{ "data": { "a": { "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" }, "answer": "Ripple-65B Preview scores 53.3 (D) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 2 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on reach.", "b": { "slug": "ripple-65b-preview", "name": "Ripple-65B Preview", "vendor": "Ricursive Intelligence", "vendorUrl": "https://www.ricursive.com/ripple", "kind": "weights", "category": "open-weights", "summary": "Ricursive Intelligence's 65B dense model, pretrained from scratch and post-trained for agentic coding, on Hugging Face in BF16 and NVFP4 under Apache 2.0. Its custom architecture needs Ricursive's own model code to run.", "url": "https://www.anchorterminal.com/tools/ripple-65b-preview", "markdownUrl": "https://www.anchorterminal.com/tools/ripple-65b-preview.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ripple-65b-preview.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.json", "dns": "ripple-65b-preview.grade.anchorterminal.com", "repo": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "ricursiveAI/Ripple-65B-Preview" }, { "registry": "huggingface", "name": "ricursiveAI/Ripple-65B-Preview-NVFP4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither the BF16 nor the NVFP4 repository is gated (checked 2026-11-04).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 129.6 GB for the BF16 files and 57.2 GB for the NVFP4 build by the Hub's file pages. Ricursive's launch post names Together AI, GMI Cloud and OpenRouter as hosts, but Together's model page says coming soon (checked 2026-11-04).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-04).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-04" }, "docsUrl": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "reasoning", "tool-calling", "coding", "apache-2.0", "safetensors", "remote-code", "self-hosted", "new" ], "lastRelease": "2026-11-04", "graded": true, "anchor": { "graded": true, "score": 53.3, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 100, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 75, "reach": 45, "risk": 55, "upkeep": 49 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-04" }, "negative": 0, "verdict": "A dense 65B coding model from a new lab, pretrained from scratch and released today under Apache 2.0, that Ricursive says leads U.S. open-weight models on Terminal-Bench 2.1. Nobody has checked that yet, and the custom architecture runs only through Ricursive's own SGLang plugin on Blackwell GPUs, with a 57 GB NVFP4 build as the smallest.", "bestFor": "Teams with Blackwell GPUs or a DGX Spark who want an Apache 2.0 coding model to try for terminal and agentic coding work, and can run the publisher's code to serve it.", "strengths": [ "Apache 2.0 with nothing added, ungated, from the organisation Ricursive's site links to", "A tools-aware chat template with several calls a turn, and a server that returns reasoning, content and tool calls separately", "Trained for long context at 2M-token sequences, with 1M stated", "Base model and a speculative-decoding draft published beside the instruct model", "Safetensors only, with a checksum on every file" ], "weaknesses": [ "No released runtime runs the architecture, so running it means Ricursive's Python in SGLang 0.5.19 or Transformers' remote code", "The smallest build is 57.2 GB NVFP4, for NVIDIA Blackwell only, and there is no GGUF", "No independent benchmark yet, and the top-ranking claim rests on Ricursive's own Terminal-Bench 2.1 run", "Thinking can't be turned off or budgeted", "No safety guidance for agent use, no security contact and no knowledge cut-off" ], "agentNotes": [ "Run Ricursive's launcher from inference/server with `uv run --script serve.py` on two B200 GPUs; it listens on 127.0.0.1:30000 with a 16K context by default", "The launcher turns on `--reasoning-parser ripple` and `--tool-call-parser ripple`; without them tool calls come back as raw text inside tool_call tags", "For `tool_choice: \"required\"` set `strict: true` and `additionalProperties: false` on each function, which the README says stopped extra argument fields", "Leave room in `max_tokens` for reasoning as well as the answer, since thinking is always on", "Use chat_template.jinja, not the template inside tokenizer_config.json, which has no tool support" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 53.3 } ], "editorialScores": { "interface": 75, "reach": 45, "risk": 55, "upkeep": 49 }, "provenanceScore": 36, "checked": { "categories": { "interface": "2026-11-04", "reach": "2026-11-04", "risk": "2026-11-04", "upkeep": "2026-11-04" }, "last": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "hf download ricursiveAI/Ripple-65B-Preview --local-dir ./ripple-65b\nhf download ricursiveAI/Ripple-65B-Preview-DFlash2 --local-dir ./ripple-dflash2\ncd ripple-65b/inference/server\nCUDA_VISIBLE_DEVICES=0,1 uv run --script serve.py \\\n --model-path ../.. \\\n --speculative-draft-model-path ../../../ripple-dflash2", "http": "curl http://127.0.0.1:30000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"ripple\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello\"}], \"max_tokens\": 128}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/ripple-65b-preview" }, "weights": { "trainedBy": "Ricursive Intelligence", "params": "65B", "context": 1048576, "goodFor": "A dense 65B model post-trained for agentic coding and terminal work, with several tool calls a turn and a long context, for teams with Blackwell GPUs or a DGX Spark.", "runtimes": [], "builds": [ { "name": "Ripple-65B-Preview-NVFP4", "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 57.2, "memoryGB": 69, "format": "safetensors", "note": "NVFP4 MLP weights and activations with BF16 attention, 6 shards summed from the Hub's file page. For NVIDIA Blackwell, with Ricursive's DGX Spark loader. The checkpoint was replaced in place on release day" }, { "name": "Ripple-65B-Preview", "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview", "who": "publisher", "precision": "bf16", "sizeGB": 129.6, "memoryGB": 160, "format": "safetensors", "note": "16 shards summed from the Hub's file page. Served with Ricursive's SGLang 0.5.19 plugin on two B200 GPUs" } ], "run": "CUDA_VISIBLE_DEVICES=0,1 uv run --script serve.py --model-path /path/to/Ripple-65B-Preview --speculative-draft-model-path /path/to/Ripple-65B-Preview-DFlash2", "runBuild": "Ripple-65B-Preview", "checked": "2026-11-04", "minMemoryGB": 69 }, "area": "models", "provenance": { "legalEntity": "Ricursive Intelligence, Inc.", "domain": "ricursive.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview/blob/main/LICENSE", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-11-04", "notes": [ "Every page on www.ricursive.com ends with \"© 2026 Ricursive Intelligence, Inc.\". The site links the Hugging Face organisation ricursiveAI (fullname Ricursive Intelligence), which publishes the four Ripple repositories. RDAP wasn't queried, by the run's rules.", "The Apache 2.0 LICENSE in the repository governs the weights and stands in for terms of service. The site's own legal pages (/legal and /legal/terms, titled Model Attribution Terms) render their text by script only and couldn't be read.", "Nothing is hosted by Ricursive, so no privacy policy or status page is scored.", "No changelog. The repositories' commit messages are uploads and one in-place swap of the NVFP4 checkpoint on release day.", "www.ricursive.com/.well-known/security.txt is 404 on 4 November 2026, and the only contact on the card and site is a general one." ], "score": 36 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/ripple-65b-preview.json", "live": { "slug": "ripple-65b-preview", "securityTxt": { "url": "https://ricursive.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:09:39.085200806Z" }, "domain": { "domain": "ricursive.com", "registered": "2025-10-05", "source": "https://rdap.verisign.com/com/v1/domain/ricursive.com", "checkedAt": "2026-11-08T14:52:57.113403323Z" }, "pages": [ { "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview/blob/main/LICENSE", "kind": "terms", "status": 200, "checkedAt": "2026-11-10T16:23:44.447363623Z", "changedAt": "2026-11-10T16:23:44.447363623Z", "fingerprint": "ceb3545d1daa" } ], "updatedAt": "2026-11-10T16:23:44.447363623Z" } }, "edges": [ { "by": 25, "category": "interface", "leads": "ripple-65b-preview", "name": "Agent interface", "reasons": { "a": "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.", "b": "Chat template 20, chat_template.jinja in the repository. Tool-calling format 25, Ricursive's server README says how tools are declared and calls come back, with the ripple parser in SGLang 0.5.19. Trained tool calling 20, one or more calls a turn and agentic-coding RL. Structured output 0, not documented. Reasoning control 0, the template has no thinking switch or effort levels. Context 10, long-context training at 2M-token sequences for a stated 1M." }, "scores": { "a": 50, "b": 75 } }, { "by": 10, "category": "reach", "leads": "qwen-agentworld-35b-a3b", "name": "Reach", "reasons": { "a": "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.", "b": "Memory 10 of 30, the smallest 4-bit build is Ricursive's NVFP4 at 57.2 GB, 68.7 GB with a fifth added, which is over 64 GB and fits our reference machine. Runtimes 0, no released runtime runs the ricursive architecture with its own code; Ricursive's SGLang plugin and Transformers' remote code don't count. Builds 10, one quantised precision (NVFP4) from Ricursive. Terms 15, Apache 2.0 with nothing added. Download 10, not gated. Hosted 0, Ricursive serves no API, Hugging Face maps no provider, Together AI's page says coming soon, and GMI Cloud's and OpenRouter's pages weren't read." }, "scores": { "a": 55, "b": 45 } }, { "by": 8, "category": "upkeep", "leads": "ripple-65b-preview", "name": "Documentation and upkeep", "reasons": { "a": "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.", "b": "Model card 14 of 20, no limitations and no knowledge cut-off. Training data 5, a general description with token counts and no cut-off. Last change 20, released today. Responsiveness 0, no discussions yet, read as none answered. Version policy 5, a first release, with the NVFP4 checkpoint replaced in place on release day under a commit note. File health 5, tokenizer_config.json carries a second, tool-less chat template and a 131,072-token limit against the card's 1M." }, "scores": { "a": 41, "b": 49 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Alibaba (Qwen team)", "b": "Ricursive Intelligence", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-06-25", "b": "2026-11-04", "name": "Last release" }, { "a": "no document linked", "b": "not read yet", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" } ], "faq": [ { "answer": "Ripple-65B Preview scores 53.3 (D) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 2 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on reach.", "question": "Which is better for AI agents, Qwen-AgentWorld-35B-A3B or Ripple-65B Preview?" } ], "goodFor": [ { "aheadOn": [ "Reach, 55 against 45" ], "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" }, { "aheadOn": [ "Agent interface, 75 against 50", "Documentation and upkeep, 49 against 41" ], "also": null, "goodFor": "Teams with Blackwell GPUs or a DGX Spark who want an Apache 2.0 coding model to try for terminal and agentic coding work, and can run the publisher's code to serve it.", "slug": "ripple-65b-preview", "watchFor": "No released runtime runs the architecture, so running it means Ricursive's Python in SGLang 0.5.19 or Transformers' remote code" } ], "history": null, "job": { "capability": 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"Qwen3.6-35B-A3B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-ripple-65b-preview.json", "title": "Qwen3.8-27B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-ripple-65b-preview.json", "title": "Qwen3.8-Flash-Next vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-ripple-65b-preview.json", "title": "Qwen3-Coder-Next vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-VL-2B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-VL-32B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-VL-4B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-VL-8B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-ripple-65b-preview.json", "title": "Qwopus3.8-27B-Flash-V2 vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-ripple-65b-preview.json", "title": "Recursive Flint vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-sensenova-loop-9b-preview.json", "title": "Ripple-65B Preview vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-smollm3-3b.json", "title": "Ripple-65B Preview vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-spark-x2-6-4b.json", "title": "Ripple-65B Preview vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-step-3-7-flash.json", "title": "Ripple-65B Preview vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-27b.json", "title": "Ripple-65B Preview vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Ripple-65B Preview vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-thinkingcap-qwen3-8-27b.json", "title": "Ripple-65B Preview vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-unlimited-ocr-2.json", "title": "Ripple-65B Preview vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-wevisone-vl-2b.json", "title": "Ripple-65B Preview vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-wevisone-vl-4b.json", "title": "Ripple-65B Preview vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-zaya1-8b.json", "title": "Ripple-65B Preview vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 10, "edge": "qwen-agentworld-35b-a3b", "key": "reach", "name": "Reach", "qwen-agentworld-35b-a3b": 55, "ripple-65b-preview": 45, "weight": 25 }, { "by": 0, "edge": "", "key": "risk", "name": "Risk", "qwen-agentworld-35b-a3b": 55, "ripple-65b-preview": 55, "weight": 20 }, { "by": 25, "edge": "ripple-65b-preview", "key": "interface", "name": "Agent interface", "qwen-agentworld-35b-a3b": 50, "ripple-65b-preview": 75, "weight": 10 }, { "by": 8, "edge": "ripple-65b-preview", "key": "upkeep", "name": "Documentation and upkeep", "qwen-agentworld-35b-a3b": 41, "ripple-65b-preview": 49, "weight": 10 } ], "summary": "Ripple-65B Preview scores 53.3 (D) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 2 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on reach. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "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.", "ripple-65b-preview": "A dense 65B coding model from a new lab, pretrained from scratch and released today under Apache 2.0, that Ricursive says leads U.S. open-weight models on Terminal-Bench 2.1. Nobody has checked that yet, and the custom architecture runs only through Ricursive's own SGLang plugin on Blackwell GPUs, with a 57 GB NVFP4 build as the smallest." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-ripple-65b-preview", "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-ripple-65b-preview.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-ripple-65b-preview.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-ripple-65b-preview.min.md" }, "markdown": "Ripple-65B Preview scores 53.3 (D) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 2 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on reach. Both do open-weight models you run yourself.\n\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- Ripple-65B Preview: grade D, 53.3/100, rank #135 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/ripple-65b-preview.md · JSON https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\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 45\n\nWatch for: Not a general assistant, and the card doesn't describe using it to call tools\n\n### Ripple-65B Preview (D)\n\nGood for: Teams with Blackwell GPUs or a DGX Spark who want an Apache 2.0 coding model to try for terminal and agentic coding work, and can run the publisher's code to serve it.\n\nAhead on:\n- Agent interface, 75 against 50\n- Documentation and upkeep, 49 against 41\n\nWatch for: No released runtime runs the architecture, so running it means Ricursive's Python in SGLang 0.5.19 or Transformers' remote code\n\n\n## Why the edge\n\n### Agent interface: Ripple-65B Preview +25\n\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- Ripple-65B Preview (75): Chat template 20, chat_template.jinja in the repository. Tool-calling format 25, Ricursive's server README says how tools are declared and calls come back, with the ripple parser in SGLang 0.5.19. Trained tool calling 20, one or more calls a turn and agentic-coding RL. Structured output 0, not documented. Reasoning control 0, the template has no thinking switch or effort levels. Context 10, long-context training at 2M-token sequences for a stated 1M.\n\n### Reach: Qwen-AgentWorld-35B-A3B +10\n\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- Ripple-65B Preview (45): Memory 10 of 30, the smallest 4-bit build is Ricursive's NVFP4 at 57.2 GB, 68.7 GB with a fifth added, which is over 64 GB and fits our reference machine. Runtimes 0, no released runtime runs the ricursive architecture with its own code; Ricursive's SGLang plugin and Transformers' remote code don't count. Builds 10, one quantised precision (NVFP4) from Ricursive. Terms 15, Apache 2.0 with nothing added. Download 10, not gated. Hosted 0, Ricursive serves no API, Hugging Face maps no provider, Together AI's page says coming soon, and GMI Cloud's and OpenRouter's pages weren't read.\n\n### Documentation and upkeep: Ripple-65B Preview +8\n\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- Ripple-65B Preview (49): Model card 14 of 20, no limitations and no knowledge cut-off. Training data 5, a general description with token counts and no cut-off. Last change 20, released today. Responsiveness 0, no discussions yet, read as none answered. Version policy 5, a first release, with the NVFP4 checkpoint replaced in place on release day under a commit note. File health 5, tokenizer_config.json carries a second, tool-less chat template and a 131,072-token limit against the card's 1M.\n\n## Score by category\n\n| Category | Weight | Qwen-AgentWorld-35B-A3B | Ripple-65B Preview | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 55 | 45 | Qwen-AgentWorld-35B-A3B +10 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 50 | 75 | Ripple-65B Preview +25 |\n| Documentation and upkeep | 10% (10 this run) | 41 | 49 | Ripple-65B Preview +8 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **52.1 · D** | **53.3 · D** | |\n\n## Facts side by side\n\n| Fact | Qwen-AgentWorld-35B-A3B | Ripple-65B Preview |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Alibaba (Qwen team) | Ricursive Intelligence |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-06-25 | 2026-11-04 |\n| Terms last updated | no document linked | not read yet |\n| Privacy policy last updated | no document linked | no document linked |\n\n## Verdicts\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**Ripple-65B Preview.** A dense 65B coding model from a new lab, pretrained from scratch and released today under Apache 2.0, that Ricursive says leads U.S. open-weight models on Terminal-Bench 2.1. Nobody has checked that yet, and the custom architecture runs only through Ricursive's own SGLang plugin on Blackwell GPUs, with a 57 GB NVFP4 build as the smallest.\n\n## Before you call either\n\n### 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### Ripple-65B Preview\n\n1. Run Ricursive's launcher from inference/server with `uv run --script serve.py` on two B200 GPUs; it listens on 127.0.0.1:30000 with a 16K context by default\n2. The launcher turns on `--reasoning-parser ripple` and `--tool-call-parser ripple`; without them tool calls come back as raw text inside tool_call tags\n3. For `tool_choice: \"required\"` set `strict: true` and `additionalProperties: false` on each function, which the README says stopped extra argument fields\n4. Leave room in `max_tokens` for reasoning as well as the answer, since thinking is always on\n5. Use chat_template.jinja, not the template inside tokenizer_config.json, which has no tool support\n\n## Questions\n\n### Which is better for AI agents, Qwen-AgentWorld-35B-A3B or Ripple-65B Preview?\n\nRipple-65B Preview scores 53.3 (D) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 2 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/qwen-agentworld-35b-a3b-vs-ripple-65b-preview.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-ripple-65b-preview.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"qwen-agentworld-35b-a3b\", \"b\": \"ripple-65b-preview\"}`. From a terminal: `anchor compare qwen-agentworld-35b-a3b ripple-65b-preview`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/qwen-agentworld-35b-a3b.json and https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.json\n\n## Other comparisons with Qwen-AgentWorld-35B-A3B or Ripple-65B Preview\n\n- [Agents-A1-4B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen-agentworld-35b-a3b.md)\n- [Agents-A1-4B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ripple-65b-preview.md)\n- [Apertus v1.5 8B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen-agentworld-35b-a3b.md)\n- [Apertus v1.5 8B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ripple-65b-preview.md)\n- [Apodex-1.1-mini vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen-agentworld-35b-a3b.md)\n- [Apodex-1.1-mini vs Ripple-65B 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27B)](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-thinkingcap-qwen3-8-27b.md)\n- [Ripple-65B Preview vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-unlimited-ocr-2.md)\n- [Ripple-65B Preview vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-wevisone-vl-2b.md)\n- [Ripple-65B Preview vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-wevisone-vl-4b.md)\n- [Ripple-65B Preview vs ZAYA1-8B](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-zaya1-8b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-11", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", 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