{ "data": { "a": { "slug": "inkling", "name": "Inkling", "vendor": "Thinking Machines Lab", "vendorUrl": "https://thinkingmachines.ai", "kind": "weights", "category": "open-weights", "summary": "Inkling is Thinking Machines Lab's large open-weight mixture-of-experts model, 975B total and 41B active parameters, taking text, images and audio and returning text, with adjustable thinking effort, published in BF16 and NVFP4 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/inkling", "markdownUrl": "https://www.anchorterminal.com/tools/inkling.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/inkling.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/inkling.json", "dns": "inkling.grade.anchorterminal.com", "repo": "https://huggingface.co/thinkingmachines/Inkling", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "thinkingmachines/Inkling" }, { "registry": "huggingface", "name": "thinkingmachines/Inkling-NVFP4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-21). Thinking Machines' Model Acceptable Use Policy, linked from the card, says that downloading or using the weights binds you to it.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 with Thinking Machines' Model Acceptable Use Policy on top. Running it yourself costs the hardware, 1,904.8 GB for the BF16 files and 592.0 GB for the NVFP4 build. Thinking Machines' Tinker serves it as a beta serverless inference API at $1.00 per million input tokens and $4.05 per million output tokens (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-02" }, "docsUrl": "https://thinkingmachines.ai/model-card/inkling/", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "audio", "reasoning", "tool-calling", "apache-2.0", "safetensors", "nvfp4", "self-hosted" ], "lastRelease": "2026-07-23", "graded": true, "anchor": { "graded": true, "score": 64.7, "grade": "B", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 14, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 60, "interface": 85, "reach": 50, "risk": 70, "upkeep": 87 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-02" }, "negative": 0, "verdict": "Thinking Machines' large multimodal generalist, stronger than Inkling-Small on factual recall by its own numbers. It needs about 710 GB of datacentre GPU memory even in NVFP4, so most agents will use a host such as Together AI or Tinker. The main risks are a use policy that binds on download and can change, and no outside safety results.", "bestFor": "Agents calling a hosted open multimodal model with audio input, or teams with a multi-GPU datacentre node who want to run it themselves.", "strengths": [ "Three inference providers and Thinking Machines' own Tinker host it today", "Safetensors only, with a checksum on every LFS file in the publisher's own organisation", "Thinking effort set per request, several tool calls a turn and a 1M-token context", "Released support in Transformers, vLLM and SGLang, with inkling tool and reasoning parsers", "Thinking Machines answered 8 of the 12 questions and reports in its discussions", "A training-content summary with sources, mix and a July 2026 collection cut-off" ], "weaknesses": [ "The smallest build Thinking Machines publishes needs about 710 GB, beyond any single workstation", "Apache 2.0 comes with an acceptable use policy that binds on download and can change at any time", "No release of ggml-org's llama.cpp, Ollama or LM Studio runs it", "Safety results are Thinking Machines' own, with no harness to rerun them and no prompt-injection guidance", "Structured output isn't documented, and the card gives no dated knowledge cut-off" ], "agentNotes": [ "Unless you have a multi-GPU Blackwell or H200 node, call it through Together AI, Fireworks, DeepInfra or Tinker rather than starting it", "On 8 B200 GPUs, SGLang's recipe is `sglang serve --trust-remote-code --model-path thinkingmachines/Inkling-NVFP4 --tp 8 --quantization modelopt_fp4 --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling`", "Use the inkling tool parser, not Hermes, and the inkling reasoning parser", "Pass `reasoning_effort` as none, minimal, low, medium, high (the default) or max, or a number from 0 to 0.99. The repository's template has no xhigh preset and raises an error for it, though Thinking Machines' docs list xhigh", "Tool call arguments must reach the template as an object, not a JSON string, or it raises an error", "Audio should be 16 kHz WAV, ideally under 20 minutes, and images 40 to 4096 px a side, per the card", "Read the Model Acceptable Use Policy before deploying, it binds on download and bars some uses that are legal" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 64.7 } ], "editorialScores": { "capability": 60, "interface": 85, "reach": 50, "risk": 70, "upkeep": 87 }, "provenanceScore": 71, "checked": { "categories": { "capability": "2026-11-02", "interface": "2026-11-02", "reach": "2026-11-02", "risk": "2026-11-02", "upkeep": "2026-11-02" }, "last": "2026-11-02", "lastMoved": "2026-11-02", "oldest": "2026-11-02", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# per SGLang's cookbook, NVFP4 on 8 B200 GPUs:\nsglang serve --trust-remote-code --model-path thinkingmachines/Inkling-NVFP4 --tp 8 \\\n --quantization modelopt_fp4 --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling", "http": "curl http://localhost:30000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"thinkingmachines/Inkling-NVFP4\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n \"reasoning_effort\": \"low\"\n }'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/inkling" }, "sameCompany": [ "inkling-2-small", "inkling-small" ], "sameModel": [ "tinker" ], "weights": { "trainedBy": "Thinking Machines Lab", "params": "975B", "active": "41B", "context": 1048576, "goodFor": "Large mixture-of-experts generalist for coding, agents, factual recall and audio, with images in and thinking effort set per request.", "runtimes": [ "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Inkling-NVFP4", "url": "https://huggingface.co/thinkingmachines/Inkling-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 592, "memoryGB": 1024, "format": "safetensors", "note": "NVFP4 experts with FP8 and BF16 parts. Thinking Machines says W4A4 needs NVIDIA SM100 or newer, and W4A16 runs on H200" }, { "name": "Inkling", "url": "https://huggingface.co/thinkingmachines/Inkling", "who": "publisher", "precision": "bf16", "sizeGB": 1904.8, "memoryGB": 4096, "format": "safetensors", "note": "1,904.8 GB × 1.2 = 2,285.7 GB, so the 4,096 GB band. Thinking Machines states at least 2 TB of GPU memory" } ], "run": "sglang serve --trust-remote-code --model-path thinkingmachines/Inkling-NVFP4 --tp 8 --quantization modelopt_fp4 --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling", "runBuild": "Inkling-NVFP4", "checked": "2026-10-21", "minMemoryGB": 1024 }, "family": { "lab": "Thinking Machines Lab", "family": "Inkling", "url": "https://www.anchorterminal.com/open-weights/#thinking-machines", "others": [ { "name": "Inkling-Small", "url": "https://huggingface.co/thinkingmachines/Inkling-Small", "params": "265.96B (Hub safetensors count; the name states none)", "listing": "https://www.anchorterminal.com/tools/inkling-small" } ], "unrestrictedCopies": 1 }, "area": "models", "provenance": { "legalEntity": "Thinking Machines Lab, Inc. (named on its model card page and in its training-content summary)", "domain": "thinkingmachines.ai", "domainRegistered": "", "domainNote": "thinkingmachines.ai's registration date wasn't read on 21 October 2026.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/thinkingmachines/Inkling/commits/main", "securityTxt": "valid", "checked": "2026-10-21", "notes": [ "Thinking Machines' Inkling page links the thinkingmachines collection on Hugging Face, and its model card page links the repository. Hugging Face hasn't verified the organisation.", "Nothing is hosted for this listing, so the Apache 2.0 licence and the Model Acceptable Use Policy stand in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, 13 commits from 15 to 23 July 2026, the upload, a Transformers processor and template change on 15 July, card updates and community evaluation results.", "thinkingmachines.ai/.well-known/security.txt gives a security contact, a PGP key and an expiry in 2029, and the site publishes a coordinated vulnerability disclosure policy that leaves model safety to a separate incident contact." ], "score": 71 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/inkling.json", "live": { "slug": "inkling", "securityTxt": { "url": "https://thinkingmachines.ai/.well-known/security.txt", "state": "valid", "expires": "2029-07-13T07:00:00.000Z", "checkedAt": "2026-11-10T16:08:06.712462129Z" }, "domain": { "domain": "thinkingmachines.ai", "registered": "2024-07-09", "source": "https://rdap.identitydigital.services/rdap/domain/thinkingmachines.ai", "checkedAt": "2026-11-08T15:05:10.558189316Z" }, "pages": [ { "url": "https://huggingface.co/thinkingmachines/Inkling/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:23:50.70345348Z", "changedAt": "2026-11-10T16:23:50.70345348Z", "fingerprint": "60baeb3c18bc" } ], "updatedAt": "2026-11-10T16:23:50.70345348Z" } }, "answer": "Inkling scores 64.7 (B) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 3 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on reach.", "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": 46, "category": "upkeep", "leads": "inkling", "name": "Documentation and upkeep", "reasons": { "a": "Model card 17 of 20, everything but a dated knowledge cut-off, counting Thinking Machines' own model card page. Training data 15, an EU training-content summary with sources, mix, a July 2026 collection cut-off and filtering. Last change 20, the weights files last changed on 23 July 2026, and Thinking Machines' Inkling renderer in tinker-cookbook changed on 30 October 2026, 3 days ago. Responsiveness 20, Thinking Machines answered 8 of the 12 questions and reports in its discussions. Version policy 5, a first release. File health 10, the files agree and the reports were answered.", "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": 87, "b": 41 } }, { "by": 35, "category": "interface", "leads": "inkling", "name": "Agent interface", "reasons": { "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 25, Thinking Machines' tml-renderers guide shows how tools are declared and how calls come back, and its package ships the parser. Trained tool calling 20, several calls in one turn. Structured output 0, not documented. Reasoning control 10, thinking effort is set per request, and the reasoning comes back in reasoning_content. Context 10, up to 1M tokens.", "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 } }, { "by": 15, "category": "risk", "leads": "inkling", "name": "Risk", "reasons": { "a": "File safety 25, safetensors only and no repository code to run. Provenance 15, the files are in the organisation Thinking Machines links to, with a SHA-256 on every LFS file. Safety evaluation 10, Thinking Machines' own StrongREJECT and FORTRESS results and commissioned external testers who published nothing. Agent guidance 10, the card says what its refusals miss and which safeguards to add, with nothing on prompt injection. Licence risk 5, 10 off for a use policy that reaches beyond the law and that can change at any time. Security programme 5, a security.txt and a disclosure policy, with public advisories unchecked and scored as absent.", "b": "File safety 25, 21 safetensors shards and no pickle or Python files, and config.json has no auto_map. Provenance 15, files sit in the Qwen organisation, the one Alibaba's Qwen repositories on GitHub link to per the Qwen3.6-35B-A3B listing (10), and the Hub gives a SHA-256 for every large file (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, no prompt-injection guidance, instruction hierarchy, refusal statement or general cautions on the card. Licence risk 15, Apache 2.0 has no use policy, no threshold and no terms the licensor can change or revoke, and the card's licence link points at the LICENSE file in the repository. Security programme 0, no reporting route on the card. 25 + 15 + 0 + 0 + 15 + 0 = 55." }, "scores": { "a": 70, "b": 55 } }, { "by": 5, "category": "reach", "leads": "qwen-agentworld-35b-a3b", "name": "Reach", "reasons": { "a": "Memory 0 of 30, the smallest 4-bit build is Thinking Machines' NVFP4 at 592.0 GB, about 710 GB with a fifth added, far above 256 GB. Runtimes 15, released Transformers, vLLM and SGLang run it, llama.cpp, Ollama and LM Studio don't. Builds 10, one quantised build (NVFP4). Terms 10, Apache 2.0 with a binding acceptable use policy on top, read as a custom licence. Download 10, no gate. Hosted 5, Together AI, Fireworks and DeepInfra are live on Hugging Face, and Thinking Machines' Tinker serves it too.", "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": 50, "b": 55 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Thinking Machines Lab", "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": "2026-07-23", "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": "3/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Inkling scores 64.7 (B) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 3 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on reach.", "question": "Which is better for AI agents, Inkling or Qwen-AgentWorld-35B-A3B?" } ], "goodFor": [ { "aheadOn": [ "Risk, 70 against 55", "Agent interface, 85 against 50", "Documentation and upkeep, 87 against 41" ], "also": null, "goodFor": "Agents calling a hosted open multimodal model with audio input, or teams with a multi-GPU datacentre node who want to run it themselves.", "slug": "inkling", "watchFor": "The smallest build Thinking Machines publishes needs about 710 GB, beyond any single workstation" }, { "aheadOn": [ "Reach, 55 against 50" ], "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": { "inkling": [ { "causes": [ "product" ], "date": "2026-11-02", "delta": 0.5, "from": 64.2, "fromGrade": "B", "to": 64.7, "toGrade": "B" } ], "qwen-agentworld-35b-a3b": null }, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling.json", "title": "Agents-A1-4B vs Inkling", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen-agentworld-35b-a3b.json", "title": "Agents-A1-4B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling.json", "title": "Apertus v1.5 8B vs Inkling", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen-agentworld-35b-a3b.json", "title": "Apertus v1.5 8B vs Qwen-AgentWorld-35B-A3B", "url": 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"json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling.json", "title": "Gemma 4 31B IT vs Inkling", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen-agentworld-35b-a3b.json", "title": "Gemma 4 31B IT vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-inkling.json", "title": "Gemma 4 E2B IT vs Inkling", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen-agentworld-35b-a3b.json", "title": "Gemma 4 E2B IT vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-inkling.json", "title": "Gemma 4 E4B IT vs 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Kimi K3", "url": "https://www.anchorterminal.com/compare/inkling-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-kimi-k3-1-pro.json", "title": "Inkling vs Kimi K3.1 Pro", "url": "https://www.anchorterminal.com/compare/inkling-vs-kimi-k3-1-pro" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-kolibri-1.json", "title": "Inkling vs Kolibri-1", "url": "https://www.anchorterminal.com/compare/inkling-vs-kolibri-1" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-kronos.json", "title": "Inkling vs Kronos", "url": "https://www.anchorterminal.com/compare/inkling-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-laguna-m-1.json", "title": "Inkling vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/inkling-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-laguna-s-2-1.json", "title": "Inkling vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/inkling-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-laguna-xs-2-1.json", "title": "Inkling vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/inkling-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-lbs-27b.json", "title": "Inkling vs LBS-27B", "url": "https://www.anchorterminal.com/compare/inkling-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-lfm2-5-2-6b.json", "title": "Inkling vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/inkling-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-lfm3-1-7b.json", "title": "Inkling vs LFM3-1.7B", "url": "https://www.anchorterminal.com/compare/inkling-vs-lfm3-1-7b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-lfm3-audio-2b.json", "title": "Inkling vs LFM3-Audio-2B", "url": "https://www.anchorterminal.com/compare/inkling-vs-lfm3-audio-2b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-lfm3-omni-2b.json", "title": "Inkling vs LFM3-Omni-2B", "url": "https://www.anchorterminal.com/compare/inkling-vs-lfm3-omni-2b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-lfm3-vl-2b.json", "title": "Inkling vs LFM3-VL-2B", "url": "https://www.anchorterminal.com/compare/inkling-vs-lfm3-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-ling-3-0-tiny.json", "title": "Inkling vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/inkling-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-ling-3-1-flash-1019.json", "title": "Inkling vs Ling-3.1-flash-1019", "url": "https://www.anchorterminal.com/compare/inkling-vs-ling-3-1-flash-1019" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-longcat-2-5.json", "title": "Inkling vs LongCat-2.5", "url": "https://www.anchorterminal.com/compare/inkling-vs-longcat-2-5" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-mimo-v2-6-distill-qwen-9b.json", "title": "Inkling vs MiMo-V2.6 Distill Qwen 9B", "url": "https://www.anchorterminal.com/compare/inkling-vs-mimo-v2-6-distill-qwen-9b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-mimo-v2-6-flash-rl.json", "title": "Inkling vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/inkling-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-minicpm-o-4-5.json", "title": "Inkling vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/inkling-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-minicpm-v-4-7-1b.json", "title": "Inkling vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/inkling-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-minicpm-v-4-7-35b-a3b.json", "title": "Inkling vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/inkling-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-minicpm5-2b.json", "title": "Inkling vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/inkling-vs-minicpm5-2b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-minimax-m2-5.json", "title": "Inkling vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/inkling-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-minimax-m2-7.json", "title": "Inkling vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/inkling-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-minimax-m3-1-flash.json", "title": "Inkling vs MiniMax-M3.1-Flash", "url": "https://www.anchorterminal.com/compare/inkling-vs-minimax-m3-1-flash" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-mistral-large-4.json", "title": "Inkling vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/inkling-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-muse-glimmer-30b.json", "title": "Inkling vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/inkling-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-nanbeige-4-2-3b.json", "title": "Inkling vs Nanbeige4.2-3B", "url": "https://www.anchorterminal.com/compare/inkling-vs-nanbeige-4-2-3b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-nanbeige-4-3-33b.json", "title": "Inkling vs Nanbeige4.3-33B", "url": "https://www.anchorterminal.com/compare/inkling-vs-nanbeige-4-3-33b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Inkling vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Inkling vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-nano-30b-a3b.json", "title": "Inkling vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-nano-4b.json", "title": "Inkling vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Inkling vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-super-120b-a12b.json", "title": "Inkling vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-ultra-550b-a55b.json", "title": "Inkling vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-north-mini-code-1-0.json", "title": "Inkling vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/inkling-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-occamy-1-0.json", "title": "Inkling vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/inkling-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-olmo-3-7b-think.json", "title": "Inkling vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/inkling-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-122b.json", "title": "Inkling vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-35b-a3b.json", "title": "Inkling vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-397b.json", "title": "Inkling vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-9b.json", "title": "Inkling vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-otel-2-0-llm-31b-it.json", "title": "Inkling vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/inkling-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Inkling vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-4b-instruct-2507.json", "title": "Inkling vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-0-8b.json", "title": "Inkling vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-122b-a10b.json", "title": "Inkling vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-2b.json", "title": "Inkling vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-397b-a17b.json", "title": "Inkling vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-4b.json", "title": "Inkling vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-9b.json", "title": "Inkling vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-6-27b.json", "title": "Inkling vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-6-35b-a3b.json", "title": "Inkling vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-8-27b.json", "title": "Inkling vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-8-flash-next.json", "title": "Inkling vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Inkling vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-coder-next.json", "title": "Inkling vs Qwen3-Coder-Next", "url": 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1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/inkling-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Inkling vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/inkling-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-thinkingcap-qwen3-8-27b.json", "title": "Inkling vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/inkling-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-unlimited-ocr-2.json", "title": "Inkling vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/inkling-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-wevisone-vl-2b.json", "title": "Inkling vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/inkling-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-wevisone-vl-4b.json", "title": "Inkling vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/inkling-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-zaya1-8b.json", "title": "Inkling vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/inkling-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen-agentworld-35b-a3b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen-agentworld-35b-a3b.json", "title": "Kimi K3.1 Pro vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen-agentworld-35b-a3b.json", "title": "Kimi K3 vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen-agentworld-35b-a3b.json", "title": "Kolibri-1 vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen-agentworld-35b-a3b.json", "title": "Kronos vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen-agentworld-35b-a3b.json", "title": "Laguna M.1 vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen-agentworld-35b-a3b.json", "title": "Laguna S 2.1 vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen-agentworld-35b-a3b.json", "title": "Laguna XS 2.1 vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen-agentworld-35b-a3b.json", "title": "LBS-27B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen-agentworld-35b-a3b.json", "title": "LFM2.5-2.6B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen-agentworld-35b-a3b.json", "title": "LFM3-1.7B vs Qwen-AgentWorld-35B-A3B", "url": 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"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": 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"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": 5, "edge": "qwen-agentworld-35b-a3b", "inkling": 50, "key": "reach", "name": "Reach", "qwen-agentworld-35b-a3b": 55, "weight": 25 }, { "by": 15, "edge": "inkling", "inkling": 70, "key": "risk", "name": "Risk", "qwen-agentworld-35b-a3b": 55, "weight": 20 }, { "by": 35, "edge": "inkling", "inkling": 85, "key": "interface", "name": "Agent interface", "qwen-agentworld-35b-a3b": 50, "weight": 10 }, { "by": 46, "edge": "inkling", "inkling": 87, "key": "upkeep", "name": "Documentation and upkeep", "qwen-agentworld-35b-a3b": 41, "weight": 10 } ], "summary": "Inkling scores 64.7 (B) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 3 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on reach. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "inkling": "Thinking Machines' large multimodal generalist, stronger than Inkling-Small on factual recall by its own numbers. It needs about 710 GB of datacentre GPU memory even in NVFP4, so most agents will use a host such as Together AI or Tinker. The main risks are a use policy that binds on download and can change, and no outside safety results.", "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/inkling-vs-qwen-agentworld-35b-a3b", "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen-agentworld-35b-a3b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/inkling-vs-qwen-agentworld-35b-a3b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/inkling-vs-qwen-agentworld-35b-a3b.min.md" }, "markdown": "Inkling scores 64.7 (B) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 3 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on reach. Both do open-weight models you run yourself.\n\n- Inkling: grade B, 64.7/100, rank #19 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/inkling.md · JSON https://www.anchorterminal.com/api/v1/tools/inkling.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### Inkling (B)\n\nGood for: Agents calling a hosted open multimodal model with audio input, or teams with a multi-GPU datacentre node who want to run it themselves.\n\nAhead on:\n- Risk, 70 against 55\n- Agent interface, 85 against 50\n- Documentation and upkeep, 87 against 41\n\nWatch for: The smallest build Thinking Machines publishes needs about 710 GB, beyond any single workstation\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\nAhead on:\n- Reach, 55 against 50\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: Inkling +46\n\n- Inkling (87): Model card 17 of 20, everything but a dated knowledge cut-off, counting Thinking Machines' own model card page. Training data 15, an EU training-content summary with sources, mix, a July 2026 collection cut-off and filtering. Last change 20, the weights files last changed on 23 July 2026, and Thinking Machines' Inkling renderer in tinker-cookbook changed on 30 October 2026, 3 days ago. Responsiveness 20, Thinking Machines answered 8 of the 12 questions and reports in its discussions. Version policy 5, a first release. File health 10, the files agree and the reports were answered.\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### Agent interface: Inkling +35\n\n- Inkling (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, Thinking Machines' tml-renderers guide shows how tools are declared and how calls come back, and its package ships the parser. Trained tool calling 20, several calls in one turn. Structured output 0, not documented. Reasoning control 10, thinking effort is set per request, and the reasoning comes back in reasoning_content. Context 10, up to 1M tokens.\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### Risk: Inkling +15\n\n- Inkling (70): File safety 25, safetensors only and no repository code to run. Provenance 15, the files are in the organisation Thinking Machines links to, with a SHA-256 on every LFS file. Safety evaluation 10, Thinking Machines' own StrongREJECT and FORTRESS results and commissioned external testers who published nothing. Agent guidance 10, the card says what its refusals miss and which safeguards to add, with nothing on prompt injection. Licence risk 5, 10 off for a use policy that reaches beyond the law and that can change at any time. Security programme 5, a security.txt and a disclosure policy, with public advisories unchecked and scored as absent.\n- Qwen-AgentWorld-35B-A3B (55): File safety 25, 21 safetensors shards and no pickle or Python files, and config.json has no auto_map. Provenance 15, files sit in the Qwen organisation, the one Alibaba's Qwen repositories on GitHub link to per the Qwen3.6-35B-A3B listing (10), and the Hub gives a SHA-256 for every large file (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, no prompt-injection guidance, instruction hierarchy, refusal statement or general cautions on the card. Licence risk 15, Apache 2.0 has no use policy, no threshold and no terms the licensor can change or revoke, and the card's licence link points at the LICENSE file in the repository. Security programme 0, no reporting route on the card. 25 + 15 + 0 + 0 + 15 + 0 = 55.\n\n### Reach: Qwen-AgentWorld-35B-A3B +5\n\n- Inkling (50): Memory 0 of 30, the smallest 4-bit build is Thinking Machines' NVFP4 at 592.0 GB, about 710 GB with a fifth added, far above 256 GB. Runtimes 15, released Transformers, vLLM and SGLang run it, llama.cpp, Ollama and LM Studio don't. Builds 10, one quantised build (NVFP4). Terms 10, Apache 2.0 with a binding acceptable use policy on top, read as a custom licence. Download 10, no gate. Hosted 5, Together AI, Fireworks and DeepInfra are live on Hugging Face, and Thinking Machines' Tinker serves it too.\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## Score by category\n\n| Category | Weight | Inkling | Qwen-AgentWorld-35B-A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 50 | 55 | Qwen-AgentWorld-35B-A3B +5 |\n| Risk | 20% (20 this run) | 70 | 55 | Inkling +15 |\n| Agent interface | 10% (10 this run) | 85 | 50 | Inkling +35 |\n| Documentation and upkeep | 10% (10 this run) | 87 | 41 | Inkling +46 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **64.7 · B** | **52.1 · D** | |\n\n## Facts side by side\n\n| Fact | Inkling | Qwen-AgentWorld-35B-A3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Thinking Machines Lab | 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 | 2026-07-23 | 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 | 3/5 (4) | none |\n\n## How the scores have moved\n\n### Inkling\n\n- +0.5 on 2 November 2026: product\n\n### Qwen-AgentWorld-35B-A3B\n\nNo score change since it was listed.\n\n## Verdicts\n\n**Inkling.** Thinking Machines' large multimodal generalist, stronger than Inkling-Small on factual recall by its own numbers. It needs about 710 GB of datacentre GPU memory even in NVFP4, so most agents will use a host such as Together AI or Tinker. The main risks are a use policy that binds on download and can change, and no outside safety results.\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### Inkling\n\n1. Unless you have a multi-GPU Blackwell or H200 node, call it through Together AI, Fireworks, DeepInfra or Tinker rather than starting it\n2. On 8 B200 GPUs, SGLang's recipe is `sglang serve --trust-remote-code --model-path thinkingmachines/Inkling-NVFP4 --tp 8 --quantization modelopt_fp4 --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling`\n3. Use the inkling tool parser, not Hermes, and the inkling reasoning parser\n4. Pass `reasoning_effort` as none, minimal, low, medium, high (the default) or max, or a number from 0 to 0.99. The repository's template has no xhigh preset and raises an error for it, though Thinking Machines' docs list xhigh\n5. Tool call arguments must reach the template as an object, not a JSON string, or it raises an error\n6. Audio should be 16 kHz WAV, ideally under 20 minutes, and images 40 to 4096 px a side, per the card\n7. Read the Model Acceptable Use Policy before deploying, it binds on download and bars some uses that are legal\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, Inkling or Qwen-AgentWorld-35B-A3B?\n\nInkling scores 64.7 (B) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 3 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/inkling-vs-qwen-agentworld-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/inkling-vs-qwen-agentworld-35b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"inkling\", \"b\": \"qwen-agentworld-35b-a3b\"}`. From a terminal: `anchor compare inkling qwen-agentworld-35b-a3b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/inkling.json and https://www.anchorterminal.com/api/v1/tools/qwen-agentworld-35b-a3b.json\n\n## Other comparisons with Inkling or Qwen-AgentWorld-35B-A3B\n\n- [Agents-A1-4B vs Inkling](https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling.md)\n- [Agents-A1-4B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen-agentworld-35b-a3b.md)\n- [Apertus v1.5 8B vs Inkling](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling.md)\n- [Apertus v1.5 8B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen-agentworld-35b-a3b.md)\n- [Apodex-1.1-mini vs Inkling](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-inkling.md)\n- [Apodex-1.1-mini vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen-agentworld-35b-a3b.md)\n- [Arete-27B 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