{ "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": 1518, "categoryRank": 75, "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", "nemotron-3-5-transcribe-30b-a4b", "parakeet-tdt-0-6b-v3", "parakeet-unified-en-0-6b", "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": "2026-11-10T16:08:00.271598775Z" }, "domain": { "domain": "nvidia.com", "registered": "1993-04-20", "source": "https://rdap.verisign.com/com/v1/domain/nvidia.com", "checkedAt": "2026-11-08T15:03:11.895084282Z" }, "pages": [ { "url": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/", "kind": "terms", "status": 304, "checkedAt": "2026-11-10T16:43:29.102317163Z", "changedAt": "2026-10-31T06:30:16.800317464Z", "fingerprint": "657e4e24049b" } ], "updatedAt": "2026-11-10T16:43:29.102317163Z" } }, "answer": "WeVisOne-VL-2B scores 61 (C) on agent readiness against Cosmos-Reason2-2B's 59.5 (C), and leads in 3 of 4 scored categories. Cosmos-Reason2-2B leads on risk.", "b": { "slug": "wevisone-vl-2b", "name": "WeVisOne-VL-2B", "vendor": "Tencent (WeChat Vision)", "vendorUrl": "https://www.tencent.com", "kind": "weights", "category": "open-weights", "summary": "WeVisOne-VL-2B is an open-weight perception model of 2.4B parameters from Tencent's WeChat Vision team, built on Qwen3-VL. Name something in an image and it returns boxes, points, counts, text, masks or alpha mattes. Apache 2.0.", "url": "https://www.anchorterminal.com/tools/wevisone-vl-2b", "markdownUrl": "https://www.anchorterminal.com/tools/wevisone-vl-2b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/wevisone-vl-2b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/wevisone-vl-2b.json", "dns": "wevisone-vl-2b.grade.anchorterminal.com", "repo": "https://huggingface.co/tencent/WeVisOne-VL-2B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "tencent/WeVisOne-VL-2B" } ], "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 under Apache 2.0. Running it yourself costs the hardware, 4.88 GB in BF16 (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://github.com/Tencent/WeVisOne-VL", "capabilities": [ "model.open-weights", "docs.ocr" ], "tags": [ "open-weights", "model", "vision", "grounding", "object-detection", "segmentation", "matting", "ocr", "counting", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-10-28", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 65, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 70, "reach": 70, "risk": 55, "upkeep": 48 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A small Apache 2.0 perception model that finds, counts, reads and cuts out what you name in an image, and says None when it isn't there. Boxes and points run in vLLM, SGLang or Transformers. Masks and mattes need Tencent's package and a pickle file. Two weeks old, with no questions answered yet and no safety results.", "bestFor": "Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence", "strengths": [ "Seven perception tasks in one model, from boxes to alpha mattes", "Returns None for a target that isn't in the image", "Apache 2.0, ungated", "Each task's input and output is documented", "4.88 GB in BF16, so it fits a 8 GB machine" ], "weaknesses": [ "The pixel decoder ships as a pickle file", "Masks and mattes need Tencent's package from GitHub", "No quantised build from Tencent", "One image and one turn at a time, no video", "No reply from Tencent on the repository's discussions", "No context length, languages, safety results or data sources on the card" ], "agentNotes": [ "Use Tencent's package: `WeVisOneVL(\"tencent/WeVisOne-VL-2B\", backend=\"vllm\")` then `model.predict(image, task=..., query=...)`", "Tasks are detection, referring, point, count_box, count_point, ocr, ocr_grounding, segmentation and matting", "A None answer means the target isn't in the image. Treat it as a result, not an error", "mask_decoder.pt is a pickle. Load it only from the tencent repository, and check its SHA-256 against the Hub's", "Send one image a turn. Video grids are rejected by the decoder", "The card says it isn't for identifying individuals or for high-impact decisions on its own" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 70, "reach": 70, "risk": 55, "upkeep": 48 }, "provenanceScore": 68, "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": "git clone https://github.com/Tencent/WeVisOne-VL.git \u0026\u0026 cd WeVisOne-VL\npip install -r requirements.txt \u0026\u0026 pip install -e .\n# then, in Python: WeVisOneVL(\"tencent/WeVisOne-VL-2B\", backend=\"transformers\") or backend=\"vllm\"", "http": "# No HTTP API of its own. Tencent's package calls model.predict(image, task=\"detection\", query=[...]).\n# For boxes and points only, vLLM serves the backbone at http://localhost:8000/v1 with: vllm serve tencent/WeVisOne-VL-2B" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/wevisone-vl-2b" }, "sameCompany": [ "hy-memory", "tencentdb-agent-memory", "hunyuanocr", "hy-memory-30b-a3b", "hy3", "wevisone-vl-4b" ], "weights": { "trainedBy": "Tencent WeChat Vision", "params": "2.4B", "goodFor": "Finding, counting, reading and cutting out what you name in an image, on a 8 GB machine.", "runtimes": [ "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "WeVisOne-VL-2B (BF16 safetensors)", "url": "https://huggingface.co/tencent/WeVisOne-VL-2B/blob/main/model.safetensors", "who": "publisher", "precision": "bf16", "sizeGB": 4.88, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ], "note": "The only build. Masks and mattes also need mask_decoder.pt and Tencent's package" } ], "run": "vllm serve tencent/WeVisOne-VL-2B", "runBuild": "WeVisOne-VL-2B (BF16 safetensors)", "checked": "2026-11-10", "minMemoryGB": 8 }, "family": { "lab": "Tencent (Hunyuan / Hy)", "family": "WeVisOne-VL", "url": "https://www.anchorterminal.com/open-weights/#tencent", "others": [ { "name": "WeVisOne-VL-4B", "url": "https://huggingface.co/tencent/WeVisOne-VL-4B", "params": "4.8B (4,830,581,248 by the Hub's safetensors count)", "listing": "https://www.anchorterminal.com/tools/wevisone-vl-4b" } ], "olderCount": 22 }, "area": "models", "provenance": { "legalEntity": "Tencent (named in the LICENSE file's copyright line)", "domain": "tencent.com", "domainRegistered": "1998-09-14", "endpointOnVendorDomain": null, "terms": "https://huggingface.co/tencent/WeVisOne-VL-2B/blob/main/LICENSE", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/tencent/WeVisOne-VL-2B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The card names WeChat Vision, Tencent Inc. The domain's registration date is carried from our other Tencent listings.", "Nothing is hosted under this listing, so the model licence stands in for terms of service and no privacy policy is scored.", "The changelog is the commit history.", "Tencent's Security Response Center takes reports company-wide, per the Hy3 listing; nothing on the card links it." ], "score": 68 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/wevisone-vl-2b.json" }, "edges": [ { "by": 9, "category": "upkeep", "leads": "wevisone-vl-2b", "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 8, Intended use, limitations and the architecture are on the card. Training data 5, A general description in the README of a data engine and over 100 million annotated images, with no sources named and no cut-off. Last change 20, the last commit, on 28 October 2026, put the weights back in BF16, 13 days before this check. Responsiveness 0, the one discussion that isn't a pull request (23 October 2026) has no reply from a tencent member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release. File health 10, Config, template and card agree on the Qwen3-VL classes, and the card names mask_decoder.pt as required and it's there. 8 + 5 + 20 + 0 + 5 + 10 = 48." }, "scores": { "a": 39, "b": 48 } }, { "by": 5, "category": "reach", "leads": "wevisone-vl-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 30, the only build from Tencent is the BF16 safetensors file with its decoder, 4.92 GB, and 4.92 GB × 1.2 = 5.9 GB, within 16 GB. No runtime library has a build. Runtimes 15, the architecture is Qwen3VLForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors file, three at 5 each. Builds 0, Tencent publishes the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata with the LICENSE file in the repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 30 + 15 + 0 + 15 + 10 + 0 = 70." }, "scores": { "a": 65, "b": 70 } }, { "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, the backbone is safetensors, but mask_decoder.pt, a PyTorch pickle the card calls a required component, sits beside it. Provenance 15, the files sit in the tencent organisation, which Tencent's GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card or in the README. Agent guidance 5, General cautions: the card says the release is for research, isn't for autonomous high-impact decisions or identifying individuals, and lists where masks go wrong. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide, as the Hy3 listing records (5). 15 + 15 + 0 + 5 + 15 + 5 = 55." }, "scores": { "a": 60, "b": 55 } }, { "by": 5, "category": "interface", "leads": "wevisone-vl-2b", "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 (5,292 characters), Qwen3-VL's template. Tool-calling format 25, Read for a perception model as what it takes and returns. Trained tool calling 10, Read for a perception model as the inputs it was trained to use. Structured output 15, Read for a perception model as typed outputs. Reasoning control 0, the card says it writes structured output directly with no chain-of-thought reasoning. Context 0, Tencent states no context length for it. 20 + 25 + 10 + 15 + 0 + 0 = 70." }, "scores": { "a": 65, "b": 70 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "NVIDIA", "b": "Tencent (WeChat Vision)", "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": "2026-10-28", "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": "WeVisOne-VL-2B scores 61 (C) on agent readiness against Cosmos-Reason2-2B's 59.5 (C), and leads in 3 of 4 scored categories. Cosmos-Reason2-2B leads on risk.", "question": "Which is better for AI agents, Cosmos-Reason2-2B or WeVisOne-VL-2B?" } ], "goodFor": [ { "aheadOn": [ "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": [ "Reach, 70 against 65", "Agent interface, 70 against 65", "Documentation and upkeep, 48 against 39" ], "also": null, "goodFor": "Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence", "slug": "wevisone-vl-2b", "watchFor": "The pixel decoder ships as a pickle file" } ], "history": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-cosmos-reason2-2b.json", "title": 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"https://www.anchorterminal.com/compare/granite-4-2-8b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-2b.json", "title": "Hy-Memory-30B-A3B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-wevisone-vl-2b.json", "title": "Hy3 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/hy3-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-wevisone-vl-2b.json", "title": "Inkling-Small vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-wevisone-vl-2b.json", "title": "Inkling vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/inkling-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-wevisone-vl-2b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-2b.json", "title": "Kimi K3.1 Pro vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-wevisone-vl-2b.json", "title": "Kimi K3 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-wevisone-vl-2b.json", "title": "Kolibri-1 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-wevisone-vl-2b.json", "title": "Kronos vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/kronos-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-wevisone-vl-2b.json", "title": "Laguna M.1 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-wevisone-vl-2b.json", "title": "Laguna S 2.1 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-wevisone-vl-2b.json", "title": "Laguna XS 2.1 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-wevisone-vl-2b.json", "title": "LBS-27B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-wevisone-vl-2b.json", "title": "LFM2.5-2.6B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-wevisone-vl-2b.json", "title": "LFM3-1.7B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-wevisone-vl-2b.json", "title": "LFM3-Audio-2B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-wevisone-vl-2b.json", "title": "LFM3-Omni-2B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-2b.json", "title": "LFM3-VL-2B vs WeVisOne-VL-2B", "url": 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"https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-2b.json", "title": "MiMo-V2.6-Flash-RL vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-wevisone-vl-2b.json", "title": "MiniCPM-o 4.5 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-2b.json", "title": "MiniCPM-V 4.7 1B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-wevisone-vl-2b.json", "title": "MiniCPM-V 4.7 35B-A3B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-wevisone-vl-2b.json", "title": 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"json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-wevisone-vl-2b.json", "title": "Muse Glimmer 30B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-2b.json", "title": "Nanbeige4.2-3B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-wevisone-vl-2b.json", "title": "Nanbeige4.3-33B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-wevisone-vl-2b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-wevisone-vl-2b" }, { "json": 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"https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-wevisone-vl-2b.json", "title": "Qwen3-4B-Instruct-2507 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-wevisone-vl-2b.json", "title": "Qwen3.5-0.8B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-wevisone-vl-2b.json", "title": "Qwen3.5-122B-A10B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-wevisone-vl-2b.json", "title": "Qwen3.5-2B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-wevisone-vl-2b.json", "title": "Qwen3.5-397B-A17B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-wevisone-vl-2b.json", "title": "Qwen3.5-4B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-2b.json", "title": "Qwen3.5-9B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-wevisone-vl-2b.json", "title": "Qwen3.6-27B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-wevisone-vl-2b.json", "title": "Qwen3.6-35B-A3B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-wevisone-vl-2b.json", "title": "Qwen3.8-27B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-wevisone-vl-2b.json", "title": "Qwen3.8-Flash-Next vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-wevisone-vl-2b.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-wevisone-vl-2b.json", "title": "Qwen3-Coder-Next vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-wevisone-vl-2b.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-wevisone-vl-2b.json", "title": "Qwen3-VL-2B-Instruct vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-wevisone-vl-2b.json", "title": "Qwen3-VL-30B-A3B-Instruct vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-wevisone-vl-2b.json", "title": "Qwen3-VL-32B-Instruct vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-2b.json", "title": "Qwen3-VL-4B-Instruct vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-wevisone-vl-2b.json", "title": "Qwen3-VL-8B-Instruct vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-wevisone-vl-2b.json", "title": "Qwopus3.8-27B-Flash-V2 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-wevisone-vl-2b.json", "title": "Recursive Flint vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-wevisone-vl-2b" }, { "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/sensenova-loop-9b-preview-vs-wevisone-vl-2b.json", "title": "SenseNova-Loop-9B-Preview vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-2b.json", "title": "SmolLM3-3B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-2b.json", "title": "Spark-X2.6-4B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-2b.json", "title": "Step 3.7 Flash vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-wevisone-vl-2b.json", "title": "Swift 1.5 Qwen3.8-27B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-wevisone-vl-2b.json", "title": "Swift 1.5 Qwen3.8-Flash-Next vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-wevisone-vl-2b.json", "title": "ThinkingCap (Qwen3.8 27B) vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-wevisone-vl-4b.json", "title": "WeVisOne-VL-2B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-zaya1-8b.json", "title": "WeVisOne-VL-2B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-2b.json", "title": "Baidu Unlimited-OCR-2 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-2b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 5, "cosmos-reason2-2b": 65, "edge": "wevisone-vl-2b", "key": "reach", "name": "Reach", "weight": 25, "wevisone-vl-2b": 70 }, { "by": 5, "cosmos-reason2-2b": 60, "edge": "cosmos-reason2-2b", "key": "risk", "name": "Risk", "weight": 20, "wevisone-vl-2b": 55 }, { "by": 5, "cosmos-reason2-2b": 65, "edge": "wevisone-vl-2b", "key": "interface", "name": "Agent interface", "weight": 10, "wevisone-vl-2b": 70 }, { "by": 9, "cosmos-reason2-2b": 39, "edge": "wevisone-vl-2b", "key": "upkeep", "name": "Documentation and upkeep", "weight": 10, "wevisone-vl-2b": 48 } ], "summary": "WeVisOne-VL-2B scores 61 (C) on agent readiness against Cosmos-Reason2-2B's 59.5 (C), and leads in 3 of 4 scored categories. Cosmos-Reason2-2B leads on risk. 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.", "wevisone-vl-2b": "A small Apache 2.0 perception model that finds, counts, reads and cuts out what you name in an image, and says None when it isn't there. Boxes and points run in vLLM, SGLang or Transformers. Masks and mattes need Tencent's package and a pickle file. Two weeks old, with no questions answered yet and no safety results." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-wevisone-vl-2b", "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-wevisone-vl-2b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-wevisone-vl-2b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-wevisone-vl-2b.min.md" }, "markdown": "WeVisOne-VL-2B scores 61 (C) on agent readiness against Cosmos-Reason2-2B's 59.5 (C), and leads in 3 of 4 scored categories. Cosmos-Reason2-2B leads on risk. Both do open-weight models you run yourself.\n\n- Cosmos-Reason2-2B: grade C, 59.5/100, rank #95 of 160 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- WeVisOne-VL-2B: grade C, 61/100, rank #83 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/wevisone-vl-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/wevisone-vl-2b.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### 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- Risk, 60 against 55\n\nWatch for: Gated behind a click-through licence that needs a Hugging Face account\n\n### WeVisOne-VL-2B (C)\n\nGood for: Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence\n\nAhead on:\n- Reach, 70 against 65\n- Agent interface, 70 against 65\n- Documentation and upkeep, 48 against 39\n\nWatch for: The pixel decoder ships as a pickle file\n\n\n## Why the edge\n\n### Documentation and upkeep: WeVisOne-VL-2B +9\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- WeVisOne-VL-2B (48): Model card 8, Intended use, limitations and the architecture are on the card. Training data 5, A general description in the README of a data engine and over 100 million annotated images, with no sources named and no cut-off. Last change 20, the last commit, on 28 October 2026, put the weights back in BF16, 13 days before this check. Responsiveness 0, the one discussion that isn't a pull request (23 October 2026) has no reply from a tencent member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release. File health 10, Config, template and card agree on the Qwen3-VL classes, and the card names mask_decoder.pt as required and it's there. 8 + 5 + 20 + 0 + 5 + 10 = 48.\n\n### Reach: WeVisOne-VL-2B +5\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- WeVisOne-VL-2B (70): Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 4.92 GB, and 4.92 GB × 1.2 = 5.9 GB, within 16 GB. No runtime library has a build. Runtimes 15, the architecture is Qwen3VLForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors file, three at 5 each. Builds 0, Tencent publishes the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata with the LICENSE file in the repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 30 + 15 + 0 + 15 + 10 + 0 = 70.\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- WeVisOne-VL-2B (55): File safety 15, the backbone is safetensors, but mask_decoder.pt, a PyTorch pickle the card calls a required component, sits beside it. Provenance 15, the files sit in the tencent organisation, which Tencent's GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card or in the README. Agent guidance 5, General cautions: the card says the release is for research, isn't for autonomous high-impact decisions or identifying individuals, and lists where masks go wrong. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide, as the Hy3 listing records (5). 15 + 15 + 0 + 5 + 15 + 5 = 55.\n\n### Agent interface: WeVisOne-VL-2B +5\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- WeVisOne-VL-2B (70): Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template. Tool-calling format 25, Read for a perception model as what it takes and returns. Trained tool calling 10, Read for a perception model as the inputs it was trained to use. Structured output 15, Read for a perception model as typed outputs. Reasoning control 0, the card says it writes structured output directly with no chain-of-thought reasoning. Context 0, Tencent states no context length for it. 20 + 25 + 10 + 15 + 0 + 0 = 70.\n\n## Score by category\n\n| Category | Weight | Cosmos-Reason2-2B | WeVisOne-VL-2B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 70 | WeVisOne-VL-2B +5 |\n| Risk | 20% (20 this run) | 60 | 55 | Cosmos-Reason2-2B +5 |\n| Agent interface | 10% (10 this run) | 65 | 70 | WeVisOne-VL-2B +5 |\n| Documentation and upkeep | 10% (10 this run) | 39 | 48 | WeVisOne-VL-2B +9 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **59.5 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Cosmos-Reason2-2B | WeVisOne-VL-2B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | NVIDIA | Tencent (WeChat Vision) |\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 | 2026-10-28 |\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**WeVisOne-VL-2B.** A small Apache 2.0 perception model that finds, counts, reads and cuts out what you name in an image, and says None when it isn't there. Boxes and points run in vLLM, SGLang or Transformers. Masks and mattes need Tencent's package and a pickle file. Two weeks old, with no questions answered yet and no safety results.\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### WeVisOne-VL-2B\n\n1. Use Tencent's package: `WeVisOneVL(\"tencent/WeVisOne-VL-2B\", backend=\"vllm\")` then `model.predict(image, task=..., query=...)`\n2. Tasks are detection, referring, point, count_box, count_point, ocr, ocr_grounding, segmentation and matting\n3. A None answer means the target isn't in the image. Treat it as a result, not an error\n4. mask_decoder.pt is a pickle. Load it only from the tencent repository, and check its SHA-256 against the Hub's\n5. Send one image a turn. Video grids are rejected by the decoder\n6. The card says it isn't for identifying individuals or for high-impact decisions on its own\n\n## Questions\n\n### Which is better for AI agents, Cosmos-Reason2-2B or WeVisOne-VL-2B?\n\nWeVisOne-VL-2B scores 61 (C) on agent readiness against Cosmos-Reason2-2B's 59.5 (C), and leads in 3 of 4 scored categories. Cosmos-Reason2-2B leads on risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-wevisone-vl-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-wevisone-vl-2b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"cosmos-reason2-2b\", \"b\": \"wevisone-vl-2b\"}`. From a terminal: `anchor compare cosmos-reason2-2b wevisone-vl-2b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/cosmos-reason2-2b.json and https://www.anchorterminal.com/api/v1/tools/wevisone-vl-2b.json\n\n## Other comparisons with Cosmos-Reason2-2B or WeVisOne-VL-2B\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 WeVisOne-VL-2B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-wevisone-vl-2b.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 WeVisOne-VL-2B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-wevisone-vl-2b.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 WeVisOne-VL-2B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-wevisone-vl-2b.md)\n- 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[WeVisOne-VL-2B vs ZAYA1-8B](https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-zaya1-8b.md)\n- [Baidu Unlimited-OCR-2 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-2b.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/", "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": 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