{ "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": 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" } }, "answer": "Granite 4.2 3B scores 59.8 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 3 of 4 scored categories.", "b": { "slug": "qwen-agentworld-35b-a3b", "name": "Qwen-AgentWorld-35B-A3B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen-AgentWorld-35B-A3B is Alibaba's open-weight language world model, a 35B mixture of experts with 3B active that simulates agent environments by predicting the next observation across seven domains, published in BF16 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen-agentworld-35b-a3b.json", "dns": "qwen-agentworld-35b-a3b.grade.anchorterminal.com", "repo": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen-AgentWorld-35B-A3B" } ], "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 and use, commercial use included, under Apache 2.0, with a LICENSE file in the repository. Running it yourself costs the hardware, 69.3 GB for the BF16 files, or 22.1 GB for Unsloth's 4-bit GGUF (checked 2026-11-10).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "world-model", "agent-simulation", "reasoning", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-06-25", "graded": true, "anchor": { "graded": true, "score": 52.1, "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": 1518, "categoryRank": 104, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 50, "reach": 55, "risk": 55, "upkeep": 41 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A specialist, not a chat model. It plays the environment an agent acts in, across seven domains, so you can train or test an agent without the real thing. Alibaba ships BF16 only, which fits a 128 GB machine, and hasn't answered a discussion on it or changed the files since June.", "bestFor": "Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine", "strengths": [ "Simulates seven kinds of agent environment in one model, with the system prompts for each published", "Apache 2.0, ungated, safetensors only", "Runs in vLLM, SGLang, Transformers and llama.cpp from their releases", "Fits a 128 GB machine in BF16, and 32 GB from Unsloth's 4-bit GGUF", "A 262,144-token context for long interaction histories" ], "weaknesses": [ "Not a general assistant, and the card doesn't describe using it to call tools", "No quantised build from Alibaba, so the GGUF route depends on a third party", "No reply from Alibaba on any of 10 discussions, and no change to the files since 25 June 2026", "No limitations, safety results or security contact on the card", "Its results are on Alibaba's own benchmark" ], "agentNotes": [ "Use it as the environment, not the agent. Give it the domain's system prompt from Alibaba's GitHub repository, the history and the agent's action, and it returns the predicted observation", "With vLLM pass `--language-model-only`, or startup fails on the missing vision weights", "Use temperature 0.6, top_p 0.95 and top_k 20, and allow up to 32,768 output tokens, as the card recommends", "Keep at least 128K of context, since the simulation depends on the history", "Thinking is on by default and comes back through the qwen3 reasoning parser", "A simulated observation is a prediction. Check anything that matters against the real environment" ], "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": 52.1 } ], "editorialScores": { "interface": 50, "reach": 55, "risk": 55, "upkeep": 41 }, "provenanceScore": 66, "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": "# vLLM, as the card gives it (four GPUs)\nvllm serve Qwen/Qwen-AgentWorld-35B-A3B --port 8000 --tensor-parallel-size 4 \\\n --max-model-len 262144 --reasoning-parser qwen3 --language-model-only --trust-remote-code\n# or, with llama.cpp and Unsloth's GGUF, a third party's conversion:\nllama-server -hf unsloth/Qwen-AgentWorld-35B-A3B-GGUF:UD-Q4_K_M --jinja", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"Qwen/Qwen-AgentWorld-35B-A3B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"temperature\": 0.6}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen-agentworld-35b-a3b" }, "sameCompany": [ "qwen3-embedding-0-6b", "qwen3-embedding-4b", "qwen3-embedding-8b", "qwen3-vl-embedding-2b", "qwen3-vl-embedding-8b", "qwen3-asr-0-6b", "qwen3-asr-1-7b", "qwen3-tts-1-7b-customvoice", "qwen3-30b-a3b-instruct-2507", "qwen3-4b-instruct-2507", "qwen3-coder-30b-a3b-instruct", "qwen3-coder-next", "qwen3-omni-30b-a3b-instruct", "qwen3-vl-2b-instruct", "qwen3-vl-30b-a3b-instruct", "qwen3-vl-32b-instruct", "qwen3-vl-4b-instruct", "qwen3-vl-8b-instruct", "qwen3-5-0-8b", "qwen3-5-122b-a10b", "qwen3-5-2b", "qwen3-5-397b-a17b", "qwen3-5-4b", "qwen3-5-9b", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen)", "builtOn": "Qwen3.5-35B-A3B-Base, named on the card and in its metadata", "params": "34.7B", "active": "3B", "context": 262144, "goodFor": "A simulator of agent environments (MCP tools, search, terminal, software engineering, Android, web and OS) that predicts the next observation, for training and testing agents without the real environment.", "runtimes": [ "vLLM", "SGLang", "Transformers", "llama.cpp" ], "builds": [ { "name": "unsloth Qwen-AgentWorld-35B-A3B-GGUF (UD-Q4_K_M)", "url": "https://huggingface.co/unsloth/Qwen-AgentWorld-35B-A3B-GGUF", "who": "unsloth", "precision": "q4_k_m", "sizeGB": 22.13, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From Unsloth, a third party's conversion and not Alibaba's. 22.13 GB of UD-Q4_K_M weights, from the Hub's model information on 10 November 2026. Reach doesn't count it" }, { "name": "unsloth Qwen-AgentWorld-35B-A3B-GGUF (Q8_0)", "url": "https://huggingface.co/unsloth/Qwen-AgentWorld-35B-A3B-GGUF", "who": "unsloth", "precision": "q8_0", "sizeGB": 36.9, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From Unsloth, a third party's conversion and not Alibaba's. 36.90 GB of Q8_0 weights, from the Hub's model information on 10 November 2026. Reach doesn't count it" }, { "name": "Qwen-AgentWorld-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B", "who": "publisher", "precision": "bf16", "sizeGB": 69.32, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ], "note": "The only build Alibaba publishes. Language weights only" } ], "run": "llama-server -hf unsloth/Qwen-AgentWorld-35B-A3B-GGUF:UD-Q4_K_M --jinja", "runBuild": "unsloth Qwen-AgentWorld-35B-A3B-GGUF (UD-Q4_K_M)", "checked": "2026-11-10", "minMemoryGB": 32 }, "family": { "lab": "Alibaba (Qwen)", "family": "Qwen-AgentWorld", "url": "https://www.anchorterminal.com/open-weights/#qwen", "olderCount": 109 }, "area": "models", "provenance": { "legalEntity": "Alibaba Cloud (named in the licence's copyright line)", "domain": "qwen.ai", "domainRegistered": "2023-08-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The LICENSE file names Alibaba Cloud, as the Qwen3.6-35B-A3B listing records for the organisation.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, nine commits between 22 and 25 June 2026, the upload and updates to the card.", "The Qwen3.6-35B-A3B listing found no security.txt on qwen.ai. unchecked on 10 November." ], "score": 66 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.json" }, "edges": [ { "by": 51, "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 11, the card gives intended use (simulating agent environments), architecture, parameters and context length. Training data 5, A general description without a cut-off, the three training stages and a statement that no outputs from external API services were included. Last change 10, the last commit was on 25 June 2026, an update to the card, 138 days before this check, past 90 days and within 180. Responsiveness 0, 10 discussions that aren't pull requests, opened between 24 June and 5 July 2026. Version policy 5, A first release, with nothing replaced. File health 10, Config, both copies of the template and the card agree on the architecture and the context. 11 + 5 + 10 + 0 + 5 + 10 = 41." }, "scores": { "a": 92, "b": 41 } }, { "by": 40, "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, Alibaba's only build is the BF16 safetensors, 69.32 GB, and 69.32 GB × 1.2 = 83.2 GB, above 64 GB and inside the reference machine's GPU share. Runtimes 20, Four released runtimes run the architecture, vLLM 0.33.0 (Qwen3_5MoeForConditionalGeneration), SGLang 0.5.23, Transformers 5.22.2 (qwen3_5_moe) and llama.cpp b12242 (its Qwen conversion code). Builds 0, Alibaba publishes the BF16 weights only. Terms 15, Apache 2.0, an OSI licence, in the metadata and as a LICENSE file, with no use policy or other terms added. Download 10, the repository isn't gated, so the files download with no account and no terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026). 10 + 20 + 0 + 15 + 10 + 0 = 55." }, "scores": { "a": 95, "b": 55 } }, { "by": 35, "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, the same as the template in tokenizer_config.json. Tool-calling format 10, only the template says how tools are declared and how calls come back. Trained tool calling 10, the template renders several tool_call blocks in one turn, but the card presents the model as a simulator of what tools and environments return, not as a tool caller. Structured output 0, no JSON or JSON Schema output documented for the model. Reasoning control 0, the card says thinking is on by default and names the qwen3 reasoning parser, but doesn't say how to turn thinking off or budget it. Context 10, 262,144 tokens native per the card and the text config. 20 + 10 + 10 + 0 + 0 + 10 = 50." }, "scores": { "a": 85, "b": 50 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "IBM", "b": "Alibaba (Qwen team)", "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": "2026-06-25", "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": "3.8/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Granite 4.2 3B scores 59.8 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 3 of 4 scored categories.", "question": "Which is better for AI agents, Granite 4.2 3B or Qwen-AgentWorld-35B-A3B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 95 against 55", "Agent interface, 85 against 50", "Documentation and upkeep, 92 against 41" ], "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": "Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine", "slug": "qwen-agentworld-35b-a3b", "watchFor": "Not a general assistant, and the card doesn't describe using it to call tools" } ], "history": 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Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen-agentworld-35b-a3b.json", "title": "MiniCPM-V 4.7 35B-A3B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen-agentworld-35b-a3b.json", "title": "MiniCPM5-2B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen-agentworld-35b-a3b.json", "title": "MiniMax-M2.5 vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen-agentworld-35b-a3b.json", "title": "MiniMax-M2.7 vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen-agentworld-35b-a3b.json", "title": "MiniMax-M3.1-Flash vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen-agentworld-35b-a3b.json", "title": "Mistral Large 4 vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen-agentworld-35b-a3b.json", "title": "Muse Glimmer 30B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen-agentworld-35b-a3b.json", "title": "Nanbeige4.2-3B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen-agentworld-35b-a3b.json", "title": "Nanbeige4.3-33B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen-agentworld-35b-a3b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen-agentworld-35b-a3b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen-agentworld-35b-a3b.json", "title": "Nemotron 3 Nano 30B A3B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen-agentworld-35b-a3b.json", "title": "Nemotron 3 Nano 4B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen-agentworld-35b-a3b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen-agentworld-35b-a3b.json", "title": "Nemotron 3 Super 120B A12B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen-agentworld-35b-a3b.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen-agentworld-35b-a3b.json", "title": "North Mini Code 1.0 vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen-agentworld-35b-a3b.json", "title": "occamy-1.0 vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen-agentworld-35b-a3b.json", "title": "Olmo 3 7B Think vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen-agentworld-35b-a3b.json", "title": "Ornith-1.5-122B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen-agentworld-35b-a3b.json", "title": "Ornith-1.5-35B-A3B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen-agentworld-35b-a3b.json", "title": "Ornith-1.5-397B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen-agentworld-35b-a3b.json", "title": "Ornith-1.5-9B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen-agentworld-35b-a3b.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-4b-instruct-2507.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-0-8b.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-122b-a10b.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-2b.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-397b-a17b.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-4b.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-9b.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-6-27b.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-6-35b-a3b.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-8-27b.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-8-flash-next.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-coder-next.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-2b-instruct.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-32b-instruct.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-8b-instruct.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-recursive-flint.json", "title": "Qwen-AgentWorld-35B-A3B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-ripple-65b-preview.json", "title": "Qwen-AgentWorld-35B-A3B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-sensenova-loop-9b-preview.json", "title": "Qwen-AgentWorld-35B-A3B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-smollm3-3b.json", "title": "Qwen-AgentWorld-35B-A3B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-spark-x2-6-4b.json", "title": "Qwen-AgentWorld-35B-A3B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-step-3-7-flash.json", "title": "Qwen-AgentWorld-35B-A3B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen-AgentWorld-35B-A3B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen-AgentWorld-35B-A3B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen-AgentWorld-35B-A3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-unlimited-ocr-2.json", "title": "Qwen-AgentWorld-35B-A3B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-2b.json", "title": "Qwen-AgentWorld-35B-A3B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-4b.json", "title": "Qwen-AgentWorld-35B-A3B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-zaya1-8b.json", "title": "Qwen-AgentWorld-35B-A3B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 40, "edge": "granite-4-2-3b", "granite-4-2-3b": 95, "key": "reach", "name": "Reach", "qwen-agentworld-35b-a3b": 55, "weight": 25 }, { "by": 0, "edge": "", "granite-4-2-3b": 55, "key": "risk", "name": "Risk", "qwen-agentworld-35b-a3b": 55, "weight": 20 }, { "by": 35, "edge": "granite-4-2-3b", "granite-4-2-3b": 85, "key": "interface", "name": "Agent interface", "qwen-agentworld-35b-a3b": 50, "weight": 10 }, { "by": 51, "edge": "granite-4-2-3b", "granite-4-2-3b": 92, "key": "upkeep", "name": "Documentation and upkeep", "qwen-agentworld-35b-a3b": 41, "weight": 10 } ], "summary": "Granite 4.2 3B scores 59.8 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (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.", "qwen-agentworld-35b-a3b": "A specialist, not a chat model. It plays the environment an agent acts in, across seven domains, so you can train or test an agent without the real thing. Alibaba ships BF16 only, which fits a 128 GB machine, and hasn't answered a discussion on it or changed the files since June." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen-agentworld-35b-a3b", "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen-agentworld-35b-a3b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen-agentworld-35b-a3b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen-agentworld-35b-a3b.min.md" }, "markdown": "Granite 4.2 3B scores 59.8 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (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 #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- Qwen-AgentWorld-35B-A3B: grade D, 52.1/100, rank #141 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen-agentworld-35b-a3b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Granite 4.2 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 55\n- Agent interface, 85 against 50\n- Documentation and upkeep, 92 against 41\n\nWatch for: No safety results published for 4.2, and only general safety cautions\n\n### Qwen-AgentWorld-35B-A3B (D)\n\nGood for: Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine\n\nWatch for: Not a general assistant, and the card doesn't describe using it to call tools\n\n\n## Why the edge\n\n### Documentation and upkeep: Granite 4.2 3B +51\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- Qwen-AgentWorld-35B-A3B (41): Model card 11, the card gives intended use (simulating agent environments), architecture, parameters and context length. Training data 5, A general description without a cut-off, the three training stages and a statement that no outputs from external API services were included. Last change 10, the last commit was on 25 June 2026, an update to the card, 138 days before this check, past 90 days and within 180. Responsiveness 0, 10 discussions that aren't pull requests, opened between 24 June and 5 July 2026. Version policy 5, A first release, with nothing replaced. File health 10, Config, both copies of the template and the card agree on the architecture and the context. 11 + 5 + 10 + 0 + 5 + 10 = 41.\n\n### Reach: Granite 4.2 3B +40\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- Qwen-AgentWorld-35B-A3B (55): Memory 10, Alibaba's only build is the BF16 safetensors, 69.32 GB, and 69.32 GB × 1.2 = 83.2 GB, above 64 GB and inside the reference machine's GPU share. Runtimes 20, Four released runtimes run the architecture, vLLM 0.33.0 (Qwen3_5MoeForConditionalGeneration), SGLang 0.5.23, Transformers 5.22.2 (qwen3_5_moe) and llama.cpp b12242 (its Qwen conversion code). Builds 0, Alibaba publishes the BF16 weights only. Terms 15, Apache 2.0, an OSI licence, in the metadata and as a LICENSE file, with no use policy or other terms added. Download 10, the repository isn't gated, so the files download with no account and no terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026). 10 + 20 + 0 + 15 + 10 + 0 = 55.\n\n### Agent interface: Granite 4.2 3B +35\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- Qwen-AgentWorld-35B-A3B (50): Chat template 20, chat_template.jinja in the repository, the same as the template in tokenizer_config.json. Tool-calling format 10, only the template says how tools are declared and how calls come back. Trained tool calling 10, the template renders several tool_call blocks in one turn, but the card presents the model as a simulator of what tools and environments return, not as a tool caller. Structured output 0, no JSON or JSON Schema output documented for the model. Reasoning control 0, the card says thinking is on by default and names the qwen3 reasoning parser, but doesn't say how to turn thinking off or budget it. Context 10, 262,144 tokens native per the card and the text config. 20 + 10 + 10 + 0 + 0 + 10 = 50.\n\n## Score by category\n\n| Category | Weight | Granite 4.2 3B | Qwen-AgentWorld-35B-A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 55 | Granite 4.2 3B +40 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 85 | 50 | Granite 4.2 3B +35 |\n| Documentation and upkeep | 10% (10 this run) | 92 | 41 | Granite 4.2 3B +51 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **59.8 · C** | **52.1 · D** | |\n\n## Facts side by side\n\n| Fact | Granite 4.2 3B | Qwen-AgentWorld-35B-A3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | IBM | Alibaba (Qwen team) |\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 | 2026-06-25 |\n| Terms last updated | no document linked | no document linked |\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**Qwen-AgentWorld-35B-A3B.** A specialist, not a chat model. It plays the environment an agent acts in, across seven domains, so you can train or test an agent without the real thing. Alibaba ships BF16 only, which fits a 128 GB machine, and hasn't answered a discussion on it or changed the files since June.\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### Qwen-AgentWorld-35B-A3B\n\n1. Use it as the environment, not the agent. Give it the domain's system prompt from Alibaba's GitHub repository, the history and the agent's action, and it returns the predicted observation\n2. With vLLM pass `--language-model-only`, or startup fails on the missing vision weights\n3. Use temperature 0.6, top_p 0.95 and top_k 20, and allow up to 32,768 output tokens, as the card recommends\n4. Keep at least 128K of context, since the simulation depends on the history\n5. Thinking is on by default and comes back through the qwen3 reasoning parser\n6. A simulated observation is a prediction. Check anything that matters against the real environment\n\n## Questions\n\n### Which is better for AI agents, Granite 4.2 3B or Qwen-AgentWorld-35B-A3B?\n\nGranite 4.2 3B scores 59.8 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (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-qwen-agentworld-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen-agentworld-35b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"granite-4-2-3b\", \"b\": \"qwen-agentworld-35b-a3b\"}`. 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