{ "data": { "a": { "slug": "cosmos-reason2-2b", "name": "Cosmos-Reason2-2B", "vendor": "NVIDIA", "vendorUrl": "https://github.com/nvidia-cosmos/cosmos-reason2", "kind": "weights", "category": "open-weights", "summary": "Cosmos-Reason2-2B is NVIDIA's open-weight vision language model for physical AI and robotics, a 2.4B-parameter fine-tune of Qwen3-VL-2B-Instruct that reasons over video and images. The repository is gated behind a click-through licence.", "url": "https://www.anchorterminal.com/tools/cosmos-reason2-2b", "markdownUrl": "https://www.anchorterminal.com/tools/cosmos-reason2-2b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/cosmos-reason2-2b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/cosmos-reason2-2b.json", "dns": "cosmos-reason2-2b.grade.anchorterminal.com", "repo": "https://huggingface.co/nvidia/Cosmos-Reason2-2B", "license": "NVIDIA Open Model License (custom, commercial use allowed, not an OSI licence); built on Qwen3-VL-2B-Instruct under Apache 2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "nvidia/Cosmos-Reason2-2B" } ], "auth": "none", "authNotes": "The repository is gated (gated: auto). Downloading needs a Hugging Face account and token and accepting the NVIDIA Open Model License on the card, which Hugging Face approves automatically. The card, the file list and the licence text are public. We didn't accept the terms (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the NVIDIA Open Model License, with a contact for a custom licence. Running it costs the hardware, 4.9 GB of BF16 safetensors by the Hub's file list; NVIDIA states a minimum of 24 GB of GPU memory (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": "2026-11-04" }, "docsUrl": "https://github.com/nvidia-cosmos/cosmos-reason2", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "vision", "video", "reasoning", "robotics", "physical-ai", "fine-tune", "safetensors", "gated", "self-hosted" ], "lastRelease": "2026-03-10", "graded": true, "anchor": { "graded": true, "score": 59.5, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1350, "categoryRank": 56, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 65, "reach": 65, "risk": 60, "upkeep": 39 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-04" }, "negative": 0, "verdict": "A small local model for reasoning over video and images in robotics, driving and warehouse scenes, returning points, boxes and timed events as JSON. The 4.9 GB file runs in Transformers or vLLM, though NVIDIA asks for 24 GB of GPU memory. Development has moved to Cosmos 3, and the gated licence is revocable.", "bestFor": "Agents that need a small local model to reason over camera footage and images for robotics, driving or smart spaces", "strengths": [ "4.9 GB of BF16 safetensors with no remote code, loaded by Transformers' own Qwen3-VL class", "Runs on released Transformers, vLLM, SGLang and llama.cpp through the Qwen3-VL architecture", "NVIDIA's prompt files give JSON formats for bounding boxes, timed events and robot trajectories", "Reasoning is prompted with a documented think and answer format and parsed by vLLM's qwen3 parser", "Commercial use allowed, files in NVIDIA's own organisation with hashes" ], "weaknesses": [ "Gated behind a click-through licence that needs a Hugging Face account", "The licence is revocable, can be updated by NVIDIA and ends if a guardrail is bypassed", "NVIDIA says the repository is no longer under active development and points to Cosmos 3", "No quantised build from NVIDIA, no hosted endpoint for the 2B, and no NVIDIA reply in its discussions", "No documented tool calling, no safety results and no training data sizes or cut-off" ], "agentNotes": [ "Accept the licence on the Hugging Face card with a signed-in account and set HF_TOKEN before downloading", "Serve it with `vllm serve nvidia/Cosmos-Reason2-2B --max-model-len 16384 --media-io-kwargs '{\"video\": {\"num_frames\": -1}}' --reasoning-parser qwen3`, keeping max-model-len between 8,192 and 16,384 as NVIDIA advises", "For local video or images, make a folder for them (`mkdir -p media`) and pass `--allowed-local-media-path \"$(pwd)/media\"`, so the server can read only that folder, not everything under the directory you start it from", "Sample video at 4 fps and allow at least 4,096 output tokens so the reasoning isn't cut off", "For reasoning, add the card's instruction to answer with a think block and then the answer; leave it out for a direct reply", "Use NVIDIA's prompt files (prompts/*.yaml in nvidia-cosmos/cosmos-reason2) when you want JSON boxes, events or trajectories, and validate the JSON yourself", "Check its plans before a robot acts on them, since NVIDIA's own safety card warns of collision and manipulation risks" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 59.5 } ], "editorialScores": { "interface": 65, "reach": 65, "risk": 60, "upkeep": 39 }, "provenanceScore": 70, "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": "# accept the licence on the Hugging Face card first, then set HF_TOKEN\nvllm serve nvidia/Cosmos-Reason2-2B --max-model-len 16384 \\\n --media-io-kwargs '{\"video\": {\"num_frames\": -1}}' --reasoning-parser qwen3 --port 8000", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"nvidia/Cosmos-Reason2-2B\", \"max_tokens\": 4096, \"messages\": [{\"role\": \"user\", \"content\": [{\"type\": \"image_url\", \"image_url\": {\"url\": \"file:///path/to/frame.jpg\"}}, {\"type\": \"text\", \"text\": \"Is it safe to turn right?\"}]}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/cosmos-reason2-2b" }, "sameCompany": [ "nemotron-3-5-super-vl", "nvidia-nemo-retriever", "nemo-guardrails", "nemotron-3-5-asr-streaming-0-6b", "parakeet-tdt-0-6b-v3", "nemotron-3-5-parse", "nemotron-3-5-bolt-e4b-4gb", "nemotron-3-nano-30b-a3b", "nemotron-3-nano-4b", "nemotron-3-nano-omni-30b-a3b-reasoning", "nemotron-3-super-120b-a12b", "nemotron-3-ultra-550b-a55b", "nemotron-3-5-lightning-30b-a3b" ], "weights": { "trainedBy": "NVIDIA", "builtOn": "Qwen/Qwen3-VL-2B-Instruct (fine-tune, Apache 2.0)", "params": "2.44B", "context": 262144, "goodFor": "Small vision language model for physical AI, reasoning over video and images for robots, driving and smart spaces, with point and box outputs.", "runtimes": [ "Transformers", "vLLM", "SGLang", "llama.cpp" ], "builds": [ { "name": "Cosmos-Reason2-2B", "url": "https://huggingface.co/nvidia/Cosmos-Reason2-2B", "who": "publisher", "precision": "bf16", "sizeGB": 4.9, "memoryGB": 8, "format": "safetensors", "note": "Gated (click-through). NVIDIA states a minimum of 24 GB of GPU memory, more than the file size suggests, presumably for video input" } ], "run": "vllm serve nvidia/Cosmos-Reason2-2B --allowed-local-media-path \"$(pwd)/media\" --max-model-len 16384 --media-io-kwargs '{\"video\": {\"num_frames\": -1}}' --reasoning-parser qwen3 --port 8000", "runBuild": "Cosmos-Reason2-2B", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "NVIDIA (Nemotron)", "family": "Cosmos-Reason2", "url": "https://www.anchorterminal.com/open-weights/#nvidia", "others": [ { "name": "Cosmos-Reason2-8B", "url": "https://huggingface.co/nvidia/Cosmos-Reason2-8B", "params": "8B (8.77B per the safetensors count)" }, { "name": "Cosmos-Reason2-32B", "url": "https://huggingface.co/nvidia/Cosmos-Reason2-32B", "params": "32B (33.36B per the safetensors count)" } ], "olderCount": 47, "unrestrictedCopies": 3 }, "area": "models", "provenance": { "legalEntity": "NVIDIA Corporation", "domain": "nvidia.com", "domainRegistered": "1993-04-20", "endpointOnVendorDomain": null, "terms": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/", "privacy": "", "statusPage": "", "changelog": "https://github.com/nvidia-cosmos/cosmos-reason2/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The card names NVIDIA as model developer and the licence names NVIDIA Corporation. The domain registration date is carried from the RDAP check made for nemotron-3-nano-30b-a3b on 21 October 2026.", "Nothing is hosted for this listing, so the NVIDIA Open Model License stands in for terms of service and no privacy policy is scored.", "The Hugging Face commit history is behind the gate, so the changelog is the GitHub repository's, whose README carries dated news entries from 19 December 2025 to 1 June 2026.", "www.nvidia.com/.well-known/security.txt answered 403 at the 21 October 2026 check for nemotron-3-nano-30b-a3b. The card links NVIDIA's security vulnerability form." ], "score": 70 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/cosmos-reason2-2b.json", "live": { "slug": "cosmos-reason2-2b", "securityTxt": { "url": "https://nvidia.com/.well-known/security.txt", "state": "unknown", "checkedAt": "2026-11-04T15:54:28.709726124Z" }, "domain": { "domain": "nvidia.com", "registered": "1993-04-20", "source": "https://rdap.verisign.com/com/v1/domain/nvidia.com", "checkedAt": "2026-11-01T14:14:07.135656692Z" }, "pages": [ { "url": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/", "kind": "terms", "status": 304, "checkedAt": "2026-11-04T08:43:02.372227049Z", "changedAt": "2026-10-31T06:30:16.800317464Z", "fingerprint": "657e4e24049b" } ], "updatedAt": "2026-11-04T15:54:28.709726124Z" } }, "answer": "Cosmos-Reason2-2B scores 59.5 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 2 of 4 scored categories. Ripple-65B Preview leads on agent interface and documentation and upkeep.", "b": { "slug": "ripple-65b-preview", "name": "Ripple-65B Preview", "vendor": "Ricursive Intelligence", "vendorUrl": "https://www.ricursive.com/ripple", "kind": "weights", "category": "open-weights", "summary": "Ricursive Intelligence's 65B dense model, pretrained from scratch and post-trained for agentic coding, on Hugging Face in BF16 and NVFP4 under Apache 2.0. Its custom architecture needs Ricursive's own model code to run.", "url": "https://www.anchorterminal.com/tools/ripple-65b-preview", "markdownUrl": "https://www.anchorterminal.com/tools/ripple-65b-preview.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ripple-65b-preview.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.json", "dns": "ripple-65b-preview.grade.anchorterminal.com", "repo": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "ricursiveAI/Ripple-65B-Preview" }, { "registry": "huggingface", "name": "ricursiveAI/Ripple-65B-Preview-NVFP4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither the BF16 nor the NVFP4 repository is gated (checked 2026-11-04).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 129.6 GB for the BF16 files and 57.2 GB for the NVFP4 build by the Hub's file pages. Ricursive's launch post names Together AI, GMI Cloud and OpenRouter as hosts, but Together's model page says coming soon (checked 2026-11-04).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-04).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-04" }, "docsUrl": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "reasoning", "tool-calling", "coding", "apache-2.0", "safetensors", "remote-code", "self-hosted", "new" ], "lastRelease": "2026-11-04", "graded": true, "anchor": { "graded": true, "score": 53.3, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1350, "categoryRank": 78, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 75, "reach": 45, "risk": 55, "upkeep": 49 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-04" }, "negative": 0, "verdict": "A dense 65B coding model from a new lab, pretrained from scratch and released today under Apache 2.0, that Ricursive says leads U.S. open-weight models on Terminal-Bench 2.1. Nobody has checked that yet, and the custom architecture runs only through Ricursive's own SGLang plugin on Blackwell GPUs, with a 57 GB NVFP4 build as the smallest.", "bestFor": "Teams with Blackwell GPUs or a DGX Spark who want an Apache 2.0 coding model to try for terminal and agentic coding work, and can run the publisher's code to serve it.", "strengths": [ "Apache 2.0 with nothing added, ungated, from the organisation Ricursive's site links to", "A tools-aware chat template with several calls a turn, and a server that returns reasoning, content and tool calls separately", "Trained for long context at 2M-token sequences, with 1M stated", "Base model and a speculative-decoding draft published beside the instruct model", "Safetensors only, with a checksum on every file" ], "weaknesses": [ "No released runtime runs the architecture, so running it means Ricursive's Python in SGLang 0.5.19 or Transformers' remote code", "The smallest build is 57.2 GB NVFP4, for NVIDIA Blackwell only, and there is no GGUF", "No independent benchmark yet, and the top-ranking claim rests on Ricursive's own Terminal-Bench 2.1 run", "Thinking can't be turned off or budgeted", "No safety guidance for agent use, no security contact and no knowledge cut-off" ], "agentNotes": [ "Run Ricursive's launcher from inference/server with `uv run --script serve.py` on two B200 GPUs; it listens on 127.0.0.1:30000 with a 16K context by default", "The launcher turns on `--reasoning-parser ripple` and `--tool-call-parser ripple`; without them tool calls come back as raw text inside tool_call tags", "For `tool_choice: \"required\"` set `strict: true` and `additionalProperties: false` on each function, which the README says stopped extra argument fields", "Leave room in `max_tokens` for reasoning as well as the answer, since thinking is always on", "Use chat_template.jinja, not the template inside tokenizer_config.json, which has no tool support" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 53.3 } ], "editorialScores": { "interface": 75, "reach": 45, "risk": 55, "upkeep": 49 }, "provenanceScore": 36, "checked": { "categories": { "interface": "2026-11-04", "reach": "2026-11-04", "risk": "2026-11-04", "upkeep": "2026-11-04" }, "last": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "hf download ricursiveAI/Ripple-65B-Preview --local-dir ./ripple-65b\nhf download ricursiveAI/Ripple-65B-Preview-DFlash2 --local-dir ./ripple-dflash2\ncd ripple-65b/inference/server\nCUDA_VISIBLE_DEVICES=0,1 uv run --script serve.py \\\n --model-path ../.. \\\n --speculative-draft-model-path ../../../ripple-dflash2", "http": "curl http://127.0.0.1:30000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"ripple\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello\"}], \"max_tokens\": 128}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/ripple-65b-preview" }, "weights": { "trainedBy": "Ricursive Intelligence", "params": "65B", "context": 1048576, "goodFor": "A dense 65B model post-trained for agentic coding and terminal work, with several tool calls a turn and a long context, for teams with Blackwell GPUs or a DGX Spark.", "runtimes": [], "builds": [ { "name": "Ripple-65B-Preview-NVFP4", "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 57.2, "memoryGB": 69, "format": "safetensors", "note": "NVFP4 MLP weights and activations with BF16 attention, 6 shards summed from the Hub's file page. For NVIDIA Blackwell, with Ricursive's DGX Spark loader. The checkpoint was replaced in place on release day" }, { "name": "Ripple-65B-Preview", "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview", "who": "publisher", "precision": "bf16", "sizeGB": 129.6, "memoryGB": 160, "format": "safetensors", "note": "16 shards summed from the Hub's file page. Served with Ricursive's SGLang 0.5.19 plugin on two B200 GPUs" } ], "run": "CUDA_VISIBLE_DEVICES=0,1 uv run --script serve.py --model-path /path/to/Ripple-65B-Preview --speculative-draft-model-path /path/to/Ripple-65B-Preview-DFlash2", "runBuild": "Ripple-65B-Preview", "checked": "2026-11-04", "minMemoryGB": 69 }, "area": "models", "provenance": { "legalEntity": "Ricursive Intelligence, Inc.", "domain": "ricursive.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview/blob/main/LICENSE", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-11-04", "notes": [ "Every page on www.ricursive.com ends with \"© 2026 Ricursive Intelligence, Inc.\". The site links the Hugging Face organisation ricursiveAI (fullname Ricursive Intelligence), which publishes the four Ripple repositories. RDAP wasn't queried, by the run's rules.", "The Apache 2.0 LICENSE in the repository governs the weights and stands in for terms of service. The site's own legal pages (/legal and /legal/terms, titled Model Attribution Terms) render their text by script only and couldn't be read.", "Nothing is hosted by Ricursive, so no privacy policy or status page is scored.", "No changelog. The repositories' commit messages are uploads and one in-place swap of the NVFP4 checkpoint on release day.", "www.ricursive.com/.well-known/security.txt is 404 on 4 November 2026, and the only contact on the card and site is a general one." ], "score": 36 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/ripple-65b-preview.json" }, "edges": [ { "by": 20, "category": "reach", "leads": "cosmos-reason2-2b", "name": "Reach", "reasons": { "a": "Memory earns the full 30 because the BF16 file is 4.9 GB, about 5.9 GB with the fifth added, though NVIDIA itself asks for 24 GB of GPU memory. Runtimes get 20 because the model keeps Qwen3-VL's architecture, which Transformers, vLLM, SGLang and llama.cpp run in released versions, while LM Studio's catalogue has only the base. Builds score 0, as NVIDIA publishes no quantised build of the 2B. The NVIDIA Open Model License allows free commercial use under a use policy (10). The gate is an automatic click-through that needs an account (5), and nothing hosts the 2B (0).", "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": 65, "b": 45 } }, { "by": 10, "category": "interface", "leads": "ripple-65b-preview", "name": "Agent interface", "reasons": { "a": "A Jinja template ships as chat_template.json (20). Tools are declared only by the template it inherits from Qwen3-VL, with no NVIDIA documentation or parser (10), and nothing shows the 2B was trained for tool use (0). NVIDIA's prompt files ask for JSON with named keys for boxes, events and trajectories (15). Reasoning is asked for in the prompt and vLLM's qwen3 parser returns it apart (10). The card states 256K input tokens (10).", "b": "Chat template 20, chat_template.jinja in the repository. Tool-calling format 25, Ricursive's server README says how tools are declared and calls come back, with the ripple parser in SGLang 0.5.19. Trained tool calling 20, one or more calls a turn and agentic-coding RL. Structured output 0, not documented. Reasoning control 0, the template has no thinking switch or effort levels. Context 10, long-context training at 2M-token sequences for a stated 1M." }, "scores": { "a": 65, "b": 75 } }, { "by": 10, "category": "upkeep", "leads": "ripple-65b-preview", "name": "Documentation and upkeep", "reasons": { "a": "The card misses languages and a cut-off (14). Training data is named by dataset without sizes or a cut-off (5). The Hub repository last changed on 30 April 2026, 188 days before this check, which alone gives 5. NVIDIA's GitHub repository last changed with a README redirect to Cosmos 3 on 7 June 2026, 150 days back, and this run counts the publisher's documentation commits on this line (10). None of its 3 discussions has an NVIDIA reply, counted by author flags on 4 November (0). The 2B's weights were replaced in place in March with a dated line, and the family is now superseded by Cosmos 3 with a dated note (5). The gated files couldn't be checked against the card, which itself mixes float16 and BF16 and links a newer licence than the gate shows (5). 14 + 5 + 10 + 0 + 5 + 5 = 39.", "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": 39, "b": 49 } }, { "by": 5, "category": "risk", "leads": "cosmos-reason2-2b", "name": "Risk", "reasons": { "a": "File safety gets the full 25, one safetensors file loaded by Transformers' own code with a template, no remote code. Provenance gets 15 for files in NVIDIA's own organisation with hashes. The licence has a use policy and guardrail clause and NVIDIA can revoke or update it, so licence risk keeps 5 of 15. NVIDIA's PSIRT route and bulletins give the security programme 10. Agent guidance gets 5 for general cautions about guardrails and physical risk, and safety evaluation 0, with no safety results.", "b": "File safety 15, safetensors only, less 10 because the documented way to run it loads Ricursive's Python. Provenance 15, the files are in the organisation ricursive.com links to, with a checksum on every file. Safety evaluation 10, Ricursive's own FORTRESS and StrongREJECT results. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, Apache 2.0. Security programme 0, no way to report a flaw." }, "scores": { "a": 60, "b": 55 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "NVIDIA", "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": "NVIDIA Open Model License (custom, commercial use allowed, not an OSI licence); built on Qwen3-VL-2B-Instruct under 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": "2026-03-10", "b": "2026-11-04", "name": "Last release" }, { "a": "2025-10-24", "b": "not read yet", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "yes", "b": "not read yet", "name": "Customer content may train models" }, { "a": "not found in the text", "b": "not read yet", "name": "Terms restrict automated access" }, { "a": "not found in the text", "b": "not read yet", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "not read yet", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "not read yet", "name": "Arbitration or class-action waiver" }, { "a": "2.5/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Cosmos-Reason2-2B scores 59.5 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 2 of 4 scored categories. Ripple-65B Preview leads on agent interface and documentation and upkeep.", "question": "Which is better for AI agents, Cosmos-Reason2-2B or Ripple-65B Preview?" } ], "goodFor": [ { "aheadOn": [ "Reach, 65 against 45", "Risk, 60 against 55" ], "also": null, "goodFor": "Agents that need a small local model to reason over camera footage and images for robotics, driving or smart spaces", "slug": "cosmos-reason2-2b", "watchFor": "Gated behind a click-through licence that needs a Hugging Face account" }, { "aheadOn": [ "Agent interface, 75 against 65", "Documentation and upkeep, 49 against 39" ], "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": { 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"https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ripple-65b-preview.json", "title": "Ornith-1.5-35B-A3B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-ripple-65b-preview.json", "title": "Ornith-1.5-397B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-ripple-65b-preview.json", "title": "Ornith-1.5-9B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-ripple-65b-preview.json", "title": "Qwen3-30B-A3B-Instruct-2507 vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-ripple-65b-preview.json", "title": "Qwen3-4B-Instruct-2507 vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-ripple-65b-preview.json", "title": "Qwen3.5-0.8B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-ripple-65b-preview.json", "title": "Qwen3.5-122B-A10B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-ripple-65b-preview.json", "title": "Qwen3.5-2B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-ripple-65b-preview.json", "title": "Qwen3.5-397B-A17B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-ripple-65b-preview.json", "title": "Qwen3.5-4B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-ripple-65b-preview.json", "title": "Qwen3.5-9B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-ripple-65b-preview.json", "title": "Qwen3.6-27B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-ripple-65b-preview.json", "title": "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-smollm3-3b.json", "title": "Ripple-65B Preview vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-spark-x2-6-4b.json", "title": "Ripple-65B Preview vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-27b.json", "title": "Ripple-65B Preview vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Ripple-65B Preview vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-thinkingcap-qwen3-8-27b.json", "title": "Ripple-65B Preview vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-unlimited-ocr-2.json", "title": "Ripple-65B Preview vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-unlimited-ocr-2" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 20, "cosmos-reason2-2b": 65, "edge": "cosmos-reason2-2b", "key": "reach", "name": "Reach", "ripple-65b-preview": 45, "weight": 25 }, { "by": 5, "cosmos-reason2-2b": 60, "edge": "cosmos-reason2-2b", "key": "risk", "name": "Risk", "ripple-65b-preview": 55, "weight": 20 }, { "by": 10, "cosmos-reason2-2b": 65, "edge": "ripple-65b-preview", "key": "interface", "name": "Agent interface", "ripple-65b-preview": 75, "weight": 10 }, { "by": 10, "cosmos-reason2-2b": 39, "edge": "ripple-65b-preview", "key": "upkeep", "name": "Documentation and upkeep", "ripple-65b-preview": 49, "weight": 10 } ], "summary": "Cosmos-Reason2-2B scores 59.5 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 2 of 4 scored categories. Ripple-65B Preview leads on agent interface and documentation and upkeep. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "cosmos-reason2-2b": "A small local model for reasoning over video and images in robotics, driving and warehouse scenes, returning points, boxes and timed events as JSON. The 4.9 GB file runs in Transformers or vLLM, though NVIDIA asks for 24 GB of GPU memory. Development has moved to Cosmos 3, and the gated licence is revocable.", "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/cosmos-reason2-2b-vs-ripple-65b-preview", "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ripple-65b-preview.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ripple-65b-preview.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ripple-65b-preview.min.md" }, "markdown": "Cosmos-Reason2-2B scores 59.5 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 2 of 4 scored categories. Ripple-65B Preview leads on agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Cosmos-Reason2-2B: grade C, 59.5/100, rank #69 of 121 open-weight models. Markdown https://www.anchorterminal.com/tools/cosmos-reason2-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/cosmos-reason2-2b.json\n- Ripple-65B Preview: grade D, 53.3/100, rank #103 of 121 open-weight models. Markdown https://www.anchorterminal.com/tools/ripple-65b-preview.md · JSON https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 3674 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Cosmos-Reason2-2B (C)\n\nGood for: Agents that need a small local model to reason over camera footage and images for robotics, driving or smart spaces\n\nAhead on:\n- Reach, 65 against 45\n- Risk, 60 against 55\n\nWatch for: Gated behind a click-through licence that needs a Hugging Face account\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 65\n- Documentation and upkeep, 49 against 39\n\nWatch for: No released runtime runs the architecture, so running it means Ricursive's Python in SGLang 0.5.19 or Transformers' remote code\n\n\n## Why the edge\n\n### Reach: Cosmos-Reason2-2B +20\n\n- Cosmos-Reason2-2B (65): Memory earns the full 30 because the BF16 file is 4.9 GB, about 5.9 GB with the fifth added, though NVIDIA itself asks for 24 GB of GPU memory. Runtimes get 20 because the model keeps Qwen3-VL's architecture, which Transformers, vLLM, SGLang and llama.cpp run in released versions, while LM Studio's catalogue has only the base. Builds score 0, as NVIDIA publishes no quantised build of the 2B. The NVIDIA Open Model License allows free commercial use under a use policy (10). The gate is an automatic click-through that needs an account (5), and nothing hosts the 2B (0).\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### Agent interface: Ripple-65B Preview +10\n\n- Cosmos-Reason2-2B (65): A Jinja template ships as chat_template.json (20). Tools are declared only by the template it inherits from Qwen3-VL, with no NVIDIA documentation or parser (10), and nothing shows the 2B was trained for tool use (0). NVIDIA's prompt files ask for JSON with named keys for boxes, events and trajectories (15). Reasoning is asked for in the prompt and vLLM's qwen3 parser returns it apart (10). The card states 256K input tokens (10).\n- Ripple-65B Preview (75): Chat template 20, chat_template.jinja in the repository. Tool-calling format 25, Ricursive's server README says how tools are declared and calls come back, with the ripple parser in SGLang 0.5.19. Trained tool calling 20, one or more calls a turn and agentic-coding RL. Structured output 0, not documented. Reasoning control 0, the template has no thinking switch or effort levels. Context 10, long-context training at 2M-token sequences for a stated 1M.\n\n### Documentation and upkeep: Ripple-65B Preview +10\n\n- Cosmos-Reason2-2B (39): The card misses languages and a cut-off (14). Training data is named by dataset without sizes or a cut-off (5). The Hub repository last changed on 30 April 2026, 188 days before this check, which alone gives 5. NVIDIA's GitHub repository last changed with a README redirect to Cosmos 3 on 7 June 2026, 150 days back, and this run counts the publisher's documentation commits on this line (10). None of its 3 discussions has an NVIDIA reply, counted by author flags on 4 November (0). The 2B's weights were replaced in place in March with a dated line, and the family is now superseded by Cosmos 3 with a dated note (5). The gated files couldn't be checked against the card, which itself mixes float16 and BF16 and links a newer licence than the gate shows (5). 14 + 5 + 10 + 0 + 5 + 5 = 39.\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### Risk: Cosmos-Reason2-2B +5\n\n- Cosmos-Reason2-2B (60): File safety gets the full 25, one safetensors file loaded by Transformers' own code with a template, no remote code. Provenance gets 15 for files in NVIDIA's own organisation with hashes. The licence has a use policy and guardrail clause and NVIDIA can revoke or update it, so licence risk keeps 5 of 15. NVIDIA's PSIRT route and bulletins give the security programme 10. Agent guidance gets 5 for general cautions about guardrails and physical risk, and safety evaluation 0, with no safety results.\n- Ripple-65B Preview (55): File safety 15, safetensors only, less 10 because the documented way to run it loads Ricursive's Python. Provenance 15, the files are in the organisation ricursive.com links to, with a checksum on every file. Safety evaluation 10, Ricursive's own FORTRESS and StrongREJECT results. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, Apache 2.0. Security programme 0, no way to report a flaw.\n\n## Score by category\n\n| Category | Weight | Cosmos-Reason2-2B | Ripple-65B Preview | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 45 | Cosmos-Reason2-2B +20 |\n| Risk | 20% (20 this run) | 60 | 55 | Cosmos-Reason2-2B +5 |\n| Agent interface | 10% (10 this run) | 65 | 75 | Ripple-65B Preview +10 |\n| Documentation and upkeep | 10% (10 this run) | 39 | 49 | Ripple-65B Preview +10 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **59.5 · C** | **53.3 · D** | |\n\n## Facts side by side\n\n| Fact | Cosmos-Reason2-2B | Ripple-65B Preview |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | NVIDIA | 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 | NVIDIA Open Model License (custom, commercial use allowed, not an OSI licence); built on Qwen3-VL-2B-Instruct under Apache 2.0 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-03-10 | 2026-11-04 |\n| Terms last updated | 2025-10-24 | not read yet |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | yes | not read yet |\n| Terms restrict automated access | not found in the text | not read yet |\n| Terms restrict benchmarking | not found in the text | not read yet |\n| Terms or service can change without notice | not found in the text | not read yet |\n| Arbitration or class-action waiver | not found in the text | not read yet |\n| Agent reviews | 2.5/5 (4) | none |\n\n## Verdicts\n\n**Cosmos-Reason2-2B.** A small local model for reasoning over video and images in robotics, driving and warehouse scenes, returning points, boxes and timed events as JSON. The 4.9 GB file runs in Transformers or vLLM, though NVIDIA asks for 24 GB of GPU memory. Development has moved to Cosmos 3, and the gated licence is revocable.\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### Cosmos-Reason2-2B\n\n1. Accept the licence on the Hugging Face card with a signed-in account and set HF_TOKEN before downloading\n2. Serve it with `vllm serve nvidia/Cosmos-Reason2-2B --max-model-len 16384 --media-io-kwargs '{\"video\": {\"num_frames\": -1}}' --reasoning-parser qwen3`, keeping max-model-len between 8,192 and 16,384 as NVIDIA advises\n3. For local video or images, make a folder for them (`mkdir -p media`) and pass `--allowed-local-media-path \"$(pwd)/media\"`, so the server can read only that folder, not everything under the directory you start it from\n4. Sample video at 4 fps and allow at least 4,096 output tokens so the reasoning isn't cut off\n5. For reasoning, add the card's instruction to answer with a think block and then the answer; leave it out for a direct reply\n6. Use NVIDIA's prompt files (prompts/*.yaml in nvidia-cosmos/cosmos-reason2) when you want JSON boxes, events or trajectories, and validate the JSON yourself\n7. Check its plans before a robot acts on them, since NVIDIA's own safety card warns of collision and manipulation risks\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, Cosmos-Reason2-2B or Ripple-65B Preview?\n\nCosmos-Reason2-2B scores 59.5 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 2 of 4 scored categories. Ripple-65B Preview leads on agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ripple-65b-preview.json, and with the fewest tokens: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ripple-65b-preview.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"cosmos-reason2-2b\", \"b\": \"ripple-65b-preview\"}`. From a terminal: `anchor compare cosmos-reason2-2b ripple-65b-preview`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/cosmos-reason2-2b.json and https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.json\n\n## Other comparisons with Cosmos-Reason2-2B or Ripple-65B Preview\n\n- [Agents-A1-4B vs Cosmos-Reason2-2B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-cosmos-reason2-2b.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 Cosmos-Reason2-2B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cosmos-reason2-2b.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 Cosmos-Reason2-2B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-cosmos-reason2-2b.md)\n- [Apodex-1.1-mini vs Ripple-65B Preview](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ripple-65b-preview.md)\n- [BigBang-v1 vs Cosmos-Reason2-2B](https://www.anchorterminal.com/compare/bigbang-v1-vs-cosmos-reason2-2b.md)\n- [BigBang-v1 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/bigbang-v1-vs-ripple-65b-preview.md)\n- [Carbon-X vs Cosmos-Reason2-2B](https://www.anchorterminal.com/compare/carbon-x-vs-cosmos-reason2-2b.md)\n- [Carbon-X vs Ripple-65B Preview](https://www.anchorterminal.com/compare/carbon-x-vs-ripple-65b-preview.md)\n- [Cosmos-Reason2-2B vs Darwin-36B-Opus](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-darwin-36b-opus.md)\n- [Cosmos-Reason2-2B vs DeepSeek-V3.2](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-deepseek-v3-2.md)\n- [Cosmos-Reason2-2B vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-deepseek-v4-1-flash.md)\n- [Cosmos-Reason2-2B vs DeepSeek-V4-Flash-0731](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-deepseek-v4-flash-0731.md)\n- [Cosmos-Reason2-2B vs DiffusionGemma 26B A4B IT](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-diffusiongemma-26b-a4b-it.md)\n- [Cosmos-Reason2-2B vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-12b-it.md)\n- [Cosmos-Reason2-2B vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-26b-a4b-it.md)\n- [Cosmos-Reason2-2B vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-31b-it.md)\n- [Cosmos-Reason2-2B vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-e2b-it.md)\n- [Cosmos-Reason2-2B vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-e4b-it.md)\n- [Cosmos-Reason2-2B vs GLM-4.5-Air](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-4-5-air.md)\n- [Cosmos-Reason2-2B vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-4-7-flash.md)\n- [Cosmos-Reason2-2B vs GLM-5.3](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-5-3.md)\n- [Cosmos-Reason2-2B vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-5-3-flash.md)\n- [Cosmos-Reason2-2B vs gpt-oss-120b](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gpt-oss-120b.md)\n- [Cosmos-Reason2-2B vs gpt-oss-20b](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gpt-oss-20b.md)\n- [Cosmos-Reason2-2B vs Granite 4.2 3B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-granite-4-2-3b.md)\n- [Cosmos-Reason2-2B vs Hy3](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-hy3.md)\n- [Cosmos-Reason2-2B vs Inkling](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-inkling.md)\n- [Cosmos-Reason2-2B vs Inkling-Small](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-inkling-small.md)\n- [Cosmos-Reason2-2B vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Cosmos-Reason2-2B vs Kimi K3](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-kimi-k3.md)\n- [Cosmos-Reason2-2B vs Kolibri-1](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-kolibri-1.md)\n- [Cosmos-Reason2-2B vs Kronos](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-kronos.md)\n- [Cosmos-Reason2-2B vs Laguna M.1](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-laguna-m-1.md)\n- [Cosmos-Reason2-2B vs Laguna S 2.1](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-laguna-s-2-1.md)\n- [Cosmos-Reason2-2B vs Laguna XS 2.1](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-laguna-xs-2-1.md)\n- [Cosmos-Reason2-2B vs LBS-27B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lbs-27b.md)\n- [Cosmos-Reason2-2B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm2-5-2-6b.md)\n- [Cosmos-Reason2-2B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ling-3-0-tiny.md)\n- [Cosmos-Reason2-2B vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Cosmos-Reason2-2B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minicpm-o-4-5.md)\n- [Cosmos-Reason2-2B vs MiniCPM5-2B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minicpm5-2b.md)\n- [Cosmos-Reason2-2B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minimax-m2-5.md)\n- [Cosmos-Reason2-2B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minimax-m2-7.md)\n- [Cosmos-Reason2-2B vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minimax-m3-1-flash.md)\n- [Cosmos-Reason2-2B vs Mistral Large 4](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-mistral-large-4.md)\n- [Cosmos-Reason2-2B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-muse-glimmer-30b.md)\n- [Cosmos-Reason2-2B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nanbeige-4-3-33b.md)\n- [Cosmos-Reason2-2B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Cosmos-Reason2-2B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Cosmos-Reason2-2B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-30b-a3b.md)\n- [Cosmos-Reason2-2B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-4b.md)\n- [Cosmos-Reason2-2B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Cosmos-Reason2-2B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-super-120b-a12b.md)\n- [Cosmos-Reason2-2B vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-ultra-550b-a55b.md)\n- [Cosmos-Reason2-2B vs occamy-1.0](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-occamy-1-0.md)\n- [Cosmos-Reason2-2B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-olmo-3-7b-think.md)\n- [Cosmos-Reason2-2B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-35b-a3b.md)\n- [Cosmos-Reason2-2B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-397b.md)\n- [Cosmos-Reason2-2B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-9b.md)\n- [Cosmos-Reason2-2B vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Cosmos-Reason2-2B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-4b-instruct-2507.md)\n- [Cosmos-Reason2-2B vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-5-0-8b.md)\n- [Cosmos-Reason2-2B vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-5-122b-a10b.md)\n- [Cosmos-Reason2-2B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-5-2b.md)\n- [Cosmos-Reason2-2B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-5-397b-a17b.md)\n- [Cosmos-Reason2-2B vs Qwen3.5-4B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-5-4b.md)\n- [Cosmos-Reason2-2B vs Qwen3.5-9B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-5-9b.md)\n- [Cosmos-Reason2-2B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-6-27b.md)\n- [Cosmos-Reason2-2B vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-6-35b-a3b.md)\n- [Cosmos-Reason2-2B vs Qwen3.8-27B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-8-27b.md)\n- [Cosmos-Reason2-2B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-8-flash-next.md)\n- [Cosmos-Reason2-2B vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Cosmos-Reason2-2B vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-coder-next.md)\n- [Cosmos-Reason2-2B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Cosmos-Reason2-2B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-vl-2b-instruct.md)\n- [Cosmos-Reason2-2B vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-vl-30b-a3b-instruct.md)\n- [Cosmos-Reason2-2B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-vl-32b-instruct.md)\n- [Cosmos-Reason2-2B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-vl-4b-instruct.md)\n- [Cosmos-Reason2-2B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-vl-8b-instruct.md)\n- [Cosmos-Reason2-2B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwopus3-8-27b-flash-v2.md)\n- [Cosmos-Reason2-2B vs Recursive Flint](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-recursive-flint.md)\n- [Cosmos-Reason2-2B vs SmolLM3-3B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-smollm3-3b.md)\n- [Cosmos-Reason2-2B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-spark-x2-6-4b.md)\n- [Cosmos-Reason2-2B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-swift-1-5-qwen3-8-27b.md)\n- [Cosmos-Reason2-2B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Cosmos-Reason2-2B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-thinkingcap-qwen3-8-27b.md)\n- [Cosmos-Reason2-2B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-unlimited-ocr-2.md)\n- [Darwin-36B-Opus vs Ripple-65B Preview](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ripple-65b-preview.md)\n- [DeepSeek-V3.2 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ripple-65b-preview.md)\n- [DeepSeek V4.1 Flash vs Ripple-65B Preview](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ripple-65b-preview.md)\n- [DeepSeek-V4-Flash-0731 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ripple-65b-preview.md)\n- [DiffusionGemma 26B A4B IT vs Ripple-65B Preview](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ripple-65b-preview.md)\n- [Gemma 4 12B IT vs Ripple-65B Preview](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ripple-65b-preview.md)\n- [Gemma 4 26B A4B IT vs Ripple-65B Preview](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ripple-65b-preview.md)\n- [Gemma 4 31B IT vs Ripple-65B Preview](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ripple-65b-preview.md)\n- [Gemma 4 E2B IT vs Ripple-65B Preview](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ripple-65b-preview.md)\n- [Gemma 4 E4B IT vs Ripple-65B Preview](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ripple-65b-preview.md)\n- [GLM-4.5-Air vs Ripple-65B Preview](https://www.anchorterminal.com/compare/glm-4-5-air-vs-ripple-65b-preview.md)\n- [GLM-4.7-Flash vs Ripple-65B Preview](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ripple-65b-preview.md)\n- [GLM-5.3-Flash vs Ripple-65B Preview](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ripple-65b-preview.md)\n- [GLM-5.3 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/glm-5-3-vs-ripple-65b-preview.md)\n- [gpt-oss-120b vs Ripple-65B Preview](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ripple-65b-preview.md)\n- [gpt-oss-20b vs Ripple-65B Preview](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ripple-65b-preview.md)\n- [Granite 4.2 3B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ripple-65b-preview.md)\n- [Hy3 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/hy3-vs-ripple-65b-preview.md)\n- [Inkling-Small vs Ripple-65B Preview](https://www.anchorterminal.com/compare/inkling-small-vs-ripple-65b-preview.md)\n- [Inkling vs Ripple-65B Preview](https://www.anchorterminal.com/compare/inkling-vs-ripple-65b-preview.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ripple-65B Preview](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ripple-65b-preview.md)\n- [Kimi K3 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/kimi-k3-vs-ripple-65b-preview.md)\n- [Kolibri-1 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/kolibri-1-vs-ripple-65b-preview.md)\n- [Kronos vs Ripple-65B Preview](https://www.anchorterminal.com/compare/kronos-vs-ripple-65b-preview.md)\n- [Laguna M.1 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/laguna-m-1-vs-ripple-65b-preview.md)\n- [Laguna S 2.1 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ripple-65b-preview.md)\n- [Laguna XS 2.1 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ripple-65b-preview.md)\n- [LBS-27B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/lbs-27b-vs-ripple-65b-preview.md)\n- [LFM2.5-2.6B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ripple-65b-preview.md)\n- [Ling-3.0-tiny vs Ripple-65B Preview](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-ripple-65b-preview.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-ripple-65b-preview.md)\n- [MiniCPM-o 4.5 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ripple-65b-preview.md)\n- [MiniCPM5-2B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/minicpm5-2b-vs-ripple-65b-preview.md)\n- [MiniMax-M2.5 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", "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": "2026-11-05", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Cosmos-Reason2-2B vs Ripple-65B Preview", "url": "" } ], "description": "Cosmos-Reason2-2B scores 59.5 (C) to Ripple-65B Preview's 53.3 (D) for open-weight models you run yourself. 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