{
  "data": {
    "a": {
      "slug": "cola2",
      "name": "Cola2",
      "vendor": "ByteDance (Seed)",
      "vendorUrl": "https://github.com/ByteDance-Seed",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Cola2 is a research checkpoint from ByteDance Seed, a base language model of 8.8B parameters that generates text through continuous latents with a diffusion transformer and a token decoder. Not a chat model, and the weights carry no stated licence.",
      "url": "https://www.anchorterminal.com/tools/cola2",
      "markdownUrl": "https://www.anchorterminal.com/tools/cola2.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/cola2.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/cola2.json",
      "dns": "cola2.grade.anchorterminal.com",
      "repo": "https://huggingface.co/ByteDance-Seed/Cola2",
      "license": "None stated for the weights (the code is Apache 2.0)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "ByteDance-Seed/Cola2"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download, but nothing says what you may do with the weights. The card says the code is Apache 2.0 and that the weights remain subject to their respective terms, without naming them. Running it yourself costs the hardware: 35.1 GB of F32 files, loaded as BF16 in the card's example (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://github.com/ByteDance-Seed/Cola2",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "base-model",
        "research",
        "diffusion",
        "continuous-latent",
        "no-licence",
        "safetensors",
        "custom-code",
        "self-hosted"
      ],
      "lastRelease": "2026-11-06",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 33.9,
        "grade": "F",
        "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": 113,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 20,
          "reach": 25,
          "risk": 40,
          "upkeep": 58
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A research release, and an honest one: ByteDance Seed says it's a base model with an unconverged latent stage, scoring below Llama-3-8B on its own table. It's here for people studying continuous-latent generation. Agents can't use it as a model: 32-token completions, no chat or tools, the repository's own code to load it, and no licence on the weights.",
        "bestFor": "Researchers working on latent or diffusion language models who want a checkpoint to study",
        "strengths": [
          "A documented new architecture with a paper and a reproduction protocol",
          "Evaluation data and checksums ship with the weights",
          "A security policy that points to ByteDance's security centre",
          "The model card says plainly what it isn't for"
        ],
        "weaknesses": [
          "No licence stated for the weights",
          "A base model: no chat, no tools, 32 new tokens by default",
          "Loads only with the repository's own Python, on Transformers 4.x",
          "35 GB of F32 files and no quantised build",
          "Below Llama-3-8B on the card's own eight-task table",
          "No stated context length or cut-off"
        ],
        "agentNotes": [
          "Don't plan to use this as your model. It completes short prompts and wasn't tuned to follow instructions",
          "Don't use it commercially on the strength of this listing. The weights have no stated licence",
          "If you study it, read modeling_cola2.py first: trust_remote_code runs it",
          "Pin Transformers below 5, as the card requires",
          "Pass a seeded torch.Generator to generate(). Output varies between runs even with greedy decoding",
          "Check the download with `sha256sum -c SHA256SUMS`"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "F",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 33.9
          }
        ],
        "editorialScores": {
          "interface": 20,
          "reach": 25,
          "risk": 40,
          "upkeep": 58
        },
        "provenanceScore": 58,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "pip install \"torch\u003e=2.2\" \"transformers\u003e=4.51,\u003c5\" \"safetensors\u003e=0.4\" accelerate\n# then AutoModel.from_pretrained(\"ByteDance-Seed/Cola2\", trust_remote_code=True, torch_dtype=torch.bfloat16)\n# trust_remote_code runs the three Python files in the repository. Read them first",
        "http": "# No server. The model runs in process through Transformers, and the GitHub repository has a\n# command-line chat script (scripts/run_chat.sh) and a benchmark runner"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/cola2"
      },
      "weights": {
        "trainedBy": "ByteDance Seed",
        "builtOn": "The decoder and encoder embedding are initialised from Qwen3-4B-Base, per the GitHub model card",
        "params": "8.8B",
        "goodFor": "Research on continuous-latent text generation: short completions, cloze and multiple-choice experiments, and reproducing the paper's numbers.",
        "runtimes": [],
        "builds": [
          {
            "name": "Cola2 (F32 safetensors)",
            "url": "https://huggingface.co/ByteDance-Seed/Cola2/tree/main",
            "who": "publisher",
            "precision": "f32",
            "sizeGB": 35.07,
            "memoryGB": 48,
            "format": "safetensors",
            "note": "Eight shards in 32-bit floats. The card's example loads them as BF16"
          }
        ],
        "run": "python -c \"from transformers import AutoModel; AutoModel.from_pretrained('ByteDance-Seed/Cola2', trust_remote_code=True)\"",
        "runBuild": "Cola2 (F32 safetensors)",
        "checked": "2026-11-10",
        "minMemoryGB": 48
      },
      "family": {
        "lab": "ByteDance (Seed)",
        "family": "Cola",
        "url": "https://www.anchorterminal.com/open-weights/#bytedance-seed",
        "others": [
          {
            "name": "Cola-DLM",
            "url": "https://huggingface.co/ByteDance-Seed/Cola-DLM"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "ByteDance, named in the GitHub repository's security policy. The legal entity wasn't looked up",
        "domain": "bytedance.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/ByteDance-Seed/Cola2/commits/main",
        "securityTxt": "unknown",
        "checked": "2026-11-10",
        "notes": [
          "The files are in the ByteDance-Seed organisation on Hugging Face and the code is in the ByteDance-Seed organisation on GitHub; each links the other.",
          "No licence is stated for the weights, so there are no terms to read.",
          "The domain's registration date and security.txt weren't looked up. The GitHub repository's SECURITY.md points to ByteDance's security centre.",
          "The changelog is the commit history, six commits on 6 November 2026."
        ],
        "score": 58
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/cola2.json"
    },
    "answer": "Granite 4.2 3B scores 59.8 (C) on agent readiness against Cola2's 33.9 (F), and leads in every scored category.",
    "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",
      "dns": "granite-4-2-3b.grade.anchorterminal.com",
      "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": 1518,
        "categoryRank": 72,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 21,
          "interface": 85,
          "reach": 95,
          "risk": 55,
          "upkeep": 92
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "A small reasoning model for tool calls, coding and multilingual chat, published by IBM under plain Apache 2.0 with signed files and a dataset disclosure, and run by llama.cpp, Ollama, vLLM, SGLang and Transformers from a 2.2 GB Ollama build. 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": "0.7",
            "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": 88,
        "checked": {
          "categories": {
            "capability": "2026-11-04",
            "interface": "2026-11-04",
            "reach": "2026-11-04",
            "risk": "2026-11-04",
            "upkeep": "2026-11-04"
          },
          "last": "2026-11-04",
          "oldest": "2026-11-04",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run 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-speech-5-0-2b",
        "ibm-watson-stt",
        "granite-switch",
        "granite-4-2-30b",
        "granite-4-2-8b"
      ],
      "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)",
            "listing": "https://www.anchorterminal.com/tools/granite-4-2-8b"
          },
          {
            "name": "granite-4.2-30b",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-30b",
            "params": "30B (Hub safetensors count 29.28B)",
            "listing": "https://www.anchorterminal.com/tools/granite-4-2-30b"
          },
          {
            "name": "granite-4.1-8b-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.1-8b-base",
            "params": "8B (Hub safetensors count 8.38B)"
          },
          {
            "name": "granite-4.1-8b",
            "url": "https://huggingface.co/ibm-granite/granite-4.1-8b",
            "params": "8B (Hub safetensors count 8.79B)"
          },
          {
            "name": "granite-4.1-3b-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.1-3b-base",
            "params": "3B (Hub safetensors count 3.40B)"
          },
          {
            "name": "granite-4.1-3b",
            "url": "https://huggingface.co/ibm-granite/granite-4.1-3b",
            "params": "3B (Hub safetensors count 3.40B)"
          },
          {
            "name": "granite-4.1-30b-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.1-30b-base",
            "params": "30B (Hub safetensors count 28.87B)"
          },
          {
            "name": "granite-4.1-30b",
            "url": "https://huggingface.co/ibm-granite/granite-4.1-30b",
            "params": "30B (Hub safetensors count 28.87B)"
          },
          {
            "name": "granite-4.0-h-350m-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-350m-base",
            "params": "350M (Hub safetensors count 340M)"
          },
          {
            "name": "granite-4.0-h-350m",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-350m",
            "params": "350M (Hub safetensors count 340M)"
          },
          {
            "name": "granite-4.0-h-1b-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-1b-base",
            "params": "1B (Hub safetensors count 1.46B)"
          },
          {
            "name": "granite-4.0-h-1b",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-1b",
            "params": "1B (Hub safetensors count 1.46B)"
          },
          {
            "name": "granite-4.0-350m-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-350m-base",
            "params": "350M (Hub safetensors count 352M)"
          },
          {
            "name": "granite-4.0-350m",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-350m",
            "params": "350M (Hub safetensors count 352M)"
          },
          {
            "name": "granite-4.0-1b-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-1b-base",
            "params": "1B (Hub safetensors count 1.63B)"
          },
          {
            "name": "granite-4.0-1b",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-1b",
            "params": "1B (Hub safetensors count 1.63B)"
          },
          {
            "name": "granite-4.0-micro-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-micro-base",
            "params": "3.40B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-micro",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-micro",
            "params": "3.40B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-h-tiny-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-tiny-base",
            "params": "6.94B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-h-tiny",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-tiny",
            "params": "6.94B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-h-small-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-small-base",
            "params": "32.21B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-h-small",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-small",
            "params": "32.21B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-h-micro-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-micro-base",
            "params": "3.19B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-h-micro",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-micro",
            "params": "3.19B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-tiny-preview",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-tiny-preview",
            "params": "6.67B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-tiny-base-preview",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-tiny-base-preview",
            "params": "6.67B (Hub safetensors count; the name states none)"
          }
        ],
        "olderCount": 64,
        "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": 88
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/granite-4-2-3b.json",
      "live": {
        "slug": "granite-4-2-3b",
        "securityTxt": {
          "url": "https://ibm.com/.well-known/security.txt",
          "state": "valid",
          "expires": "2026-12-10T00:00:00+00:00",
          "checkedAt": "2026-11-10T16:09:13.66976132Z"
        },
        "domain": {
          "domain": "ibm.com",
          "registered": "1986-03-19",
          "source": "https://rdap.verisign.com/com/v1/domain/ibm.com",
          "checkedAt": "2026-11-08T15:04:54.376665564Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/ibm-granite/granite-4.2-3b/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-10T16:22:46.723800083Z",
            "changedAt": "2026-11-10T16:22:46.723800083Z",
            "fingerprint": "9d41684a25f6"
          }
        ],
        "updatedAt": "2026-11-10T16:22:46.723800083Z"
      }
    },
    "edges": [
      {
        "by": 70,
        "category": "reach",
        "leads": "granite-4-2-3b",
        "name": "Reach",
        "reasons": {
          "a": "Memory 15, the only build is the F32 safetensors set, 35.07 GB, and 35.07 GB × 1.2 = 42.1 GB, within 64 GB. Loaded as BF16, as the card's example does, it needs about half, but the line reads the file size. Runtimes 0, none of the six runs it in a released version. Builds 0, one set of weights and no quantised build. Terms 0, no licence for the weights. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and nothing names an API that serves it. 15 + 0 + 0 + 0 + 10 + 0 = 25.",
          "b": "Memory earns the full 30, since IBM's 4-bit MLX build is 2.07 GB and Ollama's Q4_K_M build 2.2 GB, about 2.5 to 2.6 GB with the fifth added. Five named runtimes run the granite architecture in released versions or their own libraries, so runtimes take the cap of 25, though LM Studio has no 4.2 entry. IBM's FP8, FP4, GGUF and MLX builds earn 15. Apache 2.0 with nothing added gives terms 15 and the ungated repositories download 10. Hosted too is 0, since only DeepInfra serves it and watsonx.ai stops at 4.1."
        },
        "scores": {
          "a": 25,
          "b": 95
        }
      },
      {
        "by": 65,
        "category": "interface",
        "leads": "granite-4-2-3b",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer. Tool-calling format 0, none. Trained tool calling 0, not trained for tool calling. Structured output 0, Nothing about JSON or schema-constrained output. Reasoning control 0, no reasoning mode. Context 0, no context length is stated. 20 + 0 + 0 + 0 + 0 + 0 = 20.",
          "b": "The template ships in the repository (20). The card shows how tools are declared and how calls come back, and names qwen3_coder for vLLM and SGLang (25). The template renders several calls in a turn (20). JSON output isn't documented for 4.2 (0). Thinking turns off or goes to low effort per request, with the reasoning returned apart in SGLang (10), and 131,072 tokens are native (10)."
        },
        "scores": {
          "a": 20,
          "b": 85
        }
      },
      {
        "by": 34,
        "category": "upkeep",
        "leads": "granite-4-2-3b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 8, Intended use and limitations (the out-of-scope list, and the unconverged latent model) and the architecture are on the card and the GitHub model card it links. Training data 15, the GitHub model card names the source (OLMo 2 pretraining text, retokenised) and the mix by stage with token counts, 346.03B in all. Last change 20, All six commits are from 6 November 2026, four days before this check, the last one updating a checksum in the README. Responsiveness 0, no discussions on the repository, so nobody has asked anything yet and the line reads as none answered, as settled in calibration. Version policy 5, A first release. File health 10, Config, files and card agree: F32 weights, a float32 config and a card that says to load them as bfloat16, with SHA256SUMS to check the download. 8 + 15 + 20 + 0 + 5 + 10 = 58.",
          "b": "The card covers everything but a knowledge cut-off (17). IBM's disclosure lists the datasets with sources and sizes (20). The index file changed on 20 October 2026, 15 days before this check, and nothing in IBM's 4.2 repositories or the GitHub repository changed since (20). The two questions asked on IBM's 3B repositories have an IBM reply, and no new thread was opened after 21 October (20). Granite 4.1 3B stays in its own repository with no note (5), and the files agree with each other (10)."
        },
        "scores": {
          "a": 58,
          "b": 92
        }
      },
      {
        "by": 15,
        "category": "risk",
        "leads": "granite-4-2-3b",
        "name": "Risk",
        "reasons": {
          "a": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the ByteDance-Seed organisation, which the ByteDance-Seed GitHub repository for the model links to (10). Safety evaluation 0, no safety results. Agent guidance 5, General cautions in the GitHub model card: not instruction-tuned or safety-aligned, not for use as an assistant, for facts or advice, or for high-impact decisions about people. Licence risk 0, With no licence for the weights there is nothing to hold ByteDance to. Security programme 5, the GitHub repository's SECURITY.md sends reports to ByteDance's security centre and says how they're assessed (5). 15 + 15 + 0 + 5 + 0 + 5 = 40.",
          "b": "File safety gets 15 of 25, safetensors only, less 10 because the card's vLLM command loads the repository's parser plugin. Provenance gets 15 for files in IBM's own organisation with hashes and a sigstore signature. Safety evaluation is 0, since 4.2 publishes no safety results. Agent guidance gets 5 for general cautions and the advice to add Granite Guardian. Apache 2.0 keeps licence risk at 15, and IBM's security.txt gives 5 of the security programme's 10."
        },
        "scores": {
          "a": 40,
          "b": 55
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "ByteDance (Seed)",
        "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": "None stated for the weights (the code is Apache 2.0)",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-11-06",
        "b": "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": "none",
        "b": "3.8/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Granite 4.2 3B scores 59.8 (C) on agent readiness against Cola2's 33.9 (F), and leads in every scored category.",
        "question": "Which is better for AI agents, Cola2 or Granite 4.2 3B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Researchers working on latent or diffusion language models who want a checkpoint to study",
        "slug": "cola2",
        "watchFor": "No licence stated for the weights"
      },
      {
        "aheadOn": [
          "Reach, 95 against 25",
          "Risk, 55 against 40",
          "Agent interface, 85 against 20",
          "Documentation and upkeep, 92 against 58"
        ],
        "also": null,
        "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"
      }
    ],
    "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-cola2.json",
        "title": "Agents-A1-4B vs Cola2",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-cola2"
      },
      {
        "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-cola2.json",
        "title": "Apertus v1.5 8B vs Cola2",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cola2"
      },
      {
        "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": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-cola2.json",
        "title": "Apodex-1.1-mini vs Cola2",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-cola2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-granite-4-2-3b.json",
        "title": "Apodex-1.1-mini vs Granite 4.2 3B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-granite-4-2-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-cola2.json",
        "title": "Arete-27B vs Cola2",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-cola2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-3b.json",
        "title": "Arete-27B vs Granite 4.2 3B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-cola2.json",
        "title": "BigBang-v1 vs Cola2",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-cola2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-granite-4-2-3b.json",
        "title": "BigBang-v1 vs Granite 4.2 3B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-granite-4-2-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-cola2.json",
        "title": "Carbon-X vs Cola2",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-cola2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-granite-4-2-3b.json",
        "title": "Carbon-X vs Granite 4.2 3B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-granite-4-2-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-cosmos-reason2-2b.json",
        "title": "Cola2 vs Cosmos-Reason2-2B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-cosmos-reason2-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-darwin-36b-opus.json",
        "title": "Cola2 vs Darwin-36B-Opus",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-darwin-36b-opus"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-deepseek-v3-2.json",
        "title": "Cola2 vs DeepSeek-V3.2",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-deepseek-v3-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-deepseek-v4-1-flash.json",
        "title": "Cola2 vs DeepSeek V4.1 Flash",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-deepseek-v4-1-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-deepseek-v4-flash-0731.json",
        "title": "Cola2 vs DeepSeek-V4-Flash-0731",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-deepseek-v4-flash-0731"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-devstral-small-2.json",
        "title": "Cola2 vs Devstral Small 2",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-devstral-small-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-diffusiongemma-26b-a4b-it.json",
        "title": "Cola2 vs DiffusionGemma 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-diffusiongemma-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-exaone-4-5-33b.json",
        "title": "Cola2 vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-exaone-4-5-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-gemma-4-12b-it.json",
        "title": "Cola2 vs Gemma 4 12B IT",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-gemma-4-12b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-gemma-4-26b-a4b-it.json",
        "title": "Cola2 vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-gemma-4-31b-it.json",
        "title": "Cola2 vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-gemma-4-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-gemma-4-e2b-it.json",
        "title": "Cola2 vs Gemma 4 E2B IT",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-gemma-4-e2b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-gemma-4-e4b-it.json",
        "title": "Cola2 vs Gemma 4 E4B IT",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-gemma-4-e4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-glm-4-5-air.json",
        "title": "Cola2 vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-glm-4-5-air"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-glm-4-7-flash.json",
        "title": "Cola2 vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-glm-4-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-glm-5-3.json",
        "title": "Cola2 vs GLM-5.3",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-glm-5-3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-glm-5-3-flash.json",
        "title": "Cola2 vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-glm-5-3-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-gpt-oss-120b.json",
        "title": "Cola2 vs gpt-oss-120b",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-gpt-oss-120b"
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        "json": "https://www.anchorterminal.com/compare/cola2-vs-gpt-oss-20b.json",
        "title": "Cola2 vs gpt-oss-20b",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-gpt-oss-20b"
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        "json": "https://www.anchorterminal.com/compare/cola2-vs-granite-4-2-30b.json",
        "title": "Cola2 vs Granite 4.2 30B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-granite-4-2-30b"
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-vl-8b-instruct"
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        "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"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-recursive-flint.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-recursive-flint"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ripple-65b-preview.json",
        "title": "Granite 4.2 3B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ripple-65b-preview"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-sensenova-loop-9b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-sensenova-loop-9b-preview"
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-smollm3-3b"
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        "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"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-step-3-7-flash.json",
        "title": "Granite 4.2 3B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-step-3-7-flash"
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        "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"
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        "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"
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      {
        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-unlimited-ocr-2.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-unlimited-ocr-2"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-wevisone-vl-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-wevisone-vl-4b.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-wevisone-vl-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-zaya1-8b.json",
        "title": "Granite 4.2 3B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-zaya1-8b"
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    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 70,
        "cola2": 25,
        "edge": "granite-4-2-3b",
        "granite-4-2-3b": 95,
        "key": "reach",
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 15,
        "cola2": 40,
        "edge": "granite-4-2-3b",
        "granite-4-2-3b": 55,
        "key": "risk",
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 65,
        "cola2": 20,
        "edge": "granite-4-2-3b",
        "granite-4-2-3b": 85,
        "key": "interface",
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 34,
        "cola2": 58,
        "edge": "granite-4-2-3b",
        "granite-4-2-3b": 92,
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Granite 4.2 3B scores 59.8 (C) on agent readiness against Cola2's 33.9 (F), and leads in every scored category. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "cola2": "A research release, and an honest one: ByteDance Seed says it's a base model with an unconverged latent stage, scoring below Llama-3-8B on its own table. It's here for people studying continuous-latent generation. Agents can't use it as a model: 32-token completions, no chat or tools, the repository's own code to load it, and no licence on the weights.",
      "granite-4-2-3b": "A small reasoning model for tool calls, coding and multilingual chat, published by IBM under plain Apache 2.0 with signed files and a dataset disclosure, and run by llama.cpp, Ollama, vLLM, SGLang and Transformers from a 2.2 GB Ollama build. Thinking turns off or shortens per request. It has no published safety results and no documented JSON mode."
    }
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    "json": "https://www.anchorterminal.com/compare/cola2-vs-granite-4-2-3b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
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  "markdown": "Granite 4.2 3B scores 59.8 (C) on agent readiness against Cola2's 33.9 (F), and leads in every scored category. Both do open-weight models you run yourself.\n\n- Cola2: grade F, 33.9/100, rank #160 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/cola2.md · JSON https://www.anchorterminal.com/api/v1/tools/cola2.json\n- Granite 4.2 3B: grade C, 59.8/100, rank #92 of 160 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 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Cola2 (F)\n\nGood for: Researchers working on latent or diffusion language models who want a checkpoint to study\n\nWatch for: No licence stated for the weights\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 25\n- Risk, 55 against 40\n- Agent interface, 85 against 20\n- Documentation and upkeep, 92 against 58\n\nWatch for: No safety results published for 4.2, and only general safety cautions\n\n\n## Why the edge\n\n### Reach: Granite 4.2 3B +70\n\n- Cola2 (25): Memory 15, the only build is the F32 safetensors set, 35.07 GB, and 35.07 GB × 1.2 = 42.1 GB, within 64 GB. Loaded as BF16, as the card's example does, it needs about half, but the line reads the file size. Runtimes 0, none of the six runs it in a released version. Builds 0, one set of weights and no quantised build. Terms 0, no licence for the weights. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and nothing names an API that serves it. 15 + 0 + 0 + 0 + 10 + 0 = 25.\n- Granite 4.2 3B (95): Memory earns the full 30, since IBM's 4-bit MLX build is 2.07 GB and Ollama's Q4_K_M build 2.2 GB, about 2.5 to 2.6 GB with the fifth added. Five named runtimes run the granite architecture in released versions or their own libraries, so runtimes take the cap of 25, though LM Studio has no 4.2 entry. IBM's FP8, FP4, GGUF and MLX builds earn 15. Apache 2.0 with nothing added gives terms 15 and the ungated repositories download 10. Hosted too is 0, since only DeepInfra serves it and watsonx.ai stops at 4.1.\n\n### Agent interface: Granite 4.2 3B +65\n\n- Cola2 (20): Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer. Tool-calling format 0, none. Trained tool calling 0, not trained for tool calling. Structured output 0, Nothing about JSON or schema-constrained output. Reasoning control 0, no reasoning mode. Context 0, no context length is stated. 20 + 0 + 0 + 0 + 0 + 0 = 20.\n- Granite 4.2 3B (85): The template ships in the repository (20). The card shows how tools are declared and how calls come back, and names qwen3_coder for vLLM and SGLang (25). The template renders several calls in a turn (20). JSON output isn't documented for 4.2 (0). Thinking turns off or goes to low effort per request, with the reasoning returned apart in SGLang (10), and 131,072 tokens are native (10).\n\n### Documentation and upkeep: Granite 4.2 3B +34\n\n- Cola2 (58): Model card 8, Intended use and limitations (the out-of-scope list, and the unconverged latent model) and the architecture are on the card and the GitHub model card it links. Training data 15, the GitHub model card names the source (OLMo 2 pretraining text, retokenised) and the mix by stage with token counts, 346.03B in all. Last change 20, All six commits are from 6 November 2026, four days before this check, the last one updating a checksum in the README. Responsiveness 0, no discussions on the repository, so nobody has asked anything yet and the line reads as none answered, as settled in calibration. Version policy 5, A first release. File health 10, Config, files and card agree: F32 weights, a float32 config and a card that says to load them as bfloat16, with SHA256SUMS to check the download. 8 + 15 + 20 + 0 + 5 + 10 = 58.\n- Granite 4.2 3B (92): The card covers everything but a knowledge cut-off (17). IBM's disclosure lists the datasets with sources and sizes (20). The index file changed on 20 October 2026, 15 days before this check, and nothing in IBM's 4.2 repositories or the GitHub repository changed since (20). The two questions asked on IBM's 3B repositories have an IBM reply, and no new thread was opened after 21 October (20). Granite 4.1 3B stays in its own repository with no note (5), and the files agree with each other (10).\n\n### Risk: Granite 4.2 3B +15\n\n- Cola2 (40): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the ByteDance-Seed organisation, which the ByteDance-Seed GitHub repository for the model links to (10). Safety evaluation 0, no safety results. Agent guidance 5, General cautions in the GitHub model card: not instruction-tuned or safety-aligned, not for use as an assistant, for facts or advice, or for high-impact decisions about people. Licence risk 0, With no licence for the weights there is nothing to hold ByteDance to. Security programme 5, the GitHub repository's SECURITY.md sends reports to ByteDance's security centre and says how they're assessed (5). 15 + 15 + 0 + 5 + 0 + 5 = 40.\n- Granite 4.2 3B (55): File safety gets 15 of 25, safetensors only, less 10 because the card's vLLM command loads the repository's parser plugin. Provenance gets 15 for files in IBM's own organisation with hashes and a sigstore signature. Safety evaluation is 0, since 4.2 publishes no safety results. Agent guidance gets 5 for general cautions and the advice to add Granite Guardian. Apache 2.0 keeps licence risk at 15, and IBM's security.txt gives 5 of the security programme's 10.\n\n## Score by category\n\n| Category | Weight | Cola2 | Granite 4.2 3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 25 | 95 | Granite 4.2 3B +70 |\n| Risk | 20% (20 this run) | 40 | 55 | Granite 4.2 3B +15 |\n| Agent interface | 10% (10 this run) | 20 | 85 | Granite 4.2 3B +65 |\n| Documentation and upkeep | 10% (10 this run) | 58 | 92 | Granite 4.2 3B +34 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **33.9 · F** | **59.8 · C** | |\n\n## Facts side by side\n\n| Fact | Cola2 | Granite 4.2 3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | ByteDance (Seed) | 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 | None stated for the weights (the code is Apache 2.0) | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-06 | 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| Agent reviews | none | 3.8/5 (4) |\n\n## Verdicts\n\n**Cola2.** A research release, and an honest one: ByteDance Seed says it's a base model with an unconverged latent stage, scoring below Llama-3-8B on its own table. It's here for people studying continuous-latent generation. Agents can't use it as a model: 32-token completions, no chat or tools, the repository's own code to load it, and no licence on the weights.\n\n**Granite 4.2 3B.** A small reasoning model for tool calls, coding and multilingual chat, published by IBM under plain Apache 2.0 with signed files and a dataset disclosure, and run by llama.cpp, Ollama, vLLM, SGLang and Transformers from a 2.2 GB Ollama build. 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### Cola2\n\n1. Don't plan to use this as your model. It completes short prompts and wasn't tuned to follow instructions\n2. Don't use it commercially on the strength of this listing. The weights have no stated licence\n3. If you study it, read modeling_cola2.py first: trust_remote_code runs it\n4. Pin Transformers below 5, as the card requires\n5. Pass a seeded torch.Generator to generate(). Output varies between runs even with greedy decoding\n6. Check the download with `sha256sum -c SHA256SUMS`\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, Cola2 or Granite 4.2 3B?\n\nGranite 4.2 3B scores 59.8 (C) on agent readiness against Cola2's 33.9 (F), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/cola2-vs-granite-4-2-3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/cola2-vs-granite-4-2-3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"cola2\", \"b\": \"granite-4-2-3b\"}`. From a terminal: `anchor compare cola2 granite-4-2-3b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/cola2.json and https://www.anchorterminal.com/api/v1/tools/granite-4-2-3b.json\n\n## Other comparisons with Cola2 or Granite 4.2 3B\n\n- [Agents-A1-4B vs Cola2](https://www.anchorterminal.com/compare/agents-a1-4b-vs-cola2.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 Cola2](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cola2.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 Cola2](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-cola2.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- [Arete-27B vs Cola2](https://www.anchorterminal.com/compare/arete-27b-vs-cola2.md)\n- [Arete-27B vs Granite 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    "title": "Cola2 vs Granite 4.2 3B for AI agents in 2026: scores and prices",
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    "url": "https://www.anchorterminal.com/compare/cola2-vs-granite-4-2-3b"
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