{ "data": { "a": { "slug": "cola2", "name": "Cola2", "vendor": "ByteDance (Seed)", "vendorUrl": "https://github.com/ByteDance-Seed", "kind": "weights", "category": "open-weights", "summary": "Cola2 is a research checkpoint from ByteDance Seed, a base language model of 8.8B parameters that generates text through continuous latents with a diffusion transformer and a token decoder. Not a chat model, and the weights carry no stated licence.", "url": "https://www.anchorterminal.com/tools/cola2", "markdownUrl": "https://www.anchorterminal.com/tools/cola2.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/cola2.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/cola2.json", "dns": "cola2.grade.anchorterminal.com", "repo": "https://huggingface.co/ByteDance-Seed/Cola2", "license": "None stated for the weights (the code is Apache 2.0)", "transports": [], "packages": [ { "registry": "huggingface", "name": "ByteDance-Seed/Cola2" } ], "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, but nothing says what you may do with the weights. The card says the code is Apache 2.0 and that the weights remain subject to their respective terms, without naming them. Running it yourself costs the hardware: 35.1 GB of F32 files, loaded as BF16 in the card's example (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/ByteDance-Seed/Cola2", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "base-model", "research", "diffusion", "continuous-latent", "no-licence", "safetensors", "custom-code", "self-hosted" ], "lastRelease": "2026-11-06", "graded": true, "anchor": { "graded": true, "score": 33.9, "grade": "F", "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": 113, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 20, "reach": 25, "risk": 40, "upkeep": 58 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A research release, and an honest one: ByteDance Seed says it's a base model with an unconverged latent stage, scoring below Llama-3-8B on its own table. It's here for people studying continuous-latent generation. Agents can't use it as a model: 32-token completions, no chat or tools, the repository's own code to load it, and no licence on the weights.", "bestFor": "Researchers working on latent or diffusion language models who want a checkpoint to study", "strengths": [ "A documented new architecture with a paper and a reproduction protocol", "Evaluation data and checksums ship with the weights", "A security policy that points to ByteDance's security centre", "The model card says plainly what it isn't for" ], "weaknesses": [ "No licence stated for the weights", "A base model: no chat, no tools, 32 new tokens by default", "Loads only with the repository's own Python, on Transformers 4.x", "35 GB of F32 files and no quantised build", "Below Llama-3-8B on the card's own eight-task table", "No stated context length or cut-off" ], "agentNotes": [ "Don't plan to use this as your model. It completes short prompts and wasn't tuned to follow instructions", "Don't use it commercially on the strength of this listing. The weights have no stated licence", "If you study it, read modeling_cola2.py first: trust_remote_code runs it", "Pin Transformers below 5, as the card requires", "Pass a seeded torch.Generator to generate(). Output varies between runs even with greedy decoding", "Check the download with `sha256sum -c SHA256SUMS`" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "F", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 33.9 } ], "editorialScores": { "interface": 20, "reach": 25, "risk": 40, "upkeep": 58 }, "provenanceScore": 58, "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": "pip install \"torch\u003e=2.2\" \"transformers\u003e=4.51,\u003c5\" \"safetensors\u003e=0.4\" accelerate\n# then AutoModel.from_pretrained(\"ByteDance-Seed/Cola2\", trust_remote_code=True, torch_dtype=torch.bfloat16)\n# trust_remote_code runs the three Python files in the repository. Read them first", "http": "# No server. The model runs in process through Transformers, and the GitHub repository has a\n# command-line chat script (scripts/run_chat.sh) and a benchmark runner" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/cola2" }, "weights": { "trainedBy": "ByteDance Seed", "builtOn": "The decoder and encoder embedding are initialised from Qwen3-4B-Base, per the GitHub model card", "params": "8.8B", "goodFor": "Research on continuous-latent text generation: short completions, cloze and multiple-choice experiments, and reproducing the paper's numbers.", "runtimes": [], "builds": [ { "name": "Cola2 (F32 safetensors)", "url": "https://huggingface.co/ByteDance-Seed/Cola2/tree/main", "who": "publisher", "precision": "f32", "sizeGB": 35.07, "memoryGB": 48, "format": "safetensors", "note": "Eight shards in 32-bit floats. The card's example loads them as BF16" } ], "run": "python -c \"from transformers import AutoModel; AutoModel.from_pretrained('ByteDance-Seed/Cola2', trust_remote_code=True)\"", "runBuild": "Cola2 (F32 safetensors)", "checked": "2026-11-10", "minMemoryGB": 48 }, "family": { "lab": "ByteDance (Seed)", "family": "Cola", "url": "https://www.anchorterminal.com/open-weights/#bytedance-seed", "others": [ { "name": "Cola-DLM", "url": "https://huggingface.co/ByteDance-Seed/Cola-DLM" } ] }, "area": "models", "provenance": { "legalEntity": "ByteDance, named in the GitHub repository's security policy. The legal entity wasn't looked up", "domain": "bytedance.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/ByteDance-Seed/Cola2/commits/main", "securityTxt": "unknown", "checked": "2026-11-10", "notes": [ "The files are in the ByteDance-Seed organisation on Hugging Face and the code is in the ByteDance-Seed organisation on GitHub; each links the other.", "No licence is stated for the weights, so there are no terms to read.", "The domain's registration date and security.txt weren't looked up. The GitHub repository's SECURITY.md points to ByteDance's security centre.", "The changelog is the commit history, six commits on 6 November 2026." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/cola2.json" }, "answer": "WeVisOne-VL-2B scores 61 (C) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Cola2 leads on documentation and upkeep.", "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": 50, "category": "interface", "leads": "wevisone-vl-2b", "name": "Agent interface", "reasons": { "a": "Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer. Tool-calling format 0, none. Trained tool calling 0, not trained for tool calling. Structured output 0, Nothing about JSON or schema-constrained output. Reasoning control 0, no reasoning mode. Context 0, no context length is stated. 20 + 0 + 0 + 0 + 0 + 0 = 20.", "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": 20, "b": 70 } }, { "by": 45, "category": "reach", "leads": "wevisone-vl-2b", "name": "Reach", "reasons": { "a": "Memory 15, the only build is the F32 safetensors set, 35.07 GB, and 35.07 GB × 1.2 = 42.1 GB, within 64 GB. Loaded as BF16, as the card's example does, it needs about half, but the line reads the file size. Runtimes 0, none of the six runs it in a released version. Builds 0, one set of weights and no quantised build. Terms 0, no licence for the weights. 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, and nothing names an API that serves it. 15 + 0 + 0 + 0 + 10 + 0 = 25.", "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": 25, "b": 70 } }, { "by": 15, "category": "risk", "leads": "wevisone-vl-2b", "name": "Risk", "reasons": { "a": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the ByteDance-Seed organisation, which the ByteDance-Seed GitHub repository for the model links to (10). Safety evaluation 0, no safety results. Agent guidance 5, General cautions in the GitHub model card: not instruction-tuned or safety-aligned, not for use as an assistant, for facts or advice, or for high-impact decisions about people. Licence risk 0, With no licence for the weights there is nothing to hold ByteDance to. Security programme 5, the GitHub repository's SECURITY.md sends reports to ByteDance's security centre and says how they're assessed (5). 15 + 15 + 0 + 5 + 0 + 5 = 40.", "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": 40, "b": 55 } }, { "by": 10, "category": "upkeep", "leads": "cola2", "name": "Documentation and upkeep", "reasons": { "a": "Model card 8, Intended use and limitations (the out-of-scope list, and the unconverged latent model) and the architecture are on the card and the GitHub model card it links. Training data 15, the GitHub model card names the source (OLMo 2 pretraining text, retokenised) and the mix by stage with token counts, 346.03B in all. Last change 20, All six commits are from 6 November 2026, four days before this check, the last one updating a checksum in the README. Responsiveness 0, no discussions on the repository, so nobody has asked anything yet and the line reads as none answered, as settled in calibration. Version policy 5, A first release. File health 10, Config, files and card agree: F32 weights, a float32 config and a card that says to load them as bfloat16, with SHA256SUMS to check the download. 8 + 15 + 20 + 0 + 5 + 10 = 58.", "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": 58, "b": 48 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "ByteDance (Seed)", "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": "None stated for the weights (the code is 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": "2026-11-06", "b": "2026-10-28", "name": "Last release" }, { "a": "no document linked", "b": "not read yet", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" } ], "faq": [ { "answer": "WeVisOne-VL-2B scores 61 (C) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Cola2 leads on documentation and upkeep.", "question": "Which is better for AI agents, Cola2 or WeVisOne-VL-2B?" } ], "goodFor": [ { "aheadOn": [ "Documentation and upkeep, 58 against 48" ], "also": null, "goodFor": "Researchers working on latent or diffusion language models who want a checkpoint to study", "slug": "cola2", "watchFor": "No licence stated for the weights" }, { "aheadOn": [ "Reach, 70 against 25", "Risk, 55 against 40", "Agent interface, 70 against 20" ], "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-cola2.json", "title": "Agents-A1-4B vs Cola2", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-cola2" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-wevisone-vl-2b.json", "title": "Agents-A1-4B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cola2.json", "title": "Apertus v1.5 8B vs Cola2", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cola2" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-wevisone-vl-2b.json", "title": "Apertus v1.5 8B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-cola2.json", "title": "Apodex-1.1-mini vs Cola2", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-cola2" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-wevisone-vl-2b.json", "title": "Apodex-1.1-mini vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-cola2.json", "title": "Arete-27B vs Cola2", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-cola2" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-2b.json", "title": "Arete-27B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-cola2.json", "title": "BigBang-v1 vs Cola2", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-cola2" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-wevisone-vl-2b.json", "title": "BigBang-v1 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-cola2.json", "title": "Carbon-X vs Cola2", "url": 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{ "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-wevisone-vl-2b.json", "title": "MiniMax-M3.1-Flash vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-wevisone-vl-2b.json", "title": "Mistral Large 4 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-wevisone-vl-2b" }, { "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": 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"https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-wevisone-vl-2b.json", "title": "North Mini Code 1.0 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-wevisone-vl-2b.json", "title": "occamy-1.0 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-wevisone-vl-2b.json", "title": "Olmo 3 7B Think vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-2b.json", "title": "Ornith-1.5-122B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-wevisone-vl-2b.json", "title": "Ornith-1.5-35B-A3B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-wevisone-vl-2b.json", "title": "Ornith-1.5-397B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-wevisone-vl-2b.json", "title": "Ornith-1.5-9B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-2b.json", "title": "OTel-2.0-LLM-31B-IT vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-2b.json", "title": "Qwen-AgentWorld-35B-A3B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-wevisone-vl-2b.json", "title": "Qwen3-30B-A3B-Instruct-2507 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-wevisone-vl-2b" }, { "json": "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": 45, "cola2": 25, "edge": "wevisone-vl-2b", "key": "reach", "name": "Reach", "weight": 25, "wevisone-vl-2b": 70 }, { "by": 15, "cola2": 40, "edge": "wevisone-vl-2b", "key": "risk", "name": "Risk", "weight": 20, "wevisone-vl-2b": 55 }, { "by": 50, "cola2": 20, "edge": "wevisone-vl-2b", "key": "interface", "name": "Agent interface", "weight": 10, "wevisone-vl-2b": 70 }, { "by": 10, "cola2": 58, "edge": "cola2", "key": "upkeep", "name": "Documentation and upkeep", "weight": 10, "wevisone-vl-2b": 48 } ], "summary": "WeVisOne-VL-2B scores 61 (C) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Cola2 leads on documentation and upkeep. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "cola2": "A research release, and an honest one: ByteDance Seed says it's a base model with an unconverged latent stage, scoring below Llama-3-8B on its own table. It's here for people studying continuous-latent generation. Agents can't use it as a model: 32-token completions, no chat or tools, the repository's own code to load it, and no licence on the weights.", "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/cola2-vs-wevisone-vl-2b", "json": "https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-2b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-2b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-2b.min.md" }, "markdown": "WeVisOne-VL-2B scores 61 (C) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Cola2 leads on documentation and upkeep. Both do open-weight models you run yourself.\n\n- Cola2: grade F, 33.9/100, rank #160 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/cola2.md · JSON https://www.anchorterminal.com/api/v1/tools/cola2.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### Cola2 (F)\n\nGood for: Researchers working on latent or diffusion language models who want a checkpoint to study\n\nAhead on:\n- Documentation and upkeep, 58 against 48\n\nWatch for: No licence stated for the weights\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 25\n- Risk, 55 against 40\n- Agent interface, 70 against 20\n\nWatch for: The pixel decoder ships as a pickle file\n\n\n## Why the edge\n\n### Agent interface: WeVisOne-VL-2B +50\n\n- Cola2 (20): Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer. Tool-calling format 0, none. Trained tool calling 0, not trained for tool calling. Structured output 0, Nothing about JSON or schema-constrained output. Reasoning control 0, no reasoning mode. Context 0, no context length is stated. 20 + 0 + 0 + 0 + 0 + 0 = 20.\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### Reach: WeVisOne-VL-2B +45\n\n- Cola2 (25): Memory 15, the only build is the F32 safetensors set, 35.07 GB, and 35.07 GB × 1.2 = 42.1 GB, within 64 GB. Loaded as BF16, as the card's example does, it needs about half, but the line reads the file size. Runtimes 0, none of the six runs it in a released version. Builds 0, one set of weights and no quantised build. Terms 0, no licence for the weights. 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, and nothing names an API that serves it. 15 + 0 + 0 + 0 + 10 + 0 = 25.\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: WeVisOne-VL-2B +15\n\n- Cola2 (40): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the ByteDance-Seed organisation, which the ByteDance-Seed GitHub repository for the model links to (10). Safety evaluation 0, no safety results. Agent guidance 5, General cautions in the GitHub model card: not instruction-tuned or safety-aligned, not for use as an assistant, for facts or advice, or for high-impact decisions about people. Licence risk 0, With no licence for the weights there is nothing to hold ByteDance to. Security programme 5, the GitHub repository's SECURITY.md sends reports to ByteDance's security centre and says how they're assessed (5). 15 + 15 + 0 + 5 + 0 + 5 = 40.\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### Documentation and upkeep: Cola2 +10\n\n- Cola2 (58): Model card 8, Intended use and limitations (the out-of-scope list, and the unconverged latent model) and the architecture are on the card and the GitHub model card it links. Training data 15, the GitHub model card names the source (OLMo 2 pretraining text, retokenised) and the mix by stage with token counts, 346.03B in all. Last change 20, All six commits are from 6 November 2026, four days before this check, the last one updating a checksum in the README. Responsiveness 0, no discussions on the repository, so nobody has asked anything yet and the line reads as none answered, as settled in calibration. Version policy 5, A first release. File health 10, Config, files and card agree: F32 weights, a float32 config and a card that says to load them as bfloat16, with SHA256SUMS to check the download. 8 + 15 + 20 + 0 + 5 + 10 = 58.\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## Score by category\n\n| Category | Weight | Cola2 | WeVisOne-VL-2B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 25 | 70 | WeVisOne-VL-2B +45 |\n| Risk | 20% (20 this run) | 40 | 55 | WeVisOne-VL-2B +15 |\n| Agent interface | 10% (10 this run) | 20 | 70 | WeVisOne-VL-2B +50 |\n| Documentation and upkeep | 10% (10 this run) | 58 | 48 | Cola2 +10 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **33.9 · F** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Cola2 | WeVisOne-VL-2B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | ByteDance (Seed) | 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 | None stated for the weights (the code is Apache 2.0) | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-06 | 2026-10-28 |\n| Terms last updated | no document linked | not read yet |\n| Privacy policy last updated | no document linked | no document linked |\n\n## Verdicts\n\n**Cola2.** A research release, and an honest one: ByteDance Seed says it's a base model with an unconverged latent stage, scoring below Llama-3-8B on its own table. It's here for people studying continuous-latent generation. Agents can't use it as a model: 32-token completions, no chat or tools, the repository's own code to load it, and no licence on the weights.\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### Cola2\n\n1. Don't plan to use this as your model. It completes short prompts and wasn't tuned to follow instructions\n2. Don't use it commercially on the strength of this listing. The weights have no stated licence\n3. If you study it, read modeling_cola2.py first: trust_remote_code runs it\n4. Pin Transformers below 5, as the card requires\n5. Pass a seeded torch.Generator to generate(). Output varies between runs even with greedy decoding\n6. Check the download with `sha256sum -c SHA256SUMS`\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, Cola2 or WeVisOne-VL-2B?\n\nWeVisOne-VL-2B scores 61 (C) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Cola2 leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-2b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"cola2\", \"b\": \"wevisone-vl-2b\"}`. 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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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