{ "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", "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": "2026-10-21" }, "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": 1197, "categoryRank": 13, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 75, "interface": 70, "reach": 60, "risk": 55, "upkeep": 58 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "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": "0.6", "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": 53, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# 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", "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": "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": 53 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/glm-5-3-flash.json" }, "answer": "GLM-5.3-Flash scores 65.1 (B) on agent readiness against Granite 4.2 3B's 59.8 (C), and leads in 1 of 5 scored categories. Granite 4.2 3B leads on reach, agent interface and documentation and upkeep.", "b": { "slug": "granite-4-2-3b", "name": "Granite 4.2 3B", "vendor": "IBM", "vendorUrl": "https://www.ibm.com/granite", "kind": "weights", "category": "open-weights", "summary": "Granite 4.2 3B is IBM's open-weight dense reasoning model of 3.7B parameters, post-trained from Granite 4.1 3B 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-4-2-3b", "markdownUrl": "https://www.anchorterminal.com/tools/granite-4-2-3b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/granite-4-2-3b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/granite-4-2-3b.json", "repo": "https://huggingface.co/ibm-granite/granite-4.2-3b", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "ibm-granite/granite-4.2-3b" }, { "registry": "huggingface", "name": "ibm-granite/granite-4.2-3b-GGUF" }, { "registry": "huggingface", "name": "ibm-granite/granite-4.2-3b-fp8" }, { "registry": "huggingface", "name": "ibm-granite/granite-4.2-3b-mxfp4" }, { "registry": "huggingface", "name": "ibm-granite/granite-4.2-3b-nvfp4" }, { "registry": "huggingface", "name": "ibm-granite/granite-4.2-3b-q4-mlx" }, { "registry": "ollama", "name": "granite4.2:3b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of IBM's 4.2 3B repositories is gated (checked 2026-10-21). The files carry a sigstore signature (model.sig) that IBM documents how to verify.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. watsonx.ai lists Granite 4.1 models but not 4.2, and DeepInfra is the only inference provider Hugging Face maps for it. Running it yourself costs the hardware, 7.3 GB for the BF16 files and 2.2 GB for Ollama's Q4_K_M build (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/ibm-granite/granite-4.2-3b", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "small", "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": 59.8, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 49, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 21, "interface": 85, "reach": 95, "risk": 55, "upkeep": 92 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A small reasoning cousin for tool calls, coding and multilingual chat that runs almost anywhere, from a 2.2 GB Ollama build to vLLM and SGLang, under plain Apache 2.0 with signed files and a dataset disclosure. Thinking turns off or shortens per request. It has no published safety results and no documented JSON mode.", "bestFor": "Agents on small hardware that want a permissive reasoning model for tool calls and coding, with thinking they can switch off", "strengths": [ "Apache 2.0 with nothing added, ungated, signed with sigstore in IBM's own organisation", "2.2 GB as Ollama's Q4_K_M build, with IBM's own FP8, FP4, GGUF and MLX builds", "Runs in llama.cpp, Ollama, vLLM, SGLang and Transformers", "Tool format and parser documented for vLLM and SGLang, with several calls a turn", "Thinking that turns off or runs at low effort per request", "A machine-readable disclosure of the training datasets with sources and sizes" ], "weaknesses": [ "No safety results published for 4.2, and only general safety cautions", "No documented JSON or schema output", "The card's vLLM route loads a parser plugin from the repository", "Only one inference provider, and watsonx.ai doesn't list 4.2", "No knowledge cut-off, and Granite 4.1 3B carries no note that 4.2 replaced it" ], "agentNotes": [ "Start it with `ollama run granite4.2:3b`; set options.num_ctx in the request, since Ollama's default context is short and the model takes up to 131,072", "With vLLM 0.32.0 or later, serve `ibm-granite/granite-4.2-3b` with `--reasoning-parser granite_thinking_parser --tool-call-parser qwen3_coder --enable-auto-tool-choice`; that release has the parser built in, so the card's `--reasoning-parser-plugin ./granite_thinking_parser.py` isn't needed", "With SGLang 0.5.18 or later, `--reasoning-parser auto --tool-call-parser auto` resolve to IBM's recommended parsers, and the reasoning comes back in reasoning_content", "Use temperature 1.0 and top_p 0.95 for every mode, as IBM requires", "Pass `enable_thinking` false for direct answers or `low_effort` true for short reasoning, through chat_template_kwargs on an OpenAI-compatible server", "Earlier turns' thinking is stripped by default (`truncate_history_thinking`), so pass it false to keep reasoning in multi-turn history", "For JSON, use the runtime's guided decoding, since IBM documents no JSON mode for 4.2", "Verify the files with `python -m model_signing verify sigstore` and IBM's identity, per IBM's signature guide" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 59.8 } ], "editorialScores": { "capability": 21, "interface": 85, "reach": 95, "risk": 55, "upkeep": 92 }, "provenanceScore": 87, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run granite4.2:3b\n# or, with vLLM 0.32.0 or later:\nvllm serve ibm-granite/granite-4.2-3b --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\": \"granite4.2:3b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/granite-4-2-3b" }, "sameCompany": [ "granite-embedding-small-english-r2", "granite-guardian", "granite-switch" ], "weights": { "trainedBy": "IBM", "builtOn": "ibm-granite/granite-4.1-3b-base (IBM's own base checkpoint, post-trained; named on the card and in its metadata)", "params": "3.66B", "context": 131072, "goodFor": "Small dense reasoning model for tool calling, coding and multilingual chat, with thinking that is on, off or short per request.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "granite-4.2-3b-fp8", "url": "https://huggingface.co/ibm-granite/granite-4.2-3b-fp8", "who": "publisher", "precision": "fp8", "sizeGB": 4.19, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM" ] }, { "name": "granite-4.2-3b-nvfp4", "url": "https://huggingface.co/ibm-granite/granite-4.2-3b-nvfp4", "who": "publisher", "precision": "nvfp4", "sizeGB": 2.81, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM" ], "note": "GPTQ calibrated with LLM Compressor, per IBM's blog" }, { "name": "granite-4.2-3b-mxfp4", "url": "https://huggingface.co/ibm-granite/granite-4.2-3b-mxfp4", "who": "publisher", "precision": "mxfp4", "sizeGB": 2.71, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM" ], "note": "GPTQ calibrated with LLM Compressor, per IBM's blog" }, { "name": "granite-4.2-3b-Q8_0", "url": "https://huggingface.co/ibm-granite/granite-4.2-3b-GGUF/blob/main/granite-4.2-3b-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 3.89, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ] }, { "name": "granite-4.2-3b-Q4_K_M", "url": "https://huggingface.co/ibm-granite/granite-4.2-3b-GGUF/blob/main/granite-4.2-3b-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 2.24, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "IBM's GGUF repository also holds Q2_K to Q6_K, Q4_0, Q4_1, Q5_0, Q5_1 and BF16 files; Q4_0 is the smallest 4-bit GGUF at 2.13 GB" }, { "name": "granite-4.2-3b-q4-mlx", "url": "https://huggingface.co/ibm-granite/granite-4.2-3b-q4-mlx", "who": "publisher", "precision": "q4", "sizeGB": 2.07, "memoryGB": 8, "format": "mlx", "runsOn": [ "mlx-lm" ], "note": "The smallest build at 4-bit or better. IBM also publishes q8-mlx (3.90 GB) and bf16-mlx (7.33 GB)" }, { "name": "ollama granite4.2:3b", "url": "https://ollama.com/library/granite4.2:3b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 2.2, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M, 3.66B parameters, granite architecture, per Ollama's model page (digest 40577dc168a3). Size as Ollama shows it" }, { "name": "granite-4.2-3b", "url": "https://huggingface.co/ibm-granite/granite-4.2-3b", "who": "publisher", "precision": "bf16", "sizeGB": 7.33, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ] } ], "run": "ollama run granite4.2:3b", "runBuild": "ollama granite4.2:3b", "checked": "2026-10-21", "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-4.2-8b", "url": "https://huggingface.co/ibm-granite/granite-4.2-8b", "params": "8B (Hub safetensors count 8.79B)" }, { "name": "granite-4.2-30b", "url": "https://huggingface.co/ibm-granite/granite-4.2-30b", "params": "30B (Hub safetensors count 29.28B)" }, { "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, "unrestrictedCopies": 5 }, "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-4.2-3b/commits/main", "securityTxt": "valid", "checked": "2026-10-21", "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": 87 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/granite-4-2-3b.json" }, "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": "2026-10-20", "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": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "2.8/5 (4)", "b": "3.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "GLM-5.3-Flash scores 65.1 (B) on agent readiness against Granite 4.2 3B's 59.8 (C), and leads in 1 of 5 scored categories. Granite 4.2 3B leads on reach, agent interface and documentation and upkeep.", "question": "Which is better for AI agents, GLM-5.3-Flash or Granite 4.2 3B?" } ], "goodFor": [ { "aheadOn": [ "Capability, 75 against 21" ], "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, 95 against 60", "Agent interface, 85 against 70", "Documentation and upkeep, 92 against 58" ], "also": [ "Rated higher by the reviewer agents, 3.8 against 2.8 out of 5" ], "goodFor": "Agents on small hardware that want a permissive reasoning model for tool calls and coding, with thinking they can switch off", "slug": "granite-4-2-3b", "watchFor": "No safety results published for 4.2, and only general safety cautions" } ], "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-4-2-3b.json", "title": "Agents-A1-4B vs Granite 4.2 3B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-granite-4-2-3b" }, { "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-4-2-3b.json", "title": "Apertus v1.5 8B vs Granite 4.2 3B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-3b" }, { "json": 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vs Granite 4.2 3B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-granite-4-2-3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-5-3-flash.json", "title": "Gemma 4 E2B IT vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-granite-4-2-3b.json", "title": "Gemma 4 E2B IT vs Granite 4.2 3B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-granite-4-2-3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-5-3-flash.json", "title": "Gemma 4 E4B IT vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-granite-4-2-3b.json", "title": "Gemma 4 E4B IT vs Granite 4.2 3B", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-granite-4-2-3b" }, { "json": 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"https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-occamy-1-0.json", "title": "Granite 4.2 3B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-olmo-3-7b-think.json", "title": "Granite 4.2 3B vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-35b-a3b.json", "title": "Granite 4.2 3B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-397b.json", "title": "Granite 4.2 3B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-9b.json", "title": "Granite 4.2 3B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Granite 4.2 3B vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-4b-instruct-2507.json", "title": "Granite 4.2 3B vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-5-0-8b.json", "title": "Granite 4.2 3B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-5-122b-a10b.json", "title": "Granite 4.2 3B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-5-2b.json", "title": "Granite 4.2 3B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-5-397b-a17b.json", "title": "Granite 4.2 3B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-5-4b.json", "title": "Granite 4.2 3B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-5-9b.json", "title": "Granite 4.2 3B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-6-27b.json", "title": "Granite 4.2 3B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-6-35b-a3b.json", "title": "Granite 4.2 3B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-8-27b.json", "title": "Granite 4.2 3B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-8-flash-next.json", "title": "Granite 4.2 3B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Granite 4.2 3B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-coder-next.json", "title": "Granite 4.2 3B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Granite 4.2 3B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-vl-2b-instruct.json", "title": "Granite 4.2 3B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Granite 4.2 3B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-vl-32b-instruct.json", "title": "Granite 4.2 3B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-vl-4b-instruct.json", "title": "Granite 4.2 3B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-vl-8b-instruct.json", "title": "Granite 4.2 3B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwopus3-8-27b-flash-v2.json", "title": "Granite 4.2 3B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-smollm3-3b.json", "title": "Granite 4.2 3B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-spark-x2-6-4b.json", "title": "Granite 4.2 3B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-swift-1-5-qwen3-8-27b.json", "title": "Granite 4.2 3B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Granite 4.2 3B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-thinkingcap-qwen3-8-27b.json", "title": "Granite 4.2 3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "by": 54, "edge": "glm-5-3-flash", "glm-5-3-flash": 75, "granite-4-2-3b": 21, "key": "capability", "name": "Capability", "weight": 35 }, { "by": 35, "edge": "granite-4-2-3b", "glm-5-3-flash": 60, "granite-4-2-3b": 95, "key": "reach", "name": "Reach", "weight": 25 }, { "by": 0, "edge": "", "glm-5-3-flash": 55, "granite-4-2-3b": 55, "key": "risk", "name": "Risk", "weight": 20 }, { "by": 15, "edge": "granite-4-2-3b", "glm-5-3-flash": 70, "granite-4-2-3b": 85, "key": "interface", "name": "Agent interface", "weight": 10 }, { "by": 34, "edge": "granite-4-2-3b", "glm-5-3-flash": 58, "granite-4-2-3b": 92, "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "GLM-5.3-Flash scores 65.1 (B) on agent readiness against Granite 4.2 3B's 59.8 (C), and leads in 1 of 5 scored categories. Granite 4.2 3B leads on reach, agent interface and documentation and upkeep. Both do open-weight models you run yourself.", "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-4-2-3b": "A small reasoning cousin for tool calls, coding and multilingual chat that runs almost anywhere, from a 2.2 GB Ollama build to vLLM and SGLang, under plain Apache 2.0 with signed files and a dataset disclosure. Thinking turns off or shortens per request. It has no published safety results and no documented JSON mode." } }, "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-4-2-3b", "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-granite-4-2-3b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-granite-4-2-3b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-granite-4-2-3b.min.md" }, "markdown": "GLM-5.3-Flash scores 65.1 (B) on agent readiness against Granite 4.2 3B's 59.8 (C), and leads in 1 of 5 scored categories. Granite 4.2 3B 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 #16 of 100 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 3B: grade C, 59.8/100, rank #57 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/granite-4-2-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/granite-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 2870 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\nAhead on:\n- Capability, 75 against 21\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 3B (C)\n\nGood for: Agents on small hardware that want a permissive reasoning model for tool calls and coding, with thinking they can switch off\n\nAhead on:\n- Reach, 95 against 60\n- Agent interface, 85 against 70\n- Documentation and upkeep, 92 against 58\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.8 against 2.8 out of 5\n\nWatch for: No safety results published for 4.2, and only general safety cautions\n\n\n## Score by category\n\n| Category | Weight | GLM-5.3-Flash | Granite 4.2 3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 75 | 21 | GLM-5.3-Flash +54 |\n| Reach | 25% (25 this run) | 60 | 95 | Granite 4.2 3B +35 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 70 | 85 | Granite 4.2 3B +15 |\n| Documentation and upkeep | 10% (10 this run) | 58 | 92 | Granite 4.2 3B +34 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **65.1 · B** | **59.8 · C** | |\n\n## Facts side by side\n\n| Fact | GLM-5.3-Flash | Granite 4.2 3B |\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 | 2026-10-20 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 2.8/5 (4) | 3.8/5 (4) |\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 3B.** A small reasoning cousin for tool calls, coding and multilingual chat that runs almost anywhere, from a 2.2 GB Ollama build to vLLM and SGLang, under plain Apache 2.0 with signed files and a dataset disclosure. Thinking turns off or shortens per request. It has no published safety results and no documented JSON mode.\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 3B\n\n1. Start it with `ollama run granite4.2:3b`; set options.num_ctx in the request, since Ollama's default context is short and the model takes up to 131,072\n2. With vLLM 0.32.0 or later, serve `ibm-granite/granite-4.2-3b` with `--reasoning-parser granite_thinking_parser --tool-call-parser qwen3_coder --enable-auto-tool-choice`; that release has the parser built in, so the card's `--reasoning-parser-plugin ./granite_thinking_parser.py` isn't needed\n3. With SGLang 0.5.18 or later, `--reasoning-parser auto --tool-call-parser auto` resolve to IBM's recommended parsers, and the reasoning comes back in reasoning_content\n4. Use temperature 1.0 and top_p 0.95 for every mode, as IBM requires\n5. Pass `enable_thinking` false for direct answers or `low_effort` true for short reasoning, through chat_template_kwargs on an OpenAI-compatible server\n6. Earlier turns' thinking is stripped by default (`truncate_history_thinking`), so pass it false to keep reasoning in multi-turn history\n7. For JSON, use the runtime's guided decoding, since IBM documents no JSON mode for 4.2\n8. Verify the files with `python -m model_signing verify sigstore` and IBM's identity, per IBM's signature guide\n\n## Questions\n\n### Which is better for AI agents, GLM-5.3-Flash or Granite 4.2 3B?\n\nGLM-5.3-Flash scores 65.1 (B) on agent readiness against Granite 4.2 3B's 59.8 (C), and leads in 1 of 5 scored categories. Granite 4.2 3B 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-4-2-3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-5-3-flash-vs-granite-4-2-3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"glm-5-3-flash\", \"b\": \"granite-4-2-3b\"}`. From a terminal: `anchor compare glm-5-3-flash granite-4-2-3b`\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-4-2-3b.json\n\n## Other comparisons with GLM-5.3-Flash or Granite 4.2 3B\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 3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-granite-4-2-3b.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 3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-3b.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 3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-granite-4-2-3b.md)\n- [BigBang-v1 vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-5-3-flash.md)\n- [BigBang-v1 vs Granite 4.2 3B](https://www.anchorterminal.com/compare/bigbang-v1-vs-granite-4-2-3b.md)\n- [Cosmos-Reason2-2B vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-5-3-flash.md)\n- [Cosmos-Reason2-2B vs Granite 4.2 3B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-granite-4-2-3b.md)\n- [Darwin-36B-Opus vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-5-3-flash.md)\n- [Darwin-36B-Opus vs Granite 4.2 3B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-granite-4-2-3b.md)\n- [DeepSeek-V3.2 vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-5-3-flash.md)\n- [DeepSeek-V3.2 vs Granite 4.2 3B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-granite-4-2-3b.md)\n- [DeepSeek V4.1 Flash vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-5-3-flash.md)\n- [DeepSeek V4.1 Flash vs Granite 4.2 3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-granite-4-2-3b.md)\n- [DeepSeek-V4-Flash-0731 vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-5-3-flash.md)\n- [DeepSeek-V4-Flash-0731 vs Granite 4.2 3B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-granite-4-2-3b.md)\n- [DiffusionGemma 26B A4B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-5-3-flash.md)\n- [DiffusionGemma 26B A4B IT vs Granite 4.2 3B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-granite-4-2-3b.md)\n- [Gemma 4 12B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-5-3-flash.md)\n- [Gemma 4 12B IT vs Granite 4.2 3B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-granite-4-2-3b.md)\n- [Gemma 4 26B A4B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-5-3-flash.md)\n- [Gemma 4 26B A4B IT vs Granite 4.2 3B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-granite-4-2-3b.md)\n- [Gemma 4 31B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3-flash.md)\n- [Gemma 4 31B IT vs Granite 4.2 3B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-granite-4-2-3b.md)\n- [Gemma 4 E2B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-5-3-flash.md)\n- [Gemma 4 E2B IT vs Granite 4.2 3B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-granite-4-2-3b.md)\n- [Gemma 4 E4B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-5-3-flash.md)\n- [Gemma 4 E4B IT vs Granite 4.2 3B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-granite-4-2-3b.md)\n- [GLM-4.5-Air vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-5-3-flash.md)\n- [GLM-4.5-Air vs Granite 4.2 3B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-granite-4-2-3b.md)\n- [GLM-4.7-Flash vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-glm-5-3-flash.md)\n- [GLM-4.7-Flash vs Granite 4.2 3B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-granite-4-2-3b.md)\n- [GLM-5.3-Flash vs gpt-oss-120b](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-gpt-oss-120b.md)\n- [GLM-5.3-Flash vs gpt-oss-20b](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-gpt-oss-20b.md)\n- [GLM-5.3-Flash vs Hy3](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-hy3.md)\n- [GLM-5.3-Flash vs Inkling](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-inkling.md)\n- [GLM-5.3-Flash vs Inkling-Small](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-inkling-small.md)\n- [GLM-5.3-Flash vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [GLM-5.3-Flash vs Kimi K3](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-kimi-k3.md)\n- [GLM-5.3-Flash vs Kolibri-1](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-kolibri-1.md)\n- [GLM-5.3-Flash vs Laguna M.1](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-m-1.md)\n- [GLM-5.3-Flash vs Laguna S 2.1](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-s-2-1.md)\n- [GLM-5.3-Flash vs Laguna XS 2.1](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-xs-2-1.md)\n- [GLM-5.3-Flash vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm2-5-2-6b.md)\n- [GLM-5.3-Flash vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ling-3-0-tiny.md)\n- [GLM-5.3-Flash vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-mimo-v2-6-distill-qwen-9b.md)\n- [GLM-5.3-Flash vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minicpm-o-4-5.md)\n- [GLM-5.3-Flash vs MiniCPM5-2B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minicpm5-2b.md)\n- [GLM-5.3-Flash vs MiniMax-M2.5](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minimax-m2-5.md)\n- [GLM-5.3-Flash vs MiniMax-M2.7](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minimax-m2-7.md)\n- [GLM-5.3-Flash vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-muse-glimmer-30b.md)\n- [GLM-5.3-Flash vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [GLM-5.3-Flash vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-nano-30b-a3b.md)\n- [GLM-5.3-Flash vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-nano-4b.md)\n- [GLM-5.3-Flash vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [GLM-5.3-Flash vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-super-120b-a12b.md)\n- [GLM-5.3-Flash vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-ultra-550b-a55b.md)\n- [GLM-5.3-Flash vs occamy-1.0](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-occamy-1-0.md)\n- [GLM-5.3-Flash vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-olmo-3-7b-think.md)\n- [GLM-5.3-Flash vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-35b-a3b.md)\n- [GLM-5.3-Flash vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-397b.md)\n- [GLM-5.3-Flash vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-9b.md)\n- [GLM-5.3-Flash vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-30b-a3b-instruct-2507.md)\n- [GLM-5.3-Flash vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-4b-instruct-2507.md)\n- [GLM-5.3-Flash vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-0-8b.md)\n- [GLM-5.3-Flash vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-122b-a10b.md)\n- [GLM-5.3-Flash vs Qwen3.5-2B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-2b.md)\n- [GLM-5.3-Flash vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-397b-a17b.md)\n- [GLM-5.3-Flash vs Qwen3.5-4B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-4b.md)\n- [GLM-5.3-Flash vs Qwen3.5-9B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-9b.md)\n- [GLM-5.3-Flash vs Qwen3.6-27B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-6-27b.md)\n- [GLM-5.3-Flash vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-6-35b-a3b.md)\n- [GLM-5.3-Flash vs Qwen3.8-27B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-8-27b.md)\n- [GLM-5.3-Flash vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-8-flash-next.md)\n- [GLM-5.3-Flash vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-coder-30b-a3b-instruct.md)\n- [GLM-5.3-Flash vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-coder-next.md)\n- [GLM-5.3-Flash vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-omni-30b-a3b-instruct.md)\n- [GLM-5.3-Flash vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-2b-instruct.md)\n- [GLM-5.3-Flash vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-30b-a3b-instruct.md)\n- [GLM-5.3-Flash vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-32b-instruct.md)\n- [GLM-5.3-Flash vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-4b-instruct.md)\n- [GLM-5.3-Flash vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-8b-instruct.md)\n- [GLM-5.3-Flash vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwopus3-8-27b-flash-v2.md)\n- [GLM-5.3-Flash vs SmolLM3-3B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-smollm3-3b.md)\n- [GLM-5.3-Flash vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-spark-x2-6-4b.md)\n- [GLM-5.3-Flash vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-swift-1-5-qwen3-8-27b.md)\n- [GLM-5.3-Flash vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-swift-1-5-qwen3-8-flash-next.md)\n- [GLM-5.3-Flash vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-thinkingcap-qwen3-8-27b.md)\n- [GLM-5.3 vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/glm-5-3-vs-glm-5-3-flash.md)\n- [GLM-5.3 vs Granite 4.2 3B](https://www.anchorterminal.com/compare/glm-5-3-vs-granite-4-2-3b.md)\n- [gpt-oss-120b vs Granite 4.2 3B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-granite-4-2-3b.md)\n- [gpt-oss-20b vs Granite 4.2 3B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-granite-4-2-3b.md)\n- [Granite 4.2 3B vs Hy3](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-hy3.md)\n- [Granite 4.2 3B vs Inkling](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-inkling.md)\n- [Granite 4.2 3B vs Inkling-Small](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-inkling-small.md)\n- [Granite 4.2 3B vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Granite 4.2 3B vs Kimi K3](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-kimi-k3.md)\n- [Granite 4.2 3B vs Kolibri-1](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-kolibri-1.md)\n- [Granite 4.2 3B vs Laguna M.1](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-laguna-m-1.md)\n- [Granite 4.2 3B vs Laguna S 2.1](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-laguna-s-2-1.md)\n- [Granite 4.2 3B vs Laguna XS 2.1](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-laguna-xs-2-1.md)\n- [Granite 4.2 3B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm2-5-2-6b.md)\n- [Granite 4.2 3B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ling-3-0-tiny.md)\n- [Granite 4.2 3B vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Granite 4.2 3B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minicpm-o-4-5.md)\n- [Granite 4.2 3B vs MiniCPM5-2B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minicpm5-2b.md)\n- [Granite 4.2 3B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minimax-m2-5.md)\n- [Granite 4.2 3B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minimax-m2-7.md)\n- [Granite 4.2 3B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-muse-glimmer-30b.md)\n- [Granite 4.2 3B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Granite 4.2 3B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nemotron-3-nano-30b-a3b.md)\n- [Granite 4.2 3B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nemotron-3-nano-4b.md)\n- [Granite 4.2 3B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Granite 4.2 3B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nemotron-3-super-120b-a12b.md)\n- [Granite 4.2 3B vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nemotron-3-ultra-550b-a55b.md)\n- [Granite 4.2 3B vs occamy-1.0](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-occamy-1-0.md)\n- [Granite 4.2 3B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-olmo-3-7b-think.md)\n- [Granite 4.2 3B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-35b-a3b.md)\n- [Granite 4.2 3B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-397b.md)\n- [Granite 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