{ "data": { "a": { "slug": "glm-5-3-flash", "name": "GLM-5.3-Flash", "vendor": "Z.ai", "vendorUrl": "https://z.ai", "kind": "weights", "category": "open-weights", "summary": "GLM-5.3-Flash is Z.ai's open-weight multimodal mixture-of-experts model, 320B parameters with 18B active and a 1,048,576-token context, published on Hugging Face in FP8 and BF16 under the MIT licence.", "url": "https://www.anchorterminal.com/tools/glm-5-3-flash", "markdownUrl": "https://www.anchorterminal.com/tools/glm-5-3-flash.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/glm-5-3-flash.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/glm-5-3-flash.json", "dns": "glm-5-3-flash.grade.anchorterminal.com", "repo": "https://huggingface.co/zai-org/GLM-5.3-Flash", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "zai-org/GLM-5.3-Flash" }, { "registry": "huggingface", "name": "zai-org/GLM-5.3-Flash-BF16" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the MIT licence. Z.ai also sells hosted access as glm-5.3-flash on its own API, which isn't graded here. Running the weights yourself costs the hardware, 328.4 GB for the FP8 files and 642.7 GB for BF16 (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": "2026-11-02" }, "docsUrl": "https://huggingface.co/zai-org/GLM-5.3-Flash", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "mit", "safetensors", "fp8", "self-hosted" ], "lastRelease": "2026-08-26", "graded": true, "anchor": { "graded": true, "score": 65.1, "grade": "B", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 13, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 75, "interface": 70, "reach": 60, "risk": 55, "upkeep": 58 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-02" }, "negative": 0, "verdict": "An MIT-licensed multimodal agent and coding model with a 1M-token context and several tool calls per turn, served by vLLM, SGLang, llama.cpp and Transformers. Z.ai's smallest build is 328 GB in FP8, so it needs a multi-GPU server. The main risk is that nothing on safety or prompt injection is published and there is no way to report a flaw.", "bestFor": "Teams with a multi-GPU server who want an MIT-licensed multimodal agent model with a 1M-token context, or agents that call it through a hosted provider", "strengths": [ "MIT weights with no use policy, downloadable with no account", "Model code in released llama.cpp b11768, vLLM 0.32.0, SGLang 0.5.22 and Transformers 5.16.1", "Safetensors only, no remote code, files in Z.ai's own organisation with a SHA-256 per shard", "1,048,576 tokens of native context and several tool calls per turn in the chat template", "Chat template changed as recently as 15 October 2026, and hosted by Z.ai and six other providers for agents without the hardware" ], "weaknesses": [ "Z.ai's smallest build is 328.4 GB in FP8, and no Ollama or LM Studio local build exists", "No safety evaluation, agent-use guidance or security contact", "Thinking is always on, and reasoning_effort only lowers it to low", "The tool-call format lives only in the chat template, with the parser named by the runtimes", "The card leaves out intended use, limitations, context length and knowledge cut-off, and no zai-org reply on discussions since 28 September" ], "agentNotes": [ "Start it with `vllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47` (vLLM 0.32.0 or later, about 386 GB of GPU memory per vLLM's recipe)", "Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; calls come back as tool_call blocks with arg_key and arg_value pairs", "Pass `chat_template_kwargs: {\"reasoning_effort\": \"low\"}` for short tasks; anything other than low or high falls back to max", "Don't rely on `thinking: false` from SGLang's cookbook, since the template has no such switch and always opens a think block", "Set `clear_thinking: true` in chat use, since the default keeps earlier reasoning in the prompt", "On a 128 GB machine only third-party low-bit builds come near fitting, none of them from Z.ai" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 65.1 } ], "editorialScores": { "capability": 75, "interface": 70, "reach": 60, "risk": 55, "upkeep": 58 }, "provenanceScore": 61, "checked": { "categories": { "capability": "2026-11-02", "interface": "2026-11-02", "reach": "2026-11-02", "risk": "2026-11-02", "upkeep": "2026-11-02" }, "last": "2026-11-02", "oldest": "2026-11-02", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# vLLM 0.32.0 or later, per vLLM's recipe (FP8, about 386 GB of GPU memory)\nvllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 \\\n --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47\n# or, with llama.cpp and llama.cpp's own GGUF:\nllama-server -hf ggml-org/GLM-5.3-Flash-GGUF:Q4_K --jinja", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"zai-org/GLM-5.3-Flash\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"chat_template_kwargs\": {\"reasoning_effort\": \"low\"}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/glm-5-3-flash" }, "sameCompany": [ "glm-4-5-air", "glm-4-7-flash", "glm-5-3" ], "weights": { "trainedBy": "Z.ai", "params": "320B", "active": "18B", "context": 1048576, "goodFor": "Multimodal agent and coding model: text, images and video in, text out, with tool calling and always-on reasoning.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "GLM-5.3-Flash-FP4-Local", "url": "https://huggingface.co/nvidia/GLM-5.3-Flash-FP4-Local", "who": "NVIDIA", "precision": "nvfp4", "sizeGB": 177.5, "memoryGB": 256, "format": "safetensors", "note": "NVIDIA's own FP4 build (Model Optimizer), made for NVIDIA Blackwell GPUs such as DGX Spark and RTX PRO 6000; FP4 kernels don't run on the AMD reference machine. Not the publisher's build, so it doesn't count toward Reach's memory item. Size summed from the Hub's file list on 21 October 2026." }, { "name": "GLM-5.3-Flash-NVFP4", "url": "https://huggingface.co/nvidia/GLM-5.3-Flash-NVFP4", "who": "NVIDIA", "precision": "nvfp4", "sizeGB": 204.4, "memoryGB": 256, "format": "safetensors", "note": "NVIDIA's own FP4 build (Model Optimizer), made for NVIDIA Blackwell GPUs such as DGX Spark and RTX PRO 6000; FP4 kernels don't run on the AMD reference machine. Not the publisher's build, so it doesn't count toward Reach's memory item. Size summed from the Hub's file list on 21 October 2026." }, { "name": "ggml-org GLM-5.3-Flash-GGUF (Q4_K)", "url": "https://huggingface.co/ggml-org/GLM-5.3-Flash-GGUF", "who": "llama.cpp", "precision": "q4_k", "sizeGB": 182.03, "memoryGB": 256, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From ggml-org, the llama.cpp project's organisation. 181.37 GB of Q4_K weights plus the 0.65 GB Q8_0 vision projector, from the Hub's model information on 10 November 2026. The 2-bit files (Q2_K 133.8 GB, Q2_K_S 110.0 GB) are below the 4-bit line and aren't listed. Recorded after the last grading, so Reach doesn't count it yet" }, { "name": "GLM-5.3-Flash", "url": "https://huggingface.co/zai-org/GLM-5.3-Flash", "who": "publisher", "precision": "fp8", "sizeGB": 328.3, "memoryGB": 512, "format": "safetensors" }, { "name": "GLM-5.3-Flash-BF16", "url": "https://huggingface.co/zai-org/GLM-5.3-Flash-BF16", "who": "publisher", "precision": "bf16", "sizeGB": 642.7, "memoryGB": 1024, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47", "runBuild": "GLM-5.3-Flash", "checked": "2026-10-21", "minMemoryGB": 256 }, "family": { "lab": "Z.ai (Zhipu AI)", "family": "GLM-5", "url": "https://www.anchorterminal.com/open-weights/#zai", "others": [ { "name": "GLM-5", "url": "https://huggingface.co/zai-org/GLM-5", "params": "744B-A40B" }, { "name": "GLM-5.1", "url": "https://huggingface.co/zai-org/GLM-5.1", "params": "744B-A40B" }, { "name": "GLM-5.2", "url": "https://huggingface.co/zai-org/GLM-5.2", "params": "744B-A40B" }, { "name": "GLM-5.3", "url": "https://huggingface.co/zai-org/GLM-5.3", "params": "744B-A40B", "listing": "https://www.anchorterminal.com/tools/glm-5-3" } ], "olderCount": 19, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Z.AI Co., Ltd (named in the licence's copyright line)", "domain": "z.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/zai-org/GLM-5.3-Flash/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The LICENSE file reads Copyright (c) 2026 Z.AI Co., Ltd. The registration date of z.ai couldn't be read, since the RDAP service answered 429 on 21 October 2026.", "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history. Early messages say only \"update\"; later ones say what changed (a chat template fix on 4 September, evaluation results on 7 September and 12 October, a chat template speed-up on 15 October).", "z.ai/.well-known/security.txt and docs.z.ai/.well-known/security.txt answered 404 on 21 October 2026." ], "score": 61 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/glm-5-3-flash.json", "live": { "slug": "glm-5-3-flash", "securityTxt": { "url": "https://z.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:09:36.154640571Z" }, "domain": { "domain": "z.ai", "registered": "2017-12-15", "source": "https://rdap.identitydigital.services/rdap/domain/z.ai", "checkedAt": "2026-11-08T15:05:18.801433245Z" }, "pages": [ { "url": "https://huggingface.co/zai-org/GLM-5.3-Flash/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:24:02.708352819Z", "changedAt": "2026-11-10T16:24:02.708352819Z", "fingerprint": "108904f1a213" } ], "updatedAt": "2026-11-10T16:24:02.708352819Z" } }, "answer": "GLM-5.3-Flash scores 65.1 (B) on agent readiness against Granite 4.2 30B's 61 (C), and leads in 0 of 4 scored categories. Granite 4.2 30B leads on reach, agent interface and documentation and upkeep.", "b": { "slug": "granite-4-2-30b", "name": "Granite 4.2 30B", "vendor": "IBM", "vendorUrl": "https://www.ibm.com/granite", "kind": "weights", "category": "open-weights", "summary": "Granite 4.2 30B is IBM's open-weight dense reasoning model of 29.3B parameters, post-trained from Granite 4.1 30B 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-4-2-30b", "markdownUrl": "https://www.anchorterminal.com/tools/granite-4-2-30b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/granite-4-2-30b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/granite-4-2-30b.json", "dns": "granite-4-2-30b.grade.anchorterminal.com", "repo": "https://huggingface.co/ibm-granite/granite-4.2-30b", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "ibm-granite/granite-4.2-30b" }, { "registry": "huggingface", "name": "ibm-granite/granite-4.2-30b-GGUF" }, { "registry": "huggingface", "name": "ibm-granite/granite-4.2-30b-fp8" }, { "registry": "huggingface", "name": "ibm-granite/granite-4.2-30b-mxfp4" }, { "registry": "huggingface", "name": "ibm-granite/granite-4.2-30b-nvfp4" }, { "registry": "huggingface", "name": "ibm-granite/granite-4.2-30b-q4-mlx" }, { "registry": "ollama", "name": "granite4.2:30b" } ], "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, 58.5 GB for the BF16 files, 17.7 GB for IBM's Q4_K_M GGUF or 16.5 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-4.2-30b", "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": "2026-09-04", "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": 52, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 85, "reach": 90, "risk": 55, "upkeep": 87 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "IBM's largest reasoning cousin, with the same strengths as the 3B. It runs in every runtime we count from IBM's own builds on a 24 GB card, 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 24 GB card", "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 granite4.2:30b`, or `llama-server -hf ibm-granite/granite-4.2-30b-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": 90, "risk": 55, "upkeep": 87 }, "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 granite4.2:30b\n# or, with llama.cpp and IBM's own GGUF:\nllama-server -hf ibm-granite/granite-4.2-30b-GGUF:Q4_K_M --jinja\n# or, with vLLM 0.32.0 or later:\nvllm serve ibm-granite/granite-4.2-30b --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\": \"granite4.2:30b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/granite-4-2-30b" }, "sameCompany": [ "granite-embedding-small-english-r2", "granite-guardian", "granite-speech-5-0-2b", "ibm-watson-stt", "granite-switch", "granite-4-2-3b", "granite-4-2-8b" ], "weights": { "trainedBy": "IBM", "builtOn": "ibm-granite/granite-4.1-30b-base (IBM's own base checkpoint, post-trained; named on the card and in its metadata)", "params": "29.3B", "context": 131072, "goodFor": "IBM's largest dense reasoning model for tool calling, coding and multilingual chat, with thinking that is on, off or short per request, on a 24 GB card at 4 bits.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "granite-4.2-30b-nvfp4", "url": "https://huggingface.co/ibm-granite/granite-4.2-30b-nvfp4", "who": "publisher", "precision": "nvfp4", "sizeGB": 17.65, "memoryGB": 24, "format": "safetensors", "runsOn": [ "vLLM" ] }, { "name": "granite-4.2-30b-mxfp4", "url": "https://huggingface.co/ibm-granite/granite-4.2-30b-mxfp4", "who": "publisher", "precision": "mxfp4", "sizeGB": 16.76, "memoryGB": 24, "format": "safetensors", "runsOn": [ "vLLM" ] }, { "name": "granite-4.2-30b-Q4_K_M", "url": "https://huggingface.co/ibm-granite/granite-4.2-30b-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 17.72, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "IBM's GGUF repository also holds Q4_0 (16.58 GB), Q6_K and BF16 files among others" }, { "name": "granite-4.2-30b-q4-mlx", "url": "https://huggingface.co/ibm-granite/granite-4.2-30b-q4-mlx", "who": "publisher", "precision": "q4", "sizeGB": 16.47, "memoryGB": 24, "format": "mlx", "runsOn": [ "mlx-lm" ], "note": "The smallest build at 4-bit or better. IBM also publishes q8-mlx (31.11 GB) and bf16-mlx" }, { "name": "ollama granite4.2:30b", "url": "https://ollama.com/library/granite4.2:30b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 18, "memoryGB": 24, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Ollama's library build, size as its tags page shows it on 10 November 2026" }, { "name": "granite-4.2-30b-fp8", "url": "https://huggingface.co/ibm-granite/granite-4.2-30b-fp8", "who": "publisher", "precision": "fp8", "sizeGB": 30.11, "memoryGB": 48, "format": "safetensors", "runsOn": [ "vLLM" ] }, { "name": "granite-4.2-30b-Q8_0", "url": "https://huggingface.co/ibm-granite/granite-4.2-30b-GGUF", "who": "publisher", "precision": "q8_0", "sizeGB": 31.11, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp" ] }, { "name": "granite-4.2-30b", "url": "https://huggingface.co/ibm-granite/granite-4.2-30b", "who": "publisher", "precision": "bf16", "sizeGB": 58.55, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ] } ], "run": "ollama run granite4.2:30b", "runBuild": "ollama granite4.2:30b", "checked": "2026-11-10", "minMemoryGB": 24 }, "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-4.2-8b", "url": "https://huggingface.co/ibm-granite/granite-4.2-8b", "params": "8B (Hub safetensors count 8.79B)", "listing": "https://www.anchorterminal.com/tools/granite-4-2-8b" }, { "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-4.2-30b/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-4-2-30b.json" }, "edges": [ { "by": 30, "category": "reach", "leads": "granite-4-2-30b", "name": "Reach", "reasons": { "a": "Memory earns nothing, since Z.ai's smallest build is the 328.4 GB FP8 one, about 394 GB with the fifth added, and neither Ollama nor LM Studio has a local build (both list it as cloud only). Four runtimes run it in a released version, llama.cpp, vLLM, SGLang and Transformers, for 20, while Ollama and LM Studio don't. Z.ai publishes one quantised build, FP8, for 10. The MIT licence with nothing added gives the full 15, the files download with no account or gate for 10, and Z.ai's own API plus six providers on Hugging Face serve it, for 5.", "b": "Memory 25, IBM's own 4-bit MLX build granite-4.2-30b-q4-mlx is 16.47 GB, and 16.47 GB × 1.2 = 19.8 GB, which fits 24 GB but not 16 GB. Its Q4_0 GGUF is 16.58 GB and Ollama's library build granite4.2:30b 18 GB, the same band. 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, 30b 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. 25 + 25 + 15 + 15 + 10 + 0 = 90." }, "scores": { "a": 60, "b": 90 } }, { "by": 29, "category": "upkeep", "leads": "granite-4-2-30b", "name": "Documentation and upkeep", "reasons": { "a": "The card covers architecture, parameters and languages and misses four of the seven items, for 8. Training data is 5, a 30T-token corpus described with no sources or cut-off. The last change, a chat template speed-up on 15 October in both repositories, is within 30 days on 2 November, for 20; Z.ai's GLM-5 GitHub repository has changed only its docs since. Responsiveness is 10, as zai-org answered 14 of the 38 recent discussions others opened (26 August to 30 October), the last on 28 September, which is some but not most. Version policy is 5 for a first release, and file health is the full 10, with files and card agreeing and no broken-file report waiting as far as the research record shows.", "b": "Model card 17, Intended use (best for), limitations (ethical considerations and limitations), architecture, parameters (30B), context length (128K native) and 12 tested languages are on the card. Training data 20, IBM's disclosure file for the 30B lists the datasets used in pre-training and post-training as machine-readable metadata, and the card describes the three training stages. Last change 15, the last commit to the repository was on 4 September 2026, 67 days before this check. Responsiveness 20, Of the 4 discussions that aren't pull requests (28 August to 3 November 2026), 3 have a reply from an ibm-granite member. Version policy 5, Granite 4.1 30B 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 + 15 + 20 + 5 + 10 = 87." }, "scores": { "a": 58, "b": 87 } }, { "by": 15, "category": "interface", "leads": "granite-4-2-30b", "name": "Agent interface", "reasons": { "a": "A Jinja chat template sits in both repositories, for 20. The tool-calling format is 10, because it is defined only by the template and the glm47 parser is named by vLLM and SGLang rather than by Z.ai. The template takes several calls in one turn, for 20. Structured output is 0, with nothing on the card and only the hosted API documented for JSON. Reasoning control is 10, a per-request budget through reasoning_effort, with the reasoning coming back in a think block, though thinking can't be turned off. Context is 10, 1,048,576 tokens native with no rotary scaling.", "b": "Chat template 20, chat_template.jinja in the repository, with roles, tools, tool responses, the thinking switch and low effort. Tool-calling format 25, the card has tool-calling sections for vLLM and SGLang, declares tools with OpenAI's function schema and names the parser to use, qwen3_coder, which both runtimes ship in the released versions read. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so it renders several calls in one turn, and the card names tool calling and agentic workflows as what it's best for. Structured output 0, the card doesn't document JSON or JSON Schema output or how to ask for it. Reasoning control 10, the card documents thinking on by default, non-thinking and low-effort modes per request through the template (enable_thinking and low_effort), with the reasoning returned apart from the answer. Context 10, 131,072 tokens natively per the card and config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85." }, "scores": { "a": 70, "b": 85 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Z.ai", "b": "IBM", "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": "MIT", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-08-26", "b": "2026-09-04", "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": "2.8/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "GLM-5.3-Flash scores 65.1 (B) on agent readiness against Granite 4.2 30B's 61 (C), and leads in 0 of 4 scored categories. Granite 4.2 30B leads on reach, agent interface and documentation and upkeep.", "question": "Which is better for AI agents, GLM-5.3-Flash or Granite 4.2 30B?" } ], "goodFor": [ { "aheadOn": null, "also": null, "goodFor": "Teams with a multi-GPU server who want an MIT-licensed multimodal agent model with a 1M-token context, or agents that call it through a hosted provider", "slug": "glm-5-3-flash", "watchFor": "Z.ai's smallest build is 328.4 GB in FP8, and no Ollama or LM Studio local build exists" }, { "aheadOn": [ "Reach, 90 against 60", "Agent interface, 85 against 70", "Documentation and upkeep, 87 against 58" ], "also": null, "goodFor": "Agents that want a documented, signed, Apache 2.0 reasoning model with tool calling on a 24 GB card", "slug": "granite-4-2-30b", "watchFor": "No safety results published for it" } ], "history": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3-flash.json", "title": "Agents-A1-4B vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-granite-4-2-30b.json", "title": "Agents-A1-4B vs Granite 4.2 30B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-granite-4-2-30b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3-flash.json", "title": "Apertus v1.5 8B vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-30b.json", "title": "Apertus v1.5 8B vs Granite 4.2 30B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-30b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-5-3-flash.json", "title": "Apodex-1.1-mini vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-granite-4-2-30b.json", "title": "Apodex-1.1-mini vs Granite 4.2 30B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-granite-4-2-30b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-5-3-flash.json", "title": "Arete-27B vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-30b.json", "title": "Arete-27B vs Granite 4.2 30B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-30b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-5-3-flash.json", "title": "BigBang-v1 vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-5-3-flash" }, { "json": 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"https://www.anchorterminal.com/compare/granite-4-2-30b-vs-lbs-27b.json", "title": "Granite 4.2 30B vs LBS-27B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-lfm2-5-2-6b.json", "title": "Granite 4.2 30B vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-lfm3-1-7b.json", "title": "Granite 4.2 30B vs LFM3-1.7B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-lfm3-1-7b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-lfm3-audio-2b.json", "title": "Granite 4.2 30B vs LFM3-Audio-2B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-lfm3-audio-2b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-lfm3-omni-2b.json", "title": "Granite 4.2 30B vs LFM3-Omni-2B", "url": 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"https://www.anchorterminal.com/compare/granite-4-2-30b-vs-mimo-v2-6-distill-qwen-9b.json", "title": "Granite 4.2 30B vs MiMo-V2.6 Distill Qwen 9B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-mimo-v2-6-distill-qwen-9b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-mimo-v2-6-flash-rl.json", "title": "Granite 4.2 30B vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-minicpm-o-4-5.json", "title": "Granite 4.2 30B vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-minicpm-v-4-7-1b.json", "title": "Granite 4.2 30B vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-minicpm-v-4-7-35b-a3b.json", "title": "Granite 4.2 30B vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-minicpm5-2b.json", "title": "Granite 4.2 30B vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-minicpm5-2b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-minimax-m2-5.json", "title": "Granite 4.2 30B vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-minimax-m2-7.json", "title": "Granite 4.2 30B vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-minimax-m3-1-flash.json", "title": "Granite 4.2 30B vs MiniMax-M3.1-Flash", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-minimax-m3-1-flash" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-mistral-large-4.json", "title": "Granite 4.2 30B vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-muse-glimmer-30b.json", "title": "Granite 4.2 30B vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-nanbeige-4-2-3b.json", "title": "Granite 4.2 30B vs Nanbeige4.2-3B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-nanbeige-4-2-3b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-nanbeige-4-3-33b.json", "title": "Granite 4.2 30B vs Nanbeige4.3-33B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-nanbeige-4-3-33b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Granite 4.2 30B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Granite 4.2 30B vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-nemotron-3-nano-30b-a3b.json", "title": "Granite 4.2 30B vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-nemotron-3-nano-4b.json", "title": "Granite 4.2 30B vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Granite 4.2 30B vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-nemotron-3-super-120b-a12b.json", "title": "Granite 4.2 30B vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-nemotron-3-ultra-550b-a55b.json", "title": "Granite 4.2 30B vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-north-mini-code-1-0.json", "title": "Granite 4.2 30B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-occamy-1-0.json", "title": "Granite 4.2 30B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-olmo-3-7b-think.json", "title": "Granite 4.2 30B vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-ornith-1-5-122b.json", "title": "Granite 4.2 30B vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-ornith-1-5-35b-a3b.json", "title": "Granite 4.2 30B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-ornith-1-5-397b.json", "title": "Granite 4.2 30B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-ornith-1-5-9b.json", "title": "Granite 4.2 30B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-otel-2-0-llm-31b-it.json", "title": "Granite 4.2 30B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen-agentworld-35b-a3b.json", "title": "Granite 4.2 30B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Granite 4.2 30B vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-4b-instruct-2507.json", "title": "Granite 4.2 30B vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-0-8b.json", "title": "Granite 4.2 30B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-122b-a10b.json", "title": "Granite 4.2 30B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-2b.json", "title": "Granite 4.2 30B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-397b-a17b.json", "title": "Granite 4.2 30B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-4b.json", "title": "Granite 4.2 30B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-9b.json", "title": "Granite 4.2 30B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-6-27b.json", "title": "Granite 4.2 30B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-6-35b-a3b.json", "title": "Granite 4.2 30B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-8-27b.json", "title": "Granite 4.2 30B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-8-flash-next.json", "title": "Granite 4.2 30B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Granite 4.2 30B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-coder-next.json", "title": "Granite 4.2 30B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Granite 4.2 30B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-vl-2b-instruct.json", "title": "Granite 4.2 30B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Granite 4.2 30B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-vl-32b-instruct.json", "title": "Granite 4.2 30B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-vl-4b-instruct.json", "title": "Granite 4.2 30B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-vl-8b-instruct.json", "title": "Granite 4.2 30B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwopus3-8-27b-flash-v2.json", "title": "Granite 4.2 30B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-recursive-flint.json", "title": "Granite 4.2 30B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-ripple-65b-preview.json", "title": "Granite 4.2 30B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-sensenova-loop-9b-preview.json", "title": "Granite 4.2 30B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-smollm3-3b.json", "title": "Granite 4.2 30B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-spark-x2-6-4b.json", "title": "Granite 4.2 30B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-step-3-7-flash.json", "title": "Granite 4.2 30B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-swift-1-5-qwen3-8-27b.json", "title": "Granite 4.2 30B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Granite 4.2 30B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-thinkingcap-qwen3-8-27b.json", "title": "Granite 4.2 30B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-unlimited-ocr-2.json", "title": "Granite 4.2 30B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-wevisone-vl-2b.json", "title": "Granite 4.2 30B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-wevisone-vl-4b.json", "title": "Granite 4.2 30B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-zaya1-8b.json", "title": "Granite 4.2 30B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 30, "edge": "granite-4-2-30b", "glm-5-3-flash": 60, "granite-4-2-30b": 90, "key": "reach", "name": "Reach", "weight": 25 }, { "by": 0, "edge": "", "glm-5-3-flash": 55, "granite-4-2-30b": 55, "key": "risk", "name": "Risk", "weight": 20 }, { "by": 15, "edge": "granite-4-2-30b", "glm-5-3-flash": 70, "granite-4-2-30b": 85, "key": "interface", "name": "Agent interface", "weight": 10 }, { "by": 29, "edge": "granite-4-2-30b", "glm-5-3-flash": 58, "granite-4-2-30b": 87, "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "GLM-5.3-Flash scores 65.1 (B) on agent readiness against Granite 4.2 30B's 61 (C), and leads in 0 of 4 scored categories. Granite 4.2 30B leads on reach, agent interface and documentation and upkeep. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "glm-5-3-flash": "An MIT-licensed multimodal agent and coding model with a 1M-token context and several tool calls per turn, served by vLLM, SGLang, llama.cpp and Transformers. Z.ai's smallest build is 328 GB in FP8, so it needs a multi-GPU server. The main risk is that nothing on safety or prompt injection is published and there is no way to report a flaw.", "granite-4-2-30b": "IBM's largest reasoning cousin, with the same strengths as the 3B. It runs in every runtime we count from IBM's own builds on a 24 GB card, 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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-granite-4-2-30b", "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-granite-4-2-30b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-granite-4-2-30b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-granite-4-2-30b.min.md" }, "markdown": "GLM-5.3-Flash scores 65.1 (B) on agent readiness against Granite 4.2 30B's 61 (C), and leads in 0 of 4 scored categories. Granite 4.2 30B leads on reach, agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- GLM-5.3-Flash: grade B, 65.1/100, rank #18 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-5-3-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-5-3-flash.json\n- Granite 4.2 30B: grade C, 61/100, rank #65 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/granite-4-2-30b.md · JSON https://www.anchorterminal.com/api/v1/tools/granite-4-2-30b.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### GLM-5.3-Flash (B)\n\nGood for: Teams with a multi-GPU server who want an MIT-licensed multimodal agent model with a 1M-token context, or agents that call it through a hosted provider\n\nWatch for: Z.ai's smallest build is 328.4 GB in FP8, and no Ollama or LM Studio local build exists\n\n### Granite 4.2 30B (C)\n\nGood for: Agents that want a documented, signed, Apache 2.0 reasoning model with tool calling on a 24 GB card\n\nAhead on:\n- Reach, 90 against 60\n- Agent interface, 85 against 70\n- Documentation and upkeep, 87 against 58\n\nWatch for: No safety results published for it\n\n\n## Why the edge\n\n### Reach: Granite 4.2 30B +30\n\n- GLM-5.3-Flash (60): Memory earns nothing, since Z.ai's smallest build is the 328.4 GB FP8 one, about 394 GB with the fifth added, and neither Ollama nor LM Studio has a local build (both list it as cloud only). Four runtimes run it in a released version, llama.cpp, vLLM, SGLang and Transformers, for 20, while Ollama and LM Studio don't. Z.ai publishes one quantised build, FP8, for 10. The MIT licence with nothing added gives the full 15, the files download with no account or gate for 10, and Z.ai's own API plus six providers on Hugging Face serve it, for 5.\n- Granite 4.2 30B (90): Memory 25, IBM's own 4-bit MLX build granite-4.2-30b-q4-mlx is 16.47 GB, and 16.47 GB × 1.2 = 19.8 GB, which fits 24 GB but not 16 GB. Its Q4_0 GGUF is 16.58 GB and Ollama's library build granite4.2:30b 18 GB, the same band. 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, 30b 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. 25 + 25 + 15 + 15 + 10 + 0 = 90.\n\n### Documentation and upkeep: Granite 4.2 30B +29\n\n- GLM-5.3-Flash (58): The card covers architecture, parameters and languages and misses four of the seven items, for 8. Training data is 5, a 30T-token corpus described with no sources or cut-off. The last change, a chat template speed-up on 15 October in both repositories, is within 30 days on 2 November, for 20; Z.ai's GLM-5 GitHub repository has changed only its docs since. Responsiveness is 10, as zai-org answered 14 of the 38 recent discussions others opened (26 August to 30 October), the last on 28 September, which is some but not most. Version policy is 5 for a first release, and file health is the full 10, with files and card agreeing and no broken-file report waiting as far as the research record shows.\n- Granite 4.2 30B (87): Model card 17, Intended use (best for), limitations (ethical considerations and limitations), architecture, parameters (30B), context length (128K native) and 12 tested languages are on the card. Training data 20, IBM's disclosure file for the 30B lists the datasets used in pre-training and post-training as machine-readable metadata, and the card describes the three training stages. Last change 15, the last commit to the repository was on 4 September 2026, 67 days before this check. Responsiveness 20, Of the 4 discussions that aren't pull requests (28 August to 3 November 2026), 3 have a reply from an ibm-granite member. Version policy 5, Granite 4.1 30B 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 + 15 + 20 + 5 + 10 = 87.\n\n### Agent interface: Granite 4.2 30B +15\n\n- GLM-5.3-Flash (70): A Jinja chat template sits in both repositories, for 20. The tool-calling format is 10, because it is defined only by the template and the glm47 parser is named by vLLM and SGLang rather than by Z.ai. The template takes several calls in one turn, for 20. Structured output is 0, with nothing on the card and only the hosted API documented for JSON. Reasoning control is 10, a per-request budget through reasoning_effort, with the reasoning coming back in a think block, though thinking can't be turned off. Context is 10, 1,048,576 tokens native with no rotary scaling.\n- Granite 4.2 30B (85): Chat template 20, chat_template.jinja in the repository, with roles, tools, tool responses, the thinking switch and low effort. Tool-calling format 25, the card has tool-calling sections for vLLM and SGLang, declares tools with OpenAI's function schema and names the parser to use, qwen3_coder, which both runtimes ship in the released versions read. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so it renders several calls in one turn, and the card names tool calling and agentic workflows as what it's best for. Structured output 0, the card doesn't document JSON or JSON Schema output or how to ask for it. Reasoning control 10, the card documents thinking on by default, non-thinking and low-effort modes per request through the template (enable_thinking and low_effort), with the reasoning returned apart from the answer. Context 10, 131,072 tokens natively per the card and config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.\n\n## Score by category\n\n| Category | Weight | GLM-5.3-Flash | Granite 4.2 30B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 60 | 90 | Granite 4.2 30B +30 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 70 | 85 | Granite 4.2 30B +15 |\n| Documentation and upkeep | 10% (10 this run) | 58 | 87 | Granite 4.2 30B +29 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **65.1 · B** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | GLM-5.3-Flash | Granite 4.2 30B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Z.ai | IBM |\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 | MIT | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-26 | 2026-09-04 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 2.8/5 (4) | none |\n\n## Verdicts\n\n**GLM-5.3-Flash.** An MIT-licensed multimodal agent and coding model with a 1M-token context and several tool calls per turn, served by vLLM, SGLang, llama.cpp and Transformers. Z.ai's smallest build is 328 GB in FP8, so it needs a multi-GPU server. The main risk is that nothing on safety or prompt injection is published and there is no way to report a flaw.\n\n**Granite 4.2 30B.** IBM's largest reasoning cousin, with the same strengths as the 3B. It runs in every runtime we count from IBM's own builds on a 24 GB card, 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## Before you call either\n\n### GLM-5.3-Flash\n\n1. Start it with `vllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47` (vLLM 0.32.0 or later, about 386 GB of GPU memory per vLLM's recipe)\n2. Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; calls come back as tool_call blocks with arg_key and arg_value pairs\n3. Pass `chat_template_kwargs: {\"reasoning_effort\": \"low\"}` for short tasks; anything other than low or high falls back to max\n4. Don't rely on `thinking: false` from SGLang's cookbook, since the template has no such switch and always opens a think block\n5. Set `clear_thinking: true` in chat use, since the default keeps earlier reasoning in the prompt\n6. On a 128 GB machine only third-party low-bit builds come near fitting, none of them from Z.ai\n\n### Granite 4.2 30B\n\n1. Start it with `ollama run granite4.2:30b`, or `llama-server -hf ibm-granite/granite-4.2-30b-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## Questions\n\n### Which is better for AI agents, GLM-5.3-Flash or Granite 4.2 30B?\n\nGLM-5.3-Flash scores 65.1 (B) on agent readiness against Granite 4.2 30B's 61 (C), and leads in 0 of 4 scored categories. Granite 4.2 30B leads on reach, agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/glm-5-3-flash-vs-granite-4-2-30b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-5-3-flash-vs-granite-4-2-30b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"glm-5-3-flash\", \"b\": \"granite-4-2-30b\"}`. From a terminal: `anchor compare glm-5-3-flash granite-4-2-30b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/glm-5-3-flash.json and https://www.anchorterminal.com/api/v1/tools/granite-4-2-30b.json\n\n## Other comparisons with GLM-5.3-Flash or Granite 4.2 30B\n\n- [Agents-A1-4B vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3-flash.md)\n- [Agents-A1-4B vs Granite 4.2 30B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-granite-4-2-30b.md)\n- [Apertus v1.5 8B vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3-flash.md)\n- [Apertus v1.5 8B vs Granite 4.2 30B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-30b.md)\n- [Apodex-1.1-mini vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-5-3-flash.md)\n- [Apodex-1.1-mini vs Granite 4.2 30B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-granite-4-2-30b.md)\n- [Arete-27B vs 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