{ "data": { "a": { "slug": "laguna-m-1", "name": "Laguna M.1", "vendor": "Poolside", "vendorUrl": "https://poolside.ai", "kind": "weights", "category": "open-weights", "summary": "Laguna M.1 is Poolside's first-generation open-weight Mixture-of-Experts model for agentic coding, 225B total and 23B active parameters, with switchable reasoning and a 262,144-token context, published on Hugging Face under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/laguna-m-1", "markdownUrl": "https://www.anchorterminal.com/tools/laguna-m-1.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/laguna-m-1.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/laguna-m-1.json", "dns": "laguna-m-1.grade.anchorterminal.com", "repo": "https://huggingface.co/poolside/Laguna-M.1", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "poolside/Laguna-M.1" }, { "registry": "huggingface", "name": "poolside/Laguna-M.1-FP8" }, { "registry": "huggingface", "name": "poolside/Laguna-M.1-NVFP4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for the main repository and both builds, although the card's metadata carries an unused gate description pointing to Poolside's privacy policy (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 451.6 GB for the BF16 files and 139.3 GB for the NVFP4 build. The card links Poolside's API for hosted use, but no inference provider is mapped on the Hub and Poolside's API docs name only Laguna S 2.1 (checked 2026-11-04).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/poolside/Laguna-M.1", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "coding", "reasoning", "tool-calling", "apache-2.0", "safetensors", "fp8", "nvfp4", "self-hosted" ], "lastRelease": "2026-07-14", "graded": true, "anchor": { "graded": true, "score": 56.7, "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": 84, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 46, "interface": 85, "reach": 70, "risk": 55, "upkeep": 36 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-04" }, "negative": 0, "verdict": "A large open-weight coding and agent model under plain Apache 2.0, for teams with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine. Released llama.cpp, Ollama, vLLM, SGLang and Transformers code runs the architecture. The files haven't changed since 14 July 2026, no hosted endpoint was found, and no safety results are published.", "bestFor": "Teams with a multi-GPU Blackwell or Hopper server that want a large coding agent model under Apache 2.0.", "strengths": [ "Plain Apache 2.0 with no gate, account or use policy", "Released support in vLLM, SGLang, Transformers, llama.cpp and Ollama (through its pinned llama.cpp)", "Poolside's own FP8 and NVFP4 builds", "Tool calling and the poolside_v1 parser documented on the card, several calls a turn", "Thinking on or off per request, with the reasoning returned in reasoning_content" ], "weaknesses": [ "The smallest build needs about 167 GB, more than a 128 GB machine", "No Ollama library or LM Studio catalogue entry, and no publisher GGUF build", "No change to the files since 14 July 2026 and no reply from Poolside to its one discussion", "No safety results and only general cautions for agent use", "No hosting found. No inference provider is mapped on the Hub, and Poolside's API docs name only Laguna S 2.1" ], "agentNotes": [ "Run the NVFP4 build on Blackwell GPUs with `vllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on vLLM 0.21.0 or later", "Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{\"enable_thinking\": true}'`, which Poolside recommends for agentic coding", "Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped", "Sampling per the card is temperature 1.0 and top_k 20", "The FP8 build's Docker image and serve.sh predate upstream support and set a 4,096-token context, so prefer a released vLLM", "It doesn't fit our 128 GB reference machine at any published precision", "Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 56.7 } ], "editorialScores": { "capability": 46, "interface": 85, "reach": 70, "risk": 55, "upkeep": 36 }, "provenanceScore": 69, "checked": { "categories": { "capability": "2026-11-04", "interface": "2026-11-04", "reach": "2026-11-04", "risk": "2026-11-04", "upkeep": "2026-11-04" }, "last": "2026-11-04", "lastMoved": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install 'vllm\u003e=0.21.0'\nvllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 \\\n --reasoning-parser poolside_v1 --enable-auto-tool-choice --default-chat-template-kwargs '{\"enable_thinking\": true}'", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"poolside/Laguna-M.1-NVFP4\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/laguna-m-1" }, "sameCompany": [ "laguna-s-2-1", "laguna-xs-2-1" ], "weights": { "trainedBy": "Poolside", "params": "225.8B total, 23B active", "context": 262144, "goodFor": "Agentic coding on a multi-GPU server, with tool calling and reasoning that turns on or off per request, under Apache 2.0.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Laguna-M.1-NVFP4", "url": "https://huggingface.co/poolside/Laguna-M.1-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 139.3, "memoryGB": 192, "format": "safetensors", "note": "For NVIDIA Blackwell GPUs" }, { "name": "Laguna-M.1-FP8", "url": "https://huggingface.co/poolside/Laguna-M.1-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 238.6, "memoryGB": 512, "format": "safetensors", "note": "238.6 GB summed from the file list, of which 9.5 GB is a Docker image (image/laguna-vllm-v0.19.0-overlay.tar.gz) and 229.1 GB the weights. Either way 512 GB" }, { "name": "Laguna-M.1", "url": "https://huggingface.co/poolside/Laguna-M.1", "who": "publisher", "precision": "bf16", "sizeGB": 451.6, "memoryGB": 1024, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ] } ], "run": "vllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice --default-chat-template-kwargs '{\"enable_thinking\": true}'", "runBuild": "Laguna-M.1-NVFP4", "checked": "2026-11-04", "minMemoryGB": 192 }, "family": { "lab": "Poolside", "family": "Laguna 1 (M.1)", "url": "https://www.anchorterminal.com/open-weights/#poolside", "others": [ { "name": "Laguna-M.1-base", "url": "https://huggingface.co/poolside/Laguna-M.1-base", "params": "225.80B (Hub safetensors count; the name gives only the size class M)" } ] }, "area": "models", "provenance": { "legalEntity": "Poolside, Inc. (named in Poolside's Terms of Use; the LICENSE.md copyright line reads Poolside)", "domain": "poolside.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "https://poolside.ai/legal/eula", "privacy": "https://poolside.ai/legal/privacy", "statusPage": "", "changelog": "https://docs.poolside.ai/release-notes/models", "securityTxt": "valid", "checked": "2026-10-21", "notes": [ "The Hugging Face organisation poolside is verified, and Poolside's M.1 blog post links the repository.", "Nothing is hosted for this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "Poolside's docs keep dated model release notes, with an April 2026 entry for M.1.", "security.txt is PGP-signed, names a security contact at poolside.ai and expires on 1 April 2028. The domain's registration date wasn't checked." ], "score": 69 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/laguna-m-1.json", "live": { "slug": "laguna-m-1", "securityTxt": { "url": "https://poolside.ai/.well-known/security.txt", "state": "valid", "expires": "2028-04-01T12:00:00.000Z", "checkedAt": "2026-11-10T16:09:25.614312659Z" }, "domain": { "domain": "poolside.ai", "registered": "2020-07-07", "source": "https://rdap.identitydigital.services/rdap/domain/poolside.ai", "checkedAt": "2026-11-08T14:58:52.089559761Z" }, "pages": [ { "url": "https://docs.poolside.ai/release-notes/models", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:18:11.541481401Z", "changedAt": "2026-11-01T06:42:30.392417309Z", "fingerprint": "41c3a898c1a6" }, { "url": "https://poolside.ai/legal/privacy", "kind": "privacy", "status": 304, "checkedAt": "2026-11-10T16:28:14.659109859Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "95a56164038b" }, { "url": "https://poolside.ai/legal/eula", "kind": "terms", "status": 304, "checkedAt": "2026-11-10T16:28:12.585971651Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "6daf0ef509c0" } ], "updatedAt": "2026-11-10T16:28:14.659109859Z" } }, "answer": "WeVisOne-VL-2B scores 61 (C) on agent readiness against Laguna M.1's 56.7 (C), and leads in 1 of 4 scored categories. Laguna M.1 leads on agent interface.", "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": 15, "category": "interface", "leads": "laguna-m-1", "name": "Agent interface", "reasons": { "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and calls come back and names the poolside_v1 parser. Trained tool calling 20, several calls a turn. Structured output 0, not documented. Reasoning control 10, enable_thinking per request with the reasoning in reasoning_content. Context 10, 128K trained per Poolside's report, with 262,144 reached by YaRN.", "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": 85, "b": 70 } }, { "by": 12, "category": "upkeep", "leads": "wevisone-vl-2b", "name": "Documentation and upkeep", "reasons": { "a": "Model card 11 of 20, intended use, architecture, parameters and context are there, limitations, languages and a cut-off aren't. Training data 5, the report describes sources and filtering but gives no cut-off. Last change 10, 14 July 2026, 113 days ago. Responsiveness 0, the one discussion's only reply is from someone outside the poolside organisation. Version policy 5, a first open release with nothing replaced. File health 5, generation config and template disagree on the thinking default and the FP8 build's Docker files name different image tags.", "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": 36, "b": 48 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Poolside", "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": "Apache-2.0", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-07-14", "b": "2026-10-28", "name": "Last release" }, { "a": "2026-07-21", "b": "not read yet", "name": "Terms last updated" }, { "a": "2026-07-21", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "yes, with an opt-out", "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": "yes", "b": "not read yet", "name": "Terms or service can change without notice" }, { "a": "yes", "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 Laguna M.1's 56.7 (C), and leads in 1 of 4 scored categories. Laguna M.1 leads on agent interface.", "question": "Which is better for AI agents, Laguna M.1 or WeVisOne-VL-2B?" } ], "goodFor": [ { "aheadOn": [ "Agent interface, 85 against 70" ], "also": null, "goodFor": "Teams with a multi-GPU Blackwell or Hopper server that want a large coding agent model under Apache 2.0.", "slug": "laguna-m-1", "watchFor": "The smallest build needs about 167 GB, more than a 128 GB machine" }, { "aheadOn": [ "Documentation and upkeep, 48 against 36" ], "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": { "laguna-m-1": [ { "causes": [ "correction" ], "date": "2026-11-04", "delta": 1.2, "from": 55.5, "fromGrade": "C", "to": 56.7, "toGrade": "C" } ], "wevisone-vl-2b": null }, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-m-1.json", "title": "Agents-A1-4B vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-m-1" }, { "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-laguna-m-1.json", "title": "Apertus v1.5 8B vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-m-1" }, { "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-laguna-m-1.json", "title": "Apodex-1.1-mini vs Laguna M.1", "url": 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"json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-unlimited-ocr-2.json", "title": "Laguna M.1 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-wevisone-vl-4b.json", "title": "Laguna M.1 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-zaya1-8b.json", "title": "Laguna M.1 vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-zaya1-8b" }, { "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": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-wevisone-vl-2b.json", "title": "Ling-3.0-tiny vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-wevisone-vl-2b.json", "title": "Ling-3.1-flash-1019 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-2b.json", "title": "LongCat-2.5 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-wevisone-vl-2b.json", "title": "MiMo-V2.6 Distill Qwen 9B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-wevisone-vl-2b" }, { "json": "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": "MiniCPM5-2B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-wevisone-vl-2b.json", "title": "MiniMax-M2.5 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-2b.json", "title": "MiniMax-M2.7 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-2b" }, { "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": "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": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-wevisone-vl-2b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-wevisone-vl-2b.json", "title": "Nemotron 3 Nano 30B A3B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-wevisone-vl-2b.json", "title": "Nemotron 3 Nano 4B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-wevisone-vl-2b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-wevisone-vl-2b.json", "title": "Nemotron 3 Super 120B A12B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-wevisone-vl-2b.json", "title": "Nemotron 3 Ultra 550B A55B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-wevisone-vl-2b" }, { "json": "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": 0, "edge": "", "key": "reach", "laguna-m-1": 70, "name": "Reach", "weight": 25, "wevisone-vl-2b": 70 }, { "by": 0, "edge": "", "key": "risk", "laguna-m-1": 55, "name": "Risk", "weight": 20, "wevisone-vl-2b": 55 }, { "by": 15, "edge": "laguna-m-1", "key": "interface", "laguna-m-1": 85, "name": "Agent interface", "weight": 10, "wevisone-vl-2b": 70 }, { "by": 12, "edge": "wevisone-vl-2b", "key": "upkeep", "laguna-m-1": 36, "name": "Documentation and upkeep", "weight": 10, "wevisone-vl-2b": 48 } ], "summary": "WeVisOne-VL-2B scores 61 (C) on agent readiness against Laguna M.1's 56.7 (C), and leads in 1 of 4 scored categories. Laguna M.1 leads on agent interface. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "laguna-m-1": "A large open-weight coding and agent model under plain Apache 2.0, for teams with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine. Released llama.cpp, Ollama, vLLM, SGLang and Transformers code runs the architecture. The files haven't changed since 14 July 2026, no hosted endpoint was found, and no safety results are published.", "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/laguna-m-1-vs-wevisone-vl-2b", "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-wevisone-vl-2b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/laguna-m-1-vs-wevisone-vl-2b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/laguna-m-1-vs-wevisone-vl-2b.min.md" }, "markdown": "WeVisOne-VL-2B scores 61 (C) on agent readiness against Laguna M.1's 56.7 (C), and leads in 1 of 4 scored categories. Laguna M.1 leads on agent interface. Both do open-weight models you run yourself.\n\n- Laguna M.1: grade C, 56.7/100, rank #112 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-m-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-m-1.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### Laguna M.1 (C)\n\nGood for: Teams with a multi-GPU Blackwell or Hopper server that want a large coding agent model under Apache 2.0.\n\nAhead on:\n- Agent interface, 85 against 70\n\nWatch for: The smallest build needs about 167 GB, more than a 128 GB machine\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- Documentation and upkeep, 48 against 36\n\nWatch for: The pixel decoder ships as a pickle file\n\n\n## Why the edge\n\n### Agent interface: Laguna M.1 +15\n\n- Laguna M.1 (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and calls come back and names the poolside_v1 parser. Trained tool calling 20, several calls a turn. Structured output 0, not documented. Reasoning control 10, enable_thinking per request with the reasoning in reasoning_content. Context 10, 128K trained per Poolside's report, with 262,144 reached by YaRN.\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### Documentation and upkeep: WeVisOne-VL-2B +12\n\n- Laguna M.1 (36): Model card 11 of 20, intended use, architecture, parameters and context are there, limitations, languages and a cut-off aren't. Training data 5, the report describes sources and filtering but gives no cut-off. Last change 10, 14 July 2026, 113 days ago. Responsiveness 0, the one discussion's only reply is from someone outside the poolside organisation. Version policy 5, a first open release with nothing replaced. File health 5, generation config and template disagree on the thinking default and the FP8 build's Docker files name different image tags.\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 | Laguna M.1 | WeVisOne-VL-2B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 70 | 70 | even |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 85 | 70 | Laguna M.1 +15 |\n| Documentation and upkeep | 10% (10 this run) | 36 | 48 | WeVisOne-VL-2B +12 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **56.7 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Laguna M.1 | WeVisOne-VL-2B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Poolside | 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 | Apache-2.0 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-14 | 2026-10-28 |\n| Terms last updated | 2026-07-21 | not read yet |\n| Privacy policy last updated | 2026-07-21 | no document linked |\n| Customer content may train models | yes, with an opt-out | 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 | yes | not read yet |\n| Arbitration or class-action waiver | yes | not read yet |\n| Agent reviews | 2.5/5 (4) | none |\n\n## How the scores have moved\n\n### Laguna M.1\n\n- +1.2 on 4 November 2026: correction\n\n### WeVisOne-VL-2B\n\nNo score change since it was listed.\n\n## Verdicts\n\n**Laguna M.1.** A large open-weight coding and agent model under plain Apache 2.0, for teams with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine. Released llama.cpp, Ollama, vLLM, SGLang and Transformers code runs the architecture. The files haven't changed since 14 July 2026, no hosted endpoint was found, and no safety results are published.\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### Laguna M.1\n\n1. Run the NVFP4 build on Blackwell GPUs with `vllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on vLLM 0.21.0 or later\n2. Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{\"enable_thinking\": true}'`, which Poolside recommends for agentic coding\n3. Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped\n4. Sampling per the card is temperature 1.0 and top_k 20\n5. The FP8 build's Docker image and serve.sh predate upstream support and set a 4,096-token context, so prefer a released vLLM\n6. It doesn't fit our 128 GB reference machine at any published precision\n7. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented\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, Laguna M.1 or WeVisOne-VL-2B?\n\nWeVisOne-VL-2B scores 61 (C) on agent readiness against Laguna M.1's 56.7 (C), and leads in 1 of 4 scored categories. Laguna M.1 leads on agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/laguna-m-1-vs-wevisone-vl-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/laguna-m-1-vs-wevisone-vl-2b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"laguna-m-1\", \"b\": \"wevisone-vl-2b\"}`. From a terminal: `anchor compare laguna-m-1 wevisone-vl-2b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/laguna-m-1.json and https://www.anchorterminal.com/api/v1/tools/wevisone-vl-2b.json\n\n## Other comparisons with Laguna M.1 or WeVisOne-VL-2B\n\n- [Agents-A1-4B vs Laguna M.1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-m-1.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 Laguna M.1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-m-1.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 Laguna M.1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-m-1.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- [Arete-27B vs Laguna 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Qwen3.5-2B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-2b.md)\n- [Laguna M.1 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-397b-a17b.md)\n- [Laguna M.1 vs Qwen3.5-4B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-4b.md)\n- [Laguna M.1 vs Qwen3.5-9B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-9b.md)\n- [Laguna M.1 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-6-27b.md)\n- [Laguna M.1 vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-6-35b-a3b.md)\n- [Laguna M.1 vs Qwen3.8-27B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-8-27b.md)\n- [Laguna M.1 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-8-flash-next.md)\n- [Laguna M.1 vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Laguna M.1 vs 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WeVisOne-VL-2B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-wevisone-vl-2b.md)\n- [LFM3-Omni-2B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-wevisone-vl-2b.md)\n- [LFM3-VL-2B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-2b.md)\n- [Ling-3.0-tiny vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-wevisone-vl-2b.md)\n- [Ling-3.1-flash-1019 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-wevisone-vl-2b.md)\n- [LongCat-2.5 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-2b.md)\n- [MiMo-V2.6 Distill Qwen 9B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-wevisone-vl-2b.md)\n- [MiMo-V2.6-Flash-RL vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-2b.md)\n- [MiniCPM-o 4.5 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-wevisone-vl-2b.md)\n- [MiniCPM-V 4.7 1B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-2b.md)\n- [MiniCPM-V 4.7 35B-A3B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-wevisone-vl-2b.md)\n- [MiniCPM5-2B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minicpm5-2b-vs-wevisone-vl-2b.md)\n- [MiniMax-M2.5 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-wevisone-vl-2b.md)\n- [MiniMax-M2.7 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-2b.md)\n- [MiniMax-M3.1-Flash vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-wevisone-vl-2b.md)\n- [Mistral Large 4 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/mistral-large-4-vs-wevisone-vl-2b.md)\n- [Muse Glimmer 30B vs 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WeVisOne-VL-2B](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-wevisone-vl-2b.md)\n- [Ornith-1.5-9B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-wevisone-vl-2b.md)\n- [OTel-2.0-LLM-31B-IT vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-2b.md)\n- [Qwen-AgentWorld-35B-A3B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-2b.md)\n- [Qwen3-30B-A3B-Instruct-2507 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-wevisone-vl-2b.md)\n- [Qwen3-4B-Instruct-2507 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-wevisone-vl-2b.md)\n- [Qwen3.5-0.8B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-wevisone-vl-2b.md)\n- [Qwen3.5-122B-A10B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-wevisone-vl-2b.md)\n- [Qwen3.5-2B vs 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