nllama-server -hf ibm-granite/granite-4.2-8b-GGUF:Q4_K_M --jinja\n# or, with vLLM 0.32.0 or later:\nvllm serve ibm-granite/granite-4.2-8b --max-model-len 131072 \\\n --reasoning-parser granite_thinking_parser \\\n --tool-call-parser qwen3_coder --enable-auto-tool-choice", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"granite4.2:8b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/granite-4-2-8b" }, "sameCompany": [ "granite-embedding-small-english-r2", "granite-guardian", "granite-speech-5-0-2b", "ibm-watson-stt", "granite-switch", "granite-4-2-30b", "granite-4-2-3b" ], "weights": { "trainedBy": "IBM", "builtOn": "ibm-granite/granite-4.1-8b-base (IBM's own base checkpoint, post-trained; named on the card and in its metadata)", "params": "8.79B", "context": 131072, "goodFor": "Mid-size dense reasoning model for tool calling, coding and multilingual chat on a laptop GPU, with thinking that is on, off or short per request.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "granite-4.2-8b-nvfp4", "url": "https://huggingface.co/ibm-granite/granite-4.2-8b-nvfp4", "who": "publisher", "precision": "nvfp4", "sizeGB": 6.13, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM" ] }, { "name": "granite-4.2-8b-mxfp4", "url": "https://huggingface.co/ibm-granite/granite-4.2-8b-mxfp4", "who": "publisher", "precision": "mxfp4", "sizeGB": 5.88, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM" ] }, { "name": "granite-4.2-8b-Q4_K_M", "url": "https://huggingface.co/ibm-granite/granite-4.2-8b-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 5.35, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "IBM's GGUF repository also holds Q4_0 (5.06 GB), Q6_K and BF16 files among others" }, { "name": "granite-4.2-8b-q4-mlx", "url": "https://huggingface.co/ibm-granite/granite-4.2-8b-q4-mlx", "who": "publisher", "precision": "q4", "sizeGB": 4.95, "memoryGB": 8, "format": "mlx", "runsOn": [ "mlx-lm" ], "note": "The smallest build at 4-bit or better. IBM also publishes q8-mlx (9.34 GB) and bf16-mlx" }, { "name": "ollama granite4.2:8b", "url": "https://ollama.com/library/granite4.2:8b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 5.3, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Ollama's library build, size as its tags page shows it on 10 November 2026" }, { "name": "granite-4.2-8b-fp8", "url": "https://huggingface.co/ibm-granite/granite-4.2-8b-fp8", "who": "publisher", "precision": "fp8", "sizeGB": 9.62, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM" ] }, { "name": "granite-4.2-8b-Q8_0", "url": "https://huggingface.co/ibm-granite/granite-4.2-8b-GGUF", "who": "publisher", "precision": "q8_0", "sizeGB": 9.35, "memoryGB": 12, "format": "gguf", "runsOn": [ "llama.cpp" ] }, { "name": "granite-4.2-8b", "url": "https://huggingface.co/ibm-granite/granite-4.2-8b", "who": "publisher", "precision": "bf16", "sizeGB": 17.58, "memoryGB": 24, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ] } ], "run": "ollama run granite4.2:8b", "runBuild": "ollama granite4.2:8b", "checked": "2026-11-10", "minMemoryGB": 8 }, "family": { "lab": "IBM (Granite)", "family": "Granite 4 (4.0, 4.1, 4.2)", "url": "https://www.anchorterminal.com/open-weights/#ibm", "others": [ { "name": "granite-4.2-3b", "url": "https://huggingface.co/ibm-granite/granite-4.2-3b", "params": "3B (Hub safetensors count 3.66B)", "listing": "https://www.anchorterminal.com/tools/granite-4-2-3b" }, { "name": "granite-4.2-30b", "url": "https://huggingface.co/ibm-granite/granite-4.2-30b", "params": "30B (Hub safetensors count 29.28B)", "listing": "https://www.anchorterminal.com/tools/granite-4-2-30b" }, { "name": "granite-4.1-8b-base", "url": "https://huggingface.co/ibm-granite/granite-4.1-8b-base", "params": "8B (Hub safetensors count 8.38B)" }, { "name": "granite-4.1-8b", "url": "https://huggingface.co/ibm-granite/granite-4.1-8b", "params": "8B (Hub safetensors count 8.79B)" }, { "name": "granite-4.1-3b-base", "url": "https://huggingface.co/ibm-granite/granite-4.1-3b-base", "params": "3B (Hub safetensors count 3.40B)" }, { "name": "granite-4.1-3b", "url": "https://huggingface.co/ibm-granite/granite-4.1-3b", "params": "3B (Hub safetensors count 3.40B)" }, { "name": "granite-4.1-30b-base", "url": "https://huggingface.co/ibm-granite/granite-4.1-30b-base", "params": "30B (Hub safetensors count 28.87B)" }, { "name": "granite-4.1-30b", "url": "https://huggingface.co/ibm-granite/granite-4.1-30b", "params": "30B (Hub safetensors count 28.87B)" }, { "name": "granite-4.0-h-350m-base", "url": "https://huggingface.co/ibm-granite/granite-4.0-h-350m-base", "params": "350M (Hub safetensors count 340M)" }, { "name": "granite-4.0-h-350m", "url": "https://huggingface.co/ibm-granite/granite-4.0-h-350m", "params": "350M (Hub safetensors count 340M)" }, { "name": "granite-4.0-h-1b-base", "url": "https://huggingface.co/ibm-granite/granite-4.0-h-1b-base", "params": "1B (Hub safetensors count 1.46B)" }, { "name": "granite-4.0-h-1b", "url": "https://huggingface.co/ibm-granite/granite-4.0-h-1b", "params": "1B (Hub safetensors count 1.46B)" }, { "name": "granite-4.0-350m-base", "url": "https://huggingface.co/ibm-granite/granite-4.0-350m-base", "params": "350M (Hub safetensors count 352M)" }, { "name": "granite-4.0-350m", "url": "https://huggingface.co/ibm-granite/granite-4.0-350m", "params": "350M (Hub safetensors count 352M)" }, { "name": "granite-4.0-1b-base", "url": "https://huggingface.co/ibm-granite/granite-4.0-1b-base", "params": "1B (Hub safetensors count 1.63B)" }, { "name": "granite-4.0-1b", "url": "https://huggingface.co/ibm-granite/granite-4.0-1b", "params": "1B (Hub safetensors count 1.63B)" }, { "name": "granite-4.0-micro-base", "url": "https://huggingface.co/ibm-granite/granite-4.0-micro-base", "params": "3.40B (Hub safetensors count; the name states none)" }, { "name": "granite-4.0-micro", "url": "https://huggingface.co/ibm-granite/granite-4.0-micro", "params": "3.40B (Hub safetensors count; the name states none)" }, { "name": "granite-4.0-h-tiny-base", "url": "https://huggingface.co/ibm-granite/granite-4.0-h-tiny-base", "params": "6.94B (Hub safetensors count; the name states none)" }, { "name": "granite-4.0-h-tiny", "url": "https://huggingface.co/ibm-granite/granite-4.0-h-tiny", "params": "6.94B (Hub safetensors count; the name states none)" }, { "name": "granite-4.0-h-small-base", "url": "https://huggingface.co/ibm-granite/granite-4.0-h-small-base", "params": "32.21B (Hub safetensors count; the name states none)" }, { "name": "granite-4.0-h-small", "url": "https://huggingface.co/ibm-granite/granite-4.0-h-small", "params": "32.21B (Hub safetensors count; the name states none)" }, { "name": "granite-4.0-h-micro-base", "url": "https://huggingface.co/ibm-granite/granite-4.0-h-micro-base", "params": "3.19B (Hub safetensors count; the name states none)" }, { "name": "granite-4.0-h-micro", "url": "https://huggingface.co/ibm-granite/granite-4.0-h-micro", "params": "3.19B (Hub safetensors count; the name states none)" }, { "name": "granite-4.0-tiny-preview", "url": "https://huggingface.co/ibm-granite/granite-4.0-tiny-preview", "params": "6.67B (Hub safetensors count; the name states none)" }, { "name": "granite-4.0-tiny-base-preview", "url": "https://huggingface.co/ibm-granite/granite-4.0-tiny-base-preview", "params": "6.67B (Hub safetensors count; the name states none)" } ], "olderCount": 64 }, "area": "models", "provenance": { "legalEntity": "International Business Machines Corporation (per IBM's naming guidance, as read for granite-guardian)", "domain": "ibm.com", "domainRegistered": "1986-03-19", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/ibm-granite/granite-4.2-8b/commits/main", "securityTxt": "valid", "checked": "2026-11-10", "notes": [ "IBM's Granite docs page for 4.2 links the ibm-granite collection on Hugging Face and the GitHub repository, and the docs site says it is no longer updated. The domain date is carried from the granite-guardian check.", "The ibm-granite organisation (209 members) isn't verified on the Hub, which the calibration doesn't require.", "Nothing is hosted for this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the Hugging Face commit history, 15 commits from 7 August to 20 October 2026, most titled only \"granite-4.2\".", "www.ibm.com/.well-known/security.txt is present with PSIRT contacts and expires on 21 November 2026.", "Released on 25 August 2026 by the card and IBM's blog, which firstReleased uses. The repository and its weight files date from 7 August 2026, the date BATCH.md and the IBM family page used before 21 October." ], "score": 88 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/granite-4-2-8b.json" }, "answer": "Granite 4.2 8B and Nanbeige4.2-3B score within a point of each other on agent readiness, 61 (C) and 61 (C).", "b": { "slug": "nanbeige-4-2-3b", "name": "Nanbeige4.2-3B", "vendor": "Nanbeige LLM Lab (BOSS Zhipin)", "vendorUrl": "https://huggingface.co/Nanbeige", "kind": "weights", "category": "open-weights", "summary": "Nanbeige4.2-3B is a small agent model from BOSS Zhipin's Nanbeige lab, 4.2B parameters with layers reused in a loop, with tool calling, a thinking switch and a 256K context. Apache 2.0. Released SGLang and llama.cpp run it.", "url": "https://www.anchorterminal.com/tools/nanbeige-4-2-3b", "markdownUrl": "https://www.anchorterminal.com/tools/nanbeige-4-2-3b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nanbeige-4-2-3b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nanbeige-4-2-3b.json", "dns": "nanbeige-4-2-3b.grade.anchorterminal.com", "repo": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B", "license": "Apache-2.0 (Hub metadata only; the repository has no LICENSE file)", "transports": [], "packages": [ { "registry": "huggingface", "name": "Nanbeige/Nanbeige4.2-3B" }, { "registry": "huggingface", "name": "Nanbeige/Nanbeige4.2-3B-FP8" }, { "registry": "huggingface", "name": "Nanbeige/Nanbeige4.2-3B-GPTQ-Int8" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the repositories is gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use under Apache 2.0, per the card's metadata. Running it yourself costs little: 5.2 GB in FP8 or 8-bit from Nanbeige, 8.3 GB in BF16, and 2.7 GB for bartowski's Q4_K_M conversion (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/Nanbeige/Nanbeige4.2-3B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "small", "looped-transformer", "reasoning", "tool-calling", "agentic", "long-context", "apache-2.0", "safetensors", "fp8", "custom-code", "self-hosted" ], "lastRelease": "2026-09-11", "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": 62, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 85, "reach": 85, "risk": 55, "upkeep": 72 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A small model built for agent work, with tool calling it was trained for, a thinking switch and a 256K context in about 5 GB, under Apache 2.0, from a lab that answers most questions. Its architecture is its own, so check your runtime: SGLang and llama.cpp have it, Transformers needs the repository's code, and Nanbeige ships no GGUF.", "bestFor": "Local agents that need tool calling and a long context on a laptop or a small GPU", "strengths": [ "Trained for multi-step tool use, with parsers in released SGLang", "A 256K context in a model that fits 8 GB", "Apache 2.0, ungated", "FP8 and 8-bit builds from Nanbeige", "Nanbeige replied in most of the recent discussions" ], "weaknesses": [ "Its own architecture: Transformers loads it only with the repository's code", "No GGUF from Nanbeige, so llama.cpp users rely on a third party's conversion", "The card's serve instructions still point at Nanbeige's forks", "No safety results on the card", "Training data described only in general terms", "Not changed since September" ], "agentNotes": [ "Serve it with SGLang 0.5.23 or later and pass `--tool-call-parser nanbeige --reasoning-parser nanbeige`", "Pass `tools` in the request. The card recommends `tool_call_format=\"xml\"`", "Set `preserve_thinking` true for multi-turn tool use and coding, false for plain chat", "Use temperature 1.0 for agent and tool work and 0.6 for reasoning and chat, per the card", "For llama.cpp use b12242 or later. The GGUF files on the Hub are bartowski's, not Nanbeige's", "If you load it with Transformers, read modeling_nanbeige.py first: trust_remote_code runs it" ], "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": 61 } ], "editorialScores": { "interface": 85, "reach": 85, "risk": 55, "upkeep": 72 }, "provenanceScore": 58, "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": "pip install \"sglang\u003e=0.5.23\"\npython -m sglang.launch_server --model-path Nanbeige/Nanbeige4.2-3B --reasoning-parser nanbeige --tool-call-parser nanbeige", "http": "# SGLang serves an OpenAI-compatible API at http://localhost:30000/v1. Pass tools in the request, and\n# chat_template_kwargs {\"enable_thinking\": false} for a direct answer" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/nanbeige-4-2-3b" }, "sameCompany": [ "nanbeige-4-3-33b" ], "weights": { "trainedBy": "Nanbeige LLM Lab (BOSS Zhipin)", "builtOn": "Nanbeige4.2-3B-Base, per the card and the metadata", "params": "4.2B", "context": 262144, "goodFor": "A small local agent for tool use, coding and office tasks, with thinking you can turn off and a long context.", "runtimes": [ "llama.cpp", "vLLM", "SGLang" ], "builds": [ { "name": "Nanbeige4.2-3B-FP8", "url": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 5.19, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ], "note": "Nanbeige's FP8 build" }, { "name": "Nanbeige4.2-3B-GPTQ-Int8", "url": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B-GPTQ-Int8", "who": "publisher", "precision": "int8", "sizeGB": 5.19, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ], "note": "Nanbeige's 8-bit GPTQ build" }, { "name": "bartowski Nanbeige_Nanbeige4.2-3B-GGUF (Q4_K_M)", "url": "https://huggingface.co/bartowski/Nanbeige_Nanbeige4.2-3B-GGUF", "who": "bartowski", "precision": "q4_k_m", "sizeGB": 2.68, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not Nanbeige's. Its base_model tag names Nanbeige/Nanbeige4.2-3B, from the Hub's model information on 10 November 2026" }, { "name": "bartowski Nanbeige_Nanbeige4.2-3B-GGUF (Q8_0)", "url": "https://huggingface.co/bartowski/Nanbeige_Nanbeige4.2-3B-GGUF", "who": "bartowski", "precision": "q8_0", "sizeGB": 4.43, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not Nanbeige's. From the Hub's model information on 10 November 2026" }, { "name": "Nanbeige4.2-3B (BF16 safetensors)", "url": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B", "who": "publisher", "precision": "bf16", "sizeGB": 8.34, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ], "note": "Two shards" } ], "run": "python -m sglang.launch_server --model-path Nanbeige/Nanbeige4.2-3B --reasoning-parser nanbeige --tool-call-parser nanbeige", "runBuild": "Nanbeige4.2-3B (BF16 safetensors)", "checked": "2026-11-10", "minMemoryGB": 8 }, "family": { "lab": "Nanbeige LLM Lab (BOSS Zhipin)", "family": "Nanbeige4.2", "url": "https://www.anchorterminal.com/open-weights/#nanbeige", "others": [ { "name": "Nanbeige4.2-3B-Base", "url": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B-Base" } ], "olderCount": 7 }, "area": "models", "provenance": { "legalEntity": "BOSS Zhipin (Kanzhun Limited), unconfirmed", "domain": "zhipin.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The publisher's identity, domain and security facts are carried from our Nanbeige4.3-33B listing: the lab's report for this model names Nanbeige LLM Lab, BOSS Zhipin, and the card's contact address is on kanzhun.com.", "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored.", "The changelog is the commit history, 29 commits from 21 July to 11 September 2026." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nanbeige-4-2-3b.json" }, "edges": [ { "by": 20, "category": "upkeep", "leads": "granite-4-2-8b", "name": "Documentation and upkeep", "reasons": { "a": "Model card 17, Intended use (best for), limitations (ethical considerations and limitations), architecture, parameters (8B), context length (128K native) and 12 tested languages are on the card. Training data 20, IBM's disclosure file for the 8B lists the datasets used in pre-training and post-training as machine-readable metadata, and the card describes the three training stages. Last change 20, the last commit, on 20 October 2026, replaced model.safetensors.index.json (titled \"granite-4.2 index file fix\"), 21 days before this check. Responsiveness 20, Of the 5 discussions that aren't pull requests (13 September to 5 November 2026), 4 have a reply from an ibm-granite member. Version policy 5, Granite 4.1 8B stays in its own repository and 4.2 came out in new ones. File health 10, Config, template and card agree on the architecture, the 131,072-token context and the thinking switch. 17 + 20 + 20 + 20 + 5 + 10 = 92.", "b": "Model card 17, Intended use, limitations, the architecture, the parameters (3B outside the embeddings), the context and the languages (in the metadata) are on the card. Training data 5, A general description of how the fine-tuning and reinforcement learning data was built and filtered, with no sources, sizes or cut-off. Last change 15, the last commit, on 11 September 2026, updated modeling_nanbeige.py, 60 days before this check and within 90. Responsiveness 20, Of the 34 most recent discussions that aren't pull requests (21 July to 10 September 2026), 20 have a reply from a Nanbeige member, which is most. Version policy 5, Nanbeige's earlier models (Nanbeige4-3B, 4.1-3B) stay in their own repositories, as the Base, FP8, 8-bit and DSpark builds of this one do. File health 10, Config, tokenizer config, template and card agree on the architecture and the 262,144 tokens. 17 + 5 + 15 + 20 + 5 + 10 = 72." }, "scores": { "a": 92, "b": 72 } }, { "by": 10, "category": "reach", "leads": "granite-4-2-8b", "name": "Reach", "reasons": { "a": "Memory 30, IBM's own 4-bit MLX build granite-4.2-8b-q4-mlx is 4.95 GB, and 4.95 GB × 1.2 = 5.9 GB, within 8 GB. Its Q4_0 GGUF is 5.06 GB and Ollama's library build granite4.2:8b (5.3 GB) fits 8 GB too. Runtimes 25, Released versions run the granite architecture in llama.cpp (granite, b12242), vLLM (GraniteForCausalLM in the v0.33.0 registry), SGLang (granite.py at v0.5.23), Transformers (v5.22.2) and Ollama (its own granite4.2 library entry, 8b tag). Builds 15, IBM publishes FP8, MXFP4 and NVFP4 safetensors builds, a GGUF repository with Q4_0 to Q8_0 and BF16 files, and 4-bit, 8-bit and BF16 MLX builds, so quantised builds at two precisions or more. Terms 15, Apache 2.0, an OSI-approved licence, with no use policy or other terms added on the card. Download 10, the Hub API reports gated false for the BF16, FP8, MXFP4, NVFP4, GGUF and MLX repositories, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps one inference provider (DeepInfra, live), short of the two the line needs. 30 + 25 + 15 + 15 + 10 + 0 = 95.", "b": "Memory 30, the smallest build from Nanbeige is the FP8 repository (the 8-bit GPTQ one is the same size), 5.19 GB, and 5.19 GB × 1.2 = 6.2 GB, within 16 GB. bartowski's GGUF isn't read for this line. Runtimes 15, Released SGLang 0.5.23 has the model (srt/models/nanbeige.py) with its parsers, llama.cpp b12242 has the nanbeige architecture, and vLLM 0.33.0 names NanbeigeForCausalLM in its registry, served through its Transformers backend. Builds 15, Quantised builds from Nanbeige at two precisions, FP8 and 8-bit GPTQ, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence, with no use policy or other terms on the card. Download 10, the Hub API reports gated false for the model and its FP8 and 8-bit repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and no Nanbeige or BOSS Zhipin API for it was found. 30 + 15 + 15 + 15 + 10 + 0 = 85." }, "scores": { "a": 95, "b": 85 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "IBM", "b": "Nanbeige LLM Lab (BOSS Zhipin)", "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 (Hub metadata only; the repository has no LICENSE file)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-20", "b": "2026-09-11", "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" } ], "faq": [ { "answer": "Granite 4.2 8B and Nanbeige4.2-3B score within a point of each other on agent readiness, 61 (C) and 61 (C).", "question": "Which is better for AI agents, Granite 4.2 8B or Nanbeige4.2-3B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 95 against 85", "Documentation and upkeep, 92 against 72" ], "also": null, "goodFor": "Agents that want a documented, signed, Apache 2.0 reasoning model with tool calling on a 16 GB machine", "slug": "granite-4-2-8b", "watchFor": "No safety results published for it" }, { "aheadOn": null, "also": null, "goodFor": "Local agents that need tool calling and a long context on a laptop or a small GPU", "slug": "nanbeige-4-2-3b", "watchFor": "Its own architecture: Transformers loads it only with the repository's code" } ], "history": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": 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Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-5-9b.json", "title": "Nanbeige4.2-3B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-6-27b.json", "title": "Nanbeige4.2-3B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-6-35b-a3b.json", "title": "Nanbeige4.2-3B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-8-27b.json", "title": "Nanbeige4.2-3B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-8-flash-next.json", "title": "Nanbeige4.2-3B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Nanbeige4.2-3B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-coder-next.json", "title": "Nanbeige4.2-3B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Nanbeige4.2-3B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-2b-instruct.json", "title": "Nanbeige4.2-3B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Nanbeige4.2-3B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-32b-instruct.json", "title": "Nanbeige4.2-3B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-4b-instruct.json", "title": "Nanbeige4.2-3B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-8b-instruct.json", "title": "Nanbeige4.2-3B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwopus3-8-27b-flash-v2.json", "title": "Nanbeige4.2-3B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-recursive-flint.json", "title": "Nanbeige4.2-3B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-ripple-65b-preview.json", "title": "Nanbeige4.2-3B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-sensenova-loop-9b-preview.json", "title": "Nanbeige4.2-3B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-smollm3-3b.json", "title": "Nanbeige4.2-3B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-spark-x2-6-4b.json", "title": "Nanbeige4.2-3B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-step-3-7-flash.json", "title": "Nanbeige4.2-3B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-swift-1-5-qwen3-8-27b.json", "title": "Nanbeige4.2-3B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Nanbeige4.2-3B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-thinkingcap-qwen3-8-27b.json", "title": "Nanbeige4.2-3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-unlimited-ocr-2.json", "title": "Nanbeige4.2-3B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-2b.json", "title": "Nanbeige4.2-3B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-4b.json", "title": "Nanbeige4.2-3B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-zaya1-8b.json", "title": "Nanbeige4.2-3B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 10, "edge": "granite-4-2-8b", "granite-4-2-8b": 95, "key": "reach", "name": "Reach", "nanbeige-4-2-3b": 85, "weight": 25 }, { "by": 0, "edge": "", "granite-4-2-8b": 55, "key": "risk", "name": "Risk", "nanbeige-4-2-3b": 55, "weight": 20 }, { "by": 0, "edge": "", "granite-4-2-8b": 85, "key": "interface", "name": "Agent interface", "nanbeige-4-2-3b": 85, "weight": 10 }, { "by": 20, "edge": "granite-4-2-8b", "granite-4-2-8b": 92, "key": "upkeep", "name": "Documentation and upkeep", "nanbeige-4-2-3b": 72, "weight": 10 } ], "summary": "Granite 4.2 8B and Nanbeige4.2-3B score within a point of each other on agent readiness, 61 (C) and 61 (C). Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "granite-4-2-8b": "IBM's mid-size reasoning cousin, with the same strengths as the 3B. It runs in every runtime we count from IBM's own builds on a 16 GB machine, is signed, plain Apache 2.0, and comes with a disclosure of its training data. No safety results are published and structured output isn't documented.", "nanbeige-4-2-3b": "A small model built for agent work, with tool calling it was trained for, a thinking switch and a 256K context in about 5 GB, under Apache 2.0, from a lab that answers most questions. Its architecture is its own, so check your runtime: SGLang and llama.cpp have it, Transformers needs the repository's code, and Nanbeige ships no GGUF." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nanbeige-4-2-3b", "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nanbeige-4-2-3b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nanbeige-4-2-3b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nanbeige-4-2-3b.min.md" }, "markdown": "Granite 4.2 8B and Nanbeige4.2-3B score within a point of each other on agent readiness, 61 (C) and 61 (C). Both do open-weight models you run yourself.\n\n- Granite 4.2 8B: grade C, 61/100, rank #66 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/granite-4-2-8b.md · JSON https://www.anchorterminal.com/api/v1/tools/granite-4-2-8b.json\n- Nanbeige4.2-3B: grade C, 61/100, rank #78 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nanbeige-4-2-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nanbeige-4-2-3b.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### Granite 4.2 8B (C)\n\nGood for: Agents that want a documented, signed, Apache 2.0 reasoning model with tool calling on a 16 GB machine\n\nAhead on:\n- Reach, 95 against 85\n- Documentation and upkeep, 92 against 72\n\nWatch for: No safety results published for it\n\n### Nanbeige4.2-3B (C)\n\nGood for: Local agents that need tool calling and a long context on a laptop or a small GPU\n\nWatch for: Its own architecture: Transformers loads it only with the repository's code\n\n\n## Why the edge\n\n### Documentation and upkeep: Granite 4.2 8B +20\n\n- Granite 4.2 8B (92): Model card 17, Intended use (best for), limitations (ethical considerations and limitations), architecture, parameters (8B), context length (128K native) and 12 tested languages are on the card. Training data 20, IBM's disclosure file for the 8B lists the datasets used in pre-training and post-training as machine-readable metadata, and the card describes the three training stages. Last change 20, the last commit, on 20 October 2026, replaced model.safetensors.index.json (titled \"granite-4.2 index file fix\"), 21 days before this check. Responsiveness 20, Of the 5 discussions that aren't pull requests (13 September to 5 November 2026), 4 have a reply from an ibm-granite member. Version policy 5, Granite 4.1 8B stays in its own repository and 4.2 came out in new ones. File health 10, Config, template and card agree on the architecture, the 131,072-token context and the thinking switch. 17 + 20 + 20 + 20 + 5 + 10 = 92.\n- Nanbeige4.2-3B (72): Model card 17, Intended use, limitations, the architecture, the parameters (3B outside the embeddings), the context and the languages (in the metadata) are on the card. Training data 5, A general description of how the fine-tuning and reinforcement learning data was built and filtered, with no sources, sizes or cut-off. Last change 15, the last commit, on 11 September 2026, updated modeling_nanbeige.py, 60 days before this check and within 90. Responsiveness 20, Of the 34 most recent discussions that aren't pull requests (21 July to 10 September 2026), 20 have a reply from a Nanbeige member, which is most. Version policy 5, Nanbeige's earlier models (Nanbeige4-3B, 4.1-3B) stay in their own repositories, as the Base, FP8, 8-bit and DSpark builds of this one do. File health 10, Config, tokenizer config, template and card agree on the architecture and the 262,144 tokens. 17 + 5 + 15 + 20 + 5 + 10 = 72.\n\n### Reach: Granite 4.2 8B +10\n\n- Granite 4.2 8B (95): Memory 30, IBM's own 4-bit MLX build granite-4.2-8b-q4-mlx is 4.95 GB, and 4.95 GB × 1.2 = 5.9 GB, within 8 GB. Its Q4_0 GGUF is 5.06 GB and Ollama's library build granite4.2:8b (5.3 GB) fits 8 GB too. Runtimes 25, Released versions run the granite architecture in llama.cpp (granite, b12242), vLLM (GraniteForCausalLM in the v0.33.0 registry), SGLang (granite.py at v0.5.23), Transformers (v5.22.2) and Ollama (its own granite4.2 library entry, 8b tag). Builds 15, IBM publishes FP8, MXFP4 and NVFP4 safetensors builds, a GGUF repository with Q4_0 to Q8_0 and BF16 files, and 4-bit, 8-bit and BF16 MLX builds, so quantised builds at two precisions or more. Terms 15, Apache 2.0, an OSI-approved licence, with no use policy or other terms added on the card. Download 10, the Hub API reports gated false for the BF16, FP8, MXFP4, NVFP4, GGUF and MLX repositories, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps one inference provider (DeepInfra, live), short of the two the line needs. 30 + 25 + 15 + 15 + 10 + 0 = 95.\n- Nanbeige4.2-3B (85): Memory 30, the smallest build from Nanbeige is the FP8 repository (the 8-bit GPTQ one is the same size), 5.19 GB, and 5.19 GB × 1.2 = 6.2 GB, within 16 GB. bartowski's GGUF isn't read for this line. Runtimes 15, Released SGLang 0.5.23 has the model (srt/models/nanbeige.py) with its parsers, llama.cpp b12242 has the nanbeige architecture, and vLLM 0.33.0 names NanbeigeForCausalLM in its registry, served through its Transformers backend. Builds 15, Quantised builds from Nanbeige at two precisions, FP8 and 8-bit GPTQ, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence, with no use policy or other terms on the card. Download 10, the Hub API reports gated false for the model and its FP8 and 8-bit repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and no Nanbeige or BOSS Zhipin API for it was found. 30 + 15 + 15 + 15 + 10 + 0 = 85.\n\n## Score by category\n\n| Category | Weight | Granite 4.2 8B | Nanbeige4.2-3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 85 | Granite 4.2 8B +10 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 85 | 85 | even |\n| Documentation and upkeep | 10% (10 this run) | 92 | 72 | Granite 4.2 8B +20 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Granite 4.2 8B | Nanbeige4.2-3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | IBM | Nanbeige LLM Lab (BOSS Zhipin) |\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 (Hub metadata only; the repository has no LICENSE file) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-20 | 2026-09-11 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n\n## Verdicts\n\n**Granite 4.2 8B.** IBM's mid-size reasoning cousin, with the same strengths as the 3B. It runs in every runtime we count from IBM's own builds on a 16 GB machine, is signed, plain Apache 2.0, and comes with a disclosure of its training data. No safety results are published and structured output isn't documented.\n\n**Nanbeige4.2-3B.** A small model built for agent work, with tool calling it was trained for, a thinking switch and a 256K context in about 5 GB, under Apache 2.0, from a lab that answers most questions. Its architecture is its own, so check your runtime: SGLang and llama.cpp have it, Transformers needs the repository's code, and Nanbeige ships no GGUF.\n\n## Before you call either\n\n### Granite 4.2 8B\n\n1. Start it with `ollama run granite4.2:8b`, or `llama-server -hf ibm-granite/granite-4.2-8b-GGUF:Q4_K_M --jinja`\n2. With vLLM 0.32.0 or later leave out `--reasoning-parser-plugin`, since the parser is built in, and use `--tool-call-parser qwen3_coder`\n3. Thinking is on by default. Pass `enable_thinking` false to turn it off or `low_effort` true for short reasoning\n4. The context is 131,072 tokens. The card's 512K extension isn't documented\n5. Validate JSON output yourself, since structured output isn't documented\n6. IBM recommends Granite Guardian beside it to flag risky inputs and outputs\n\n### Nanbeige4.2-3B\n\n1. Serve it with SGLang 0.5.23 or later and pass `--tool-call-parser nanbeige --reasoning-parser nanbeige`\n2. Pass `tools` in the request. The card recommends `tool_call_format=\"xml\"`\n3. Set `preserve_thinking` true for multi-turn tool use and coding, false for plain chat\n4. Use temperature 1.0 for agent and tool work and 0.6 for reasoning and chat, per the card\n5. For llama.cpp use b12242 or later. The GGUF files on the Hub are bartowski's, not Nanbeige's\n6. If you load it with Transformers, read modeling_nanbeige.py first: trust_remote_code runs it\n\n## Questions\n\n### Which is better for AI agents, Granite 4.2 8B or Nanbeige4.2-3B?\n\nGranite 4.2 8B and Nanbeige4.2-3B score within a point of each other on agent readiness, 61 (C) and 61 (C).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nanbeige-4-2-3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nanbeige-4-2-3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"granite-4-2-8b\", \"b\": \"nanbeige-4-2-3b\"}`. From a terminal: `anchor compare granite-4-2-8b nanbeige-4-2-3b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/granite-4-2-8b.json and https://www.anchorterminal.com/api/v1/tools/nanbeige-4-2-3b.json\n\n## Other comparisons with Granite 4.2 8B or Nanbeige4.2-3B\n\n- [Agents-A1-4B vs Granite 4.2 8B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-granite-4-2-8b.md)\n- [Agents-A1-4B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nanbeige-4-2-3b.md)\n- [Apertus v1.5 8B vs Granite 4.2 8B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-8b.md)\n- [Apertus v1.5 8B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nanbeige-4-2-3b.md)\n- [Apodex-1.1-mini vs Granite 4.2 8B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-granite-4-2-8b.md)\n- [Apodex-1.1-mini vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nanbeige-4-2-3b.md)\n- [Arete-27B vs Granite 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Unlimited-OCR-2](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-unlimited-ocr-2.md)\n- [Nanbeige4.2-3B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-2b.md)\n- [Nanbeige4.2-3B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-4b.md)\n- [Nanbeige4.2-3B vs ZAYA1-8B](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-zaya1-8b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-11", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Granite 4.2 8B vs Nanbeige4.2-3B", "url": "" } ], "description": "Granite 4.2 8B and Nanbeige4.2-3B score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. 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