{ "data": { "a": { "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": 1350, "categoryRank": 54, "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-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": 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": "unknown", "checkedAt": "2026-11-04T15:54:37.176687825Z" }, "domain": { "domain": "ibm.com", "registered": "1986-03-19", "source": "https://rdap.verisign.com/com/v1/domain/ibm.com", "checkedAt": "2026-11-01T14:15:47.742238816Z" }, "pages": [ { "url": "https://huggingface.co/ibm-granite/granite-4.2-3b/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-04T08:25:48.469912642Z", "changedAt": "2026-11-04T08:25:48.469912642Z", "fingerprint": "543b74a693a3" } ], "updatedAt": "2026-11-04T15:54:37.176687825Z" } }, "answer": "Granite 4.2 3B scores 59.8 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 3 of 4 scored categories.", "b": { "slug": "ripple-65b-preview", "name": "Ripple-65B Preview", "vendor": "Ricursive Intelligence", "vendorUrl": "https://www.ricursive.com/ripple", "kind": "weights", "category": "open-weights", "summary": "Ricursive Intelligence's 65B dense model, pretrained from scratch and post-trained for agentic coding, on Hugging Face in BF16 and NVFP4 under Apache 2.0. Its custom architecture needs Ricursive's own model code to run.", "url": "https://www.anchorterminal.com/tools/ripple-65b-preview", "markdownUrl": "https://www.anchorterminal.com/tools/ripple-65b-preview.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ripple-65b-preview.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.json", "dns": "ripple-65b-preview.grade.anchorterminal.com", "repo": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "ricursiveAI/Ripple-65B-Preview" }, { "registry": "huggingface", "name": "ricursiveAI/Ripple-65B-Preview-NVFP4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither the BF16 nor the NVFP4 repository is gated (checked 2026-11-04).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 129.6 GB for the BF16 files and 57.2 GB for the NVFP4 build by the Hub's file pages. Ricursive's launch post names Together AI, GMI Cloud and OpenRouter as hosts, but Together's model page says coming soon (checked 2026-11-04).", "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 2026-11-04).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-04" }, "docsUrl": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "reasoning", "tool-calling", "coding", "apache-2.0", "safetensors", "remote-code", "self-hosted", "new" ], "lastRelease": "2026-11-04", "graded": true, "anchor": { "graded": true, "score": 53.3, "grade": "D", "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": 1350, "categoryRank": 78, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 75, "reach": 45, "risk": 55, "upkeep": 49 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-04" }, "negative": 0, "verdict": "A dense 65B coding model from a new lab, pretrained from scratch and released today under Apache 2.0, that Ricursive says leads U.S. open-weight models on Terminal-Bench 2.1. Nobody has checked that yet, and the custom architecture runs only through Ricursive's own SGLang plugin on Blackwell GPUs, with a 57 GB NVFP4 build as the smallest.", "bestFor": "Teams with Blackwell GPUs or a DGX Spark who want an Apache 2.0 coding model to try for terminal and agentic coding work, and can run the publisher's code to serve it.", "strengths": [ "Apache 2.0 with nothing added, ungated, from the organisation Ricursive's site links to", "A tools-aware chat template with several calls a turn, and a server that returns reasoning, content and tool calls separately", "Trained for long context at 2M-token sequences, with 1M stated", "Base model and a speculative-decoding draft published beside the instruct model", "Safetensors only, with a checksum on every file" ], "weaknesses": [ "No released runtime runs the architecture, so running it means Ricursive's Python in SGLang 0.5.19 or Transformers' remote code", "The smallest build is 57.2 GB NVFP4, for NVIDIA Blackwell only, and there is no GGUF", "No independent benchmark yet, and the top-ranking claim rests on Ricursive's own Terminal-Bench 2.1 run", "Thinking can't be turned off or budgeted", "No safety guidance for agent use, no security contact and no knowledge cut-off" ], "agentNotes": [ "Run Ricursive's launcher from inference/server with `uv run --script serve.py` on two B200 GPUs; it listens on 127.0.0.1:30000 with a 16K context by default", "The launcher turns on `--reasoning-parser ripple` and `--tool-call-parser ripple`; without them tool calls come back as raw text inside tool_call tags", "For `tool_choice: \"required\"` set `strict: true` and `additionalProperties: false` on each function, which the README says stopped extra argument fields", "Leave room in `max_tokens` for reasoning as well as the answer, since thinking is always on", "Use chat_template.jinja, not the template inside tokenizer_config.json, which has no tool support" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 53.3 } ], "editorialScores": { "interface": 75, "reach": 45, "risk": 55, "upkeep": 49 }, "provenanceScore": 36, "checked": { "categories": { "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": "hf download ricursiveAI/Ripple-65B-Preview --local-dir ./ripple-65b\nhf download ricursiveAI/Ripple-65B-Preview-DFlash2 --local-dir ./ripple-dflash2\ncd ripple-65b/inference/server\nCUDA_VISIBLE_DEVICES=0,1 uv run --script serve.py \\\n --model-path ../.. \\\n --speculative-draft-model-path ../../../ripple-dflash2", "http": "curl http://127.0.0.1:30000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"ripple\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello\"}], \"max_tokens\": 128}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/ripple-65b-preview" }, "weights": { "trainedBy": "Ricursive Intelligence", "params": "65B", "context": 1048576, "goodFor": "A dense 65B model post-trained for agentic coding and terminal work, with several tool calls a turn and a long context, for teams with Blackwell GPUs or a DGX Spark.", "runtimes": [], "builds": [ { "name": "Ripple-65B-Preview-NVFP4", "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 57.2, "memoryGB": 69, "format": "safetensors", "note": "NVFP4 MLP weights and activations with BF16 attention, 6 shards summed from the Hub's file page. For NVIDIA Blackwell, with Ricursive's DGX Spark loader. The checkpoint was replaced in place on release day" }, { "name": "Ripple-65B-Preview", "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview", "who": "publisher", "precision": "bf16", "sizeGB": 129.6, "memoryGB": 160, "format": "safetensors", "note": "16 shards summed from the Hub's file page. Served with Ricursive's SGLang 0.5.19 plugin on two B200 GPUs" } ], "run": "CUDA_VISIBLE_DEVICES=0,1 uv run --script serve.py --model-path /path/to/Ripple-65B-Preview --speculative-draft-model-path /path/to/Ripple-65B-Preview-DFlash2", "runBuild": "Ripple-65B-Preview", "checked": "2026-11-04", "minMemoryGB": 69 }, "area": "models", "provenance": { "legalEntity": "Ricursive Intelligence, Inc.", "domain": "ricursive.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview/blob/main/LICENSE", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-11-04", "notes": [ "Every page on www.ricursive.com ends with \"© 2026 Ricursive Intelligence, Inc.\". The site links the Hugging Face organisation ricursiveAI (fullname Ricursive Intelligence), which publishes the four Ripple repositories. RDAP wasn't queried, by the run's rules.", "The Apache 2.0 LICENSE in the repository governs the weights and stands in for terms of service. The site's own legal pages (/legal and /legal/terms, titled Model Attribution Terms) render their text by script only and couldn't be read.", "Nothing is hosted by Ricursive, so no privacy policy or status page is scored.", "No changelog. The repositories' commit messages are uploads and one in-place swap of the NVFP4 checkpoint on release day.", "www.ricursive.com/.well-known/security.txt is 404 on 4 November 2026, and the only contact on the card and site is a general one." ], "score": 36 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/ripple-65b-preview.json" }, "edges": [ { "by": 50, "category": "reach", "leads": "granite-4-2-3b", "name": "Reach", "reasons": { "a": "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.", "b": "Memory 10 of 30, the smallest 4-bit build is Ricursive's NVFP4 at 57.2 GB, 68.7 GB with a fifth added, which is over 64 GB and fits our reference machine. Runtimes 0, no released runtime runs the ricursive architecture with its own code; Ricursive's SGLang plugin and Transformers' remote code don't count. Builds 10, one quantised precision (NVFP4) from Ricursive. Terms 15, Apache 2.0 with nothing added. Download 10, not gated. Hosted 0, Ricursive serves no API, Hugging Face maps no provider, Together AI's page says coming soon, and GMI Cloud's and OpenRouter's pages weren't read." }, "scores": { "a": 95, "b": 45 } }, { "by": 43, "category": "upkeep", "leads": "granite-4-2-3b", "name": "Documentation and upkeep", "reasons": { "a": "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).", "b": "Model card 14 of 20, no limitations and no knowledge cut-off. Training data 5, a general description with token counts and no cut-off. Last change 20, released today. Responsiveness 0, no discussions yet, read as none answered. Version policy 5, a first release, with the NVFP4 checkpoint replaced in place on release day under a commit note. File health 5, tokenizer_config.json carries a second, tool-less chat template and a 131,072-token limit against the card's 1M." }, "scores": { "a": 92, "b": 49 } }, { "by": 10, "category": "interface", "leads": "granite-4-2-3b", "name": "Agent interface", "reasons": { "a": "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).", "b": "Chat template 20, chat_template.jinja in the repository. Tool-calling format 25, Ricursive's server README says how tools are declared and calls come back, with the ripple parser in SGLang 0.5.19. Trained tool calling 20, one or more calls a turn and agentic-coding RL. Structured output 0, not documented. Reasoning control 0, the template has no thinking switch or effort levels. Context 10, long-context training at 2M-token sequences for a stated 1M." }, "scores": { "a": 85, "b": 75 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "IBM", "b": "Ricursive Intelligence", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-20", "b": "2026-11-04", "name": "Last release" }, { "a": "no document linked", "b": "not read yet", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "3.8/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Granite 4.2 3B scores 59.8 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 3 of 4 scored categories.", "question": "Which is better for AI agents, Granite 4.2 3B or Ripple-65B Preview?" } ], "goodFor": [ { "aheadOn": [ "Reach, 95 against 45", "Agent interface, 85 against 75", "Documentation and upkeep, 92 against 49" ], "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" }, { "aheadOn": null, "also": null, "goodFor": "Teams with Blackwell GPUs or a DGX Spark who want an Apache 2.0 coding model to try for terminal and agentic coding work, and can run the publisher's code to serve it.", "slug": "ripple-65b-preview", "watchFor": "No released runtime runs the architecture, so running it means Ricursive's Python in SGLang 0.5.19 or Transformers' remote code" } ], "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-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/agents-a1-4b-vs-ripple-65b-preview.json", "title": "Agents-A1-4B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ripple-65b-preview" }, { "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/apertus-v1-5-8b-vs-ripple-65b-preview.json", "title": "Apertus v1.5 8B vs Ripple-65B Preview", "url": 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Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-ripple-65b-preview.json", "title": "Qwen3.8-Flash-Next vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-ripple-65b-preview.json", "title": "Qwen3-Coder-Next vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-VL-2B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-VL-32B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-VL-4B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-VL-8B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-ripple-65b-preview.json", "title": "Qwopus3.8-27B-Flash-V2 vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-ripple-65b-preview.json", "title": "Recursive Flint vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-smollm3-3b.json", "title": "Ripple-65B Preview vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-spark-x2-6-4b.json", "title": "Ripple-65B Preview vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-27b.json", "title": "Ripple-65B Preview vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Ripple-65B Preview vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-thinkingcap-qwen3-8-27b.json", "title": "Ripple-65B Preview vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-unlimited-ocr-2.json", "title": "Ripple-65B Preview vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-unlimited-ocr-2" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 50, "edge": "granite-4-2-3b", "granite-4-2-3b": 95, "key": "reach", "name": "Reach", "ripple-65b-preview": 45, "weight": 25 }, { "by": 0, "edge": "", "granite-4-2-3b": 55, "key": "risk", "name": "Risk", "ripple-65b-preview": 55, "weight": 20 }, { "by": 10, "edge": "granite-4-2-3b", "granite-4-2-3b": 85, "key": "interface", "name": "Agent interface", "ripple-65b-preview": 75, "weight": 10 }, { "by": 43, "edge": "granite-4-2-3b", "granite-4-2-3b": 92, "key": "upkeep", "name": "Documentation and upkeep", "ripple-65b-preview": 49, "weight": 10 } ], "summary": "Granite 4.2 3B scores 59.8 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "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.", "ripple-65b-preview": "A dense 65B coding model from a new lab, pretrained from scratch and released today under Apache 2.0, that Ricursive says leads U.S. open-weight models on Terminal-Bench 2.1. Nobody has checked that yet, and the custom architecture runs only through Ricursive's own SGLang plugin on Blackwell GPUs, with a 57 GB NVFP4 build as the smallest." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ripple-65b-preview", "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ripple-65b-preview.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ripple-65b-preview.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ripple-65b-preview.min.md" }, "markdown": "Granite 4.2 3B scores 59.8 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.\n\n- Granite 4.2 3B: grade C, 59.8/100, rank #67 of 121 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- Ripple-65B Preview: grade D, 53.3/100, rank #103 of 121 open-weight models. Markdown https://www.anchorterminal.com/tools/ripple-65b-preview.md · JSON https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.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 3674 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\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 45\n- Agent interface, 85 against 75\n- Documentation and upkeep, 92 against 49\n\nWatch for: No safety results published for 4.2, and only general safety cautions\n\n### Ripple-65B Preview (D)\n\nGood for: Teams with Blackwell GPUs or a DGX Spark who want an Apache 2.0 coding model to try for terminal and agentic coding work, and can run the publisher's code to serve it.\n\nWatch for: No released runtime runs the architecture, so running it means Ricursive's Python in SGLang 0.5.19 or Transformers' remote code\n\n\n## Why the edge\n\n### Reach: Granite 4.2 3B +50\n\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- Ripple-65B Preview (45): Memory 10 of 30, the smallest 4-bit build is Ricursive's NVFP4 at 57.2 GB, 68.7 GB with a fifth added, which is over 64 GB and fits our reference machine. Runtimes 0, no released runtime runs the ricursive architecture with its own code; Ricursive's SGLang plugin and Transformers' remote code don't count. Builds 10, one quantised precision (NVFP4) from Ricursive. Terms 15, Apache 2.0 with nothing added. Download 10, not gated. Hosted 0, Ricursive serves no API, Hugging Face maps no provider, Together AI's page says coming soon, and GMI Cloud's and OpenRouter's pages weren't read.\n\n### Documentation and upkeep: Granite 4.2 3B +43\n\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- Ripple-65B Preview (49): Model card 14 of 20, no limitations and no knowledge cut-off. Training data 5, a general description with token counts and no cut-off. Last change 20, released today. Responsiveness 0, no discussions yet, read as none answered. Version policy 5, a first release, with the NVFP4 checkpoint replaced in place on release day under a commit note. File health 5, tokenizer_config.json carries a second, tool-less chat template and a 131,072-token limit against the card's 1M.\n\n### Agent interface: Granite 4.2 3B +10\n\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- Ripple-65B Preview (75): Chat template 20, chat_template.jinja in the repository. Tool-calling format 25, Ricursive's server README says how tools are declared and calls come back, with the ripple parser in SGLang 0.5.19. Trained tool calling 20, one or more calls a turn and agentic-coding RL. Structured output 0, not documented. Reasoning control 0, the template has no thinking switch or effort levels. Context 10, long-context training at 2M-token sequences for a stated 1M.\n\n## Score by category\n\n| Category | Weight | Granite 4.2 3B | Ripple-65B Preview | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 45 | Granite 4.2 3B +50 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 85 | 75 | Granite 4.2 3B +10 |\n| Documentation and upkeep | 10% (10 this run) | 92 | 49 | Granite 4.2 3B +43 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **59.8 · C** | **53.3 · D** | |\n\n## Facts side by side\n\n| Fact | Granite 4.2 3B | Ripple-65B Preview |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | IBM | Ricursive Intelligence |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-20 | 2026-11-04 |\n| Terms last updated | no document linked | not read yet |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 3.8/5 (4) | none |\n\n## Verdicts\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**Ripple-65B Preview.** A dense 65B coding model from a new lab, pretrained from scratch and released today under Apache 2.0, that Ricursive says leads U.S. open-weight models on Terminal-Bench 2.1. Nobody has checked that yet, and the custom architecture runs only through Ricursive's own SGLang plugin on Blackwell GPUs, with a 57 GB NVFP4 build as the smallest.\n\n## Before you call either\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### Ripple-65B Preview\n\n1. Run Ricursive's launcher from inference/server with `uv run --script serve.py` on two B200 GPUs; it listens on 127.0.0.1:30000 with a 16K context by default\n2. The launcher turns on `--reasoning-parser ripple` and `--tool-call-parser ripple`; without them tool calls come back as raw text inside tool_call tags\n3. For `tool_choice: \"required\"` set `strict: true` and `additionalProperties: false` on each function, which the README says stopped extra argument fields\n4. Leave room in `max_tokens` for reasoning as well as the answer, since thinking is always on\n5. Use chat_template.jinja, not the template inside tokenizer_config.json, which has no tool support\n\n## Questions\n\n### Which is better for AI agents, Granite 4.2 3B or Ripple-65B Preview?\n\nGranite 4.2 3B scores 59.8 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 3 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ripple-65b-preview.json, and with the fewest tokens: https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ripple-65b-preview.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"granite-4-2-3b\", \"b\": \"ripple-65b-preview\"}`. From a terminal: `anchor compare granite-4-2-3b ripple-65b-preview`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/granite-4-2-3b.json and https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.json\n\n## Other comparisons with Granite 4.2 3B or Ripple-65B Preview\n\n- [Agents-A1-4B vs Granite 4.2 3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-granite-4-2-3b.md)\n- [Agents-A1-4B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ripple-65b-preview.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- [Apertus v1.5 8B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ripple-65b-preview.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- [Apodex-1.1-mini vs Ripple-65B 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