{ "data": { "a": { "slug": "nanbeige-4-3-33b", "name": "Nanbeige4.3-33B", "vendor": "Nanbeige LLM Lab (BOSS Zhipin)", "vendorUrl": "https://huggingface.co/Nanbeige", "kind": "weights", "category": "open-weights", "summary": "Nanbeige4.3-33B is the Nanbeige LLM Lab's open-weight dense model for agentic coding and office work, 33B parameters on the Llama architecture with a 512K-token context, thinking that switches per request and tool calls in an XML format.", "url": "https://www.anchorterminal.com/tools/nanbeige-4-3-33b", "markdownUrl": "https://www.anchorterminal.com/tools/nanbeige-4-3-33b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nanbeige-4-3-33b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nanbeige-4-3-33b.json", "dns": "nanbeige-4-3-33b.grade.anchorterminal.com", "repo": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B", "license": "Apache-2.0 (Hub metadata only; the repository has no LICENSE file)", "transports": [], "packages": [ { "registry": "huggingface", "name": "Nanbeige/Nanbeige4.3-33B" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for Nanbeige/Nanbeige4.3-33B and Nanbeige/Nanbeige4.3-33B-Base (checked 2026-10-25).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 as the card's metadata names it. Neither repository holds a LICENSE file. Running it costs the hardware, 66.26 GB of BF16 files and no quantised build from the publisher. No hosted API or inference provider serves it (checked 2026-10-25).", "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 2026-10-25).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-03" }, "docsUrl": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "llama", "reasoning", "tool-calling", "coding", "agentic", "long-context", "safetensors", "self-hosted", "new" ], "lastRelease": "2026-10-31", "graded": true, "anchor": { "graded": true, "score": 60.8, "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": 68, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 42, "interface": 85, "reach": 80, "risk": 60, "upkeep": 56 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-03" }, "negative": 0, "verdict": "A dense 33B Llama-architecture cousin for long agentic coding and office tasks, with per-request thinking, XML tool calls that stock vLLM and SGLang parse, and 512K of context. Nanbeige's INT4 build needs about 27 GB. Artificial Analysis's first independent runs give it 20.4 on its Intelligence Index, and there are no safety results, training data notes or licence file.", "bestFor": "Agents with a 32 GB GPU (INT4) or more, or the 128 GB reference machine at BF16, that want an Apache-tagged 33B model for long coding and tool loops.", "strengths": [ "A plain LlamaForCausalLM with no repository code, so released vLLM, SGLang, Transformers and llama.cpp load it", "Apache 2.0 by its metadata, ungated, safetensors only", "524,288 tokens of context set in config.json with no rope scaling", "Thinking switches per request, and preserve_thinking keeps earlier reasoning for agent loops", "Tool calls in an XML format the qwen3_coder parser reads, several per turn", "Nanbeige publishes INT4, INT8 and FP8 builds, and a member answered nine of the first 30 discussions" ], "weaknesses": [ "Independent results so far are Artificial Analysis's alone (Intelligence Index v5.0 20.4, Terminal-Bench 4.0 29.8 per cent); the card's other agentic numbers are Nanbeige's own runs", "No Ollama or LM Studio build, and Nanbeige's smallest build (INT4) still needs about 27 GB", "No LICENSE file in either repository; Apache 2.0 rests on the metadata tag", "No safety evaluation, agent-use guidance, training data description or knowledge cut-off", "No hosted API or inference provider serves it" ], "agentNotes": [ "Serve with `vllm serve Nanbeige/Nanbeige4.3-33B --enable-auto-tool-choice --reasoning-parser qwen3 --tool-call-parser qwen3_coder`, as the card says, or SGLang with the same two parsers", "On a 32 GB GPU use Nanbeige/Nanbeige4.3-33B-INT4 with the same vLLM or SGLang flags; the INT8 and FP8 builds need about 43 GB", "Keep `tool_call_format` at its default `xml`, since qwen3_coder parses that format; the `json` option writes Hermes-style JSON calls, which need a different parser such as vLLM's hermes", "Pass `chat_template_kwargs` `{\"preserve_thinking\": true}` for multi-turn tool use and coding, and `{\"enable_thinking\": false}` to skip reasoning, as the card recommends", "Use temperature 1.0, top_p 0.95 and up to 65,536 new tokens for agent work, and temperature 0.6 for reasoning and chat, per the card", "Send your own system prompt, since without one the template injects a Chinese identity line, or a Chinese tool-expert prompt when tools are passed", "Send one system message at the start only, as the template raises an error for a later one", "In plain Transformers pass `use_cache=True` to generate, since config.json and generation_config.json set it false; trust_remote_code isn't needed" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 60.8 } ], "editorialScores": { "capability": 42, "interface": 85, "reach": 80, "risk": 60, "upkeep": 56 }, "provenanceScore": 58, "checked": { "categories": { "capability": "2026-11-03", "interface": "2026-11-03", "reach": "2026-11-03", "risk": "2026-11-03", "upkeep": "2026-11-03" }, "last": "2026-11-03", "lastMoved": "2026-11-03", "oldest": "2026-11-03", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# vLLM 0.32.0 or SGLang 0.5.22, as the card's commands (the card uses four GPUs with tensor parallelism)\nhf download Nanbeige/Nanbeige4.3-33B --local-dir Nanbeige4.3-33B\nvllm serve ./Nanbeige4.3-33B --served-model-name nanbeige --tensor-parallel-size 4 \\\n --enable-auto-tool-choice --reasoning-parser qwen3 --tool-call-parser qwen3_coder\n# or\npython -m sglang.launch_server --model-path ./Nanbeige4.3-33B --tp-size 4 \\\n --reasoning-parser qwen3 --tool-call-parser qwen3_coder\n# or, with llama.cpp and bartowski's GGUF, a third party's conversion:\nllama-server -hf bartowski/Nanbeige_Nanbeige4.3-33B-GGUF:Q4_K_M --jinja", "http": "curl http://localhost:8000/v1/chat/completions -H 'Content-Type: application/json' \\\n -d '{\"model\": \"nanbeige\", \"messages\": [{\"role\": \"user\", \"content\": \"Which number is bigger, 9.11 or 9.8?\"}],\n \"temperature\": 0.6, \"top_p\": 0.95, \"chat_template_kwargs\": {\"enable_thinking\": true}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/nanbeige-4-3-33b" }, "sameCompany": [ "nanbeige-4-2-3b" ], "weights": { "trainedBy": "Nanbeige LLM Lab (BOSS Zhipin)", "builtOn": "Nanbeige/Nanbeige4.3-33B-Base (fine-tune, declared in the card's metadata; Apache 2.0 by its tag, no LICENSE file)", "params": "33B", "context": 524288, "goodFor": "A cousin for long agentic coding and office tasks on a 32 GB GPU at INT4 or about 96 GB at BF16, with a 512K context and per-request thinking.", "runtimes": [ "vLLM", "SGLang", "Transformers", "llama.cpp" ], "builds": [ { "name": "Nanbeige4.3-33B-INT4", "url": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B-INT4", "who": "publisher", "precision": "int4", "sizeGB": 22.57, "memoryGB": 32, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ], "note": "Compressed-tensors 4-bit weights in groups of 32, whole repository 22.57 GB. 22.57 GB × 1.2 = 27.1 GB" }, { "name": "bartowski Nanbeige_Nanbeige4.3-33B-GGUF (Q4_K_M)", "url": "https://huggingface.co/bartowski/Nanbeige_Nanbeige4.3-33B-GGUF", "who": "bartowski", "precision": "q4_k_m", "sizeGB": 20.79, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not Nanbeige's. 20.79 GB of Q4_K_M weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it" }, { "name": "Nanbeige4.3-33B-FP8", "url": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 35.54, "memoryGB": 48, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ], "note": "Compressed-tensors FP8 with dynamic activation scales. An INT8 build (36.01 GB) is in Nanbeige/Nanbeige4.3-33B-INT8" }, { "name": "bartowski Nanbeige_Nanbeige4.3-33B-GGUF (Q8_0)", "url": "https://huggingface.co/bartowski/Nanbeige_Nanbeige4.3-33B-GGUF", "who": "bartowski", "precision": "q8_0", "sizeGB": 35.2, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not Nanbeige's. 35.20 GB of Q8_0 weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it" }, { "name": "Nanbeige4.3-33B (BF16)", "url": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B", "who": "publisher", "precision": "bf16", "sizeGB": 66.26, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ], "note": "Whole repository, 66.24 GB of weights in 14 shards. 66.26 GB × 1.2 = 79.5 GB. Nanbeige also publishes INT4, INT8 and FP8 builds; no Ollama, LM Studio or ggml-org build exists, and llama.cpp needs a GGUF converted with its own script or taken from someone else" } ], "run": "vllm serve Nanbeige/Nanbeige4.3-33B-INT4 --enable-auto-tool-choice --reasoning-parser qwen3 --tool-call-parser qwen3_coder", "runBuild": "Nanbeige4.3-33B-INT4", "checked": "2026-11-03", "minMemoryGB": 32 }, "family": { "lab": "Nanbeige LLM Lab (BOSS Zhipin)", "family": "Nanbeige4.3", "url": "https://www.anchorterminal.com/open-weights/#nanbeige", "others": [ { "name": "Nanbeige4.3-33B-Base", "url": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B-Base", "params": "33B" } ], "olderCount": 7 }, "area": "models", "provenance": { "legalEntity": "BOSS Zhipin (Kanzhun Limited), unconfirmed", "domain": "zhipin.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B/commits/main", "securityTxt": "none", "checked": "2026-10-25", "notes": [ "The card names the Nanbeige Team and nothing else. BOSS Zhipin is identified as the maker by the lab's Nanbeige4.2-3B report on arXiv (Nanbeige LLM Lab, Boss Zhipin), which links huggingface.co/Nanbeige, and by the chat template's default system prompt naming BOSS Zhipin as developer and trainer. The card's contact address is on kanzhun.com. Kanzhun Limited as BOSS Zhipin's operator wasn't confirmed first-hand, since ir.zhipin.com refused our reader. The domain's registration date wasn't checked.", "The Hugging Face organisation Nanbeige (Nanbeige LLM Lab) isn't verified by the Hub. The arXiv report's link to it is the publisher's own documentation pointing there.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history.", "www.zhipin.com/.well-known/security.txt answers 404 and kanzhun.com's redirects to the home page. BOSS Zhipin runs a security response centre at security.zhipin.com for reporting flaws in its products; nothing links it to the model." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nanbeige-4-3-33b.json", "live": { "slug": "nanbeige-4-3-33b", "securityTxt": { "url": "https://zhipin.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:09:28.506819752Z" }, "domain": { "domain": "zhipin.com", "registered": "2003-11-11", "source": "https://rdap.verisign.com/com/v1/domain/zhipin.com", "checkedAt": "2026-11-08T15:03:54.452286646Z" }, "pages": [ { "url": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:22:18.096335669Z", "changedAt": "2026-11-10T16:22:18.096335669Z", "fingerprint": "245289df7021" } ], "updatedAt": "2026-11-10T16:22:18.096335669Z" } }, "answer": "WeVisOne-VL-4B and Nanbeige4.3-33B score within a point of each other on agent readiness, 61 (C) and 60.8 (C). Nanbeige4.3-33B leads on reach, risk, agent interface and documentation and upkeep.", "b": { "slug": "wevisone-vl-4b", "name": "WeVisOne-VL-4B", "vendor": "Tencent (WeChat Vision)", "vendorUrl": "https://www.tencent.com", "kind": "weights", "category": "open-weights", "summary": "WeVisOne-VL-4B is an open-weight perception model of 4.8B 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-4b", "markdownUrl": "https://www.anchorterminal.com/tools/wevisone-vl-4b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/wevisone-vl-4b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/wevisone-vl-4b.json", "dns": "wevisone-vl-4b.grade.anchorterminal.com", "repo": "https://huggingface.co/tencent/WeVisOne-VL-4B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "tencent/WeVisOne-VL-4B" } ], "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, 9.66 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": "2026-10-27", "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": 66, "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", "9.66 GB in BF16, so it fits a 12 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-4B\", 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-4B\", 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-4B" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/wevisone-vl-4b" }, "sameCompany": [ "hy-memory", "tencentdb-agent-memory", "hunyuanocr", "hy-memory-30b-a3b", "hy3", "wevisone-vl-2b" ], "weights": { "trainedBy": "Tencent WeChat Vision", "params": "4.8B", "goodFor": "Finding, counting, reading and cutting out what you name in an image, on a 12 GB machine.", "runtimes": [ "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "WeVisOne-VL-4B (BF16 safetensors)", "url": "https://huggingface.co/tencent/WeVisOne-VL-4B/blob/main/model.safetensors", "who": "publisher", "precision": "bf16", "sizeGB": 9.66, "memoryGB": 12, "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-4B", "runBuild": "WeVisOne-VL-4B (BF16 safetensors)", "checked": "2026-11-10", "minMemoryGB": 12 }, "family": { "lab": "Tencent (Hunyuan / Hy)", "family": "WeVisOne-VL", "url": "https://www.anchorterminal.com/open-weights/#tencent", "others": [ { "name": "WeVisOne-VL-2B", "url": "https://huggingface.co/tencent/WeVisOne-VL-2B", "params": "2.4B (2,441,744,384 by the Hub's safetensors count)", "listing": "https://www.anchorterminal.com/tools/wevisone-vl-2b" } ], "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-4B/blob/main/LICENSE", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/tencent/WeVisOne-VL-4B/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-4b.json" }, "edges": [ { "by": 15, "category": "interface", "leads": "nanbeige-4-3-33b", "name": "Agent interface", "reasons": { "a": "The chat template earns 20, Jinja in tokenizer_config.json. The tool-calling format earns 25, with the XML call format in the template and the qwen3_coder parser named for vLLM and SGLang. Trained tool calling earns 20, several calls per turn in the template. Structured output earns 0, as nothing mentions it. Reasoning control earns 10, thinking switched per request with the qwen3 reasoning parser named. Context earns 10 for 512K set in the config with no scaling.", "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": 10, "category": "reach", "leads": "nanbeige-4-3-33b", "name": "Reach", "reasons": { "a": "Memory earns 20, since Nanbeige's INT4 build is 22.57 GB, about 27 GB with a fifth added, which fits 32 GB. Runtimes earn 20 from released vLLM, SGLang, Transformers and llama.cpp, which all run the Llama architecture, and Ollama and LM Studio have no build. Builds earn 15 for Nanbeige's INT4, INT8 and FP8 builds of 31 October 2026. Terms earn 15 for Apache 2.0 with nothing added, though only the metadata names it. Download earns 10, with no gate. Hosted earns 0, as no provider or Nanbeige API serves it.", "b": "Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 9.7 GB, and 9.7 GB × 1.2 = 11.6 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": 80, "b": 70 } }, { "by": 8, "category": "upkeep", "leads": "nanbeige-4-3-33b", "name": "Documentation and upkeep", "reasons": { "a": "The model card earns 11, with intended use, parameters, context and languages but no limitations, architecture or cut-off. Training data earns 0, as nothing describes it. Last change earns 20 for the quantised builds of 31 October 2026. Responsiveness earns 10, a Nanbeige member answering nine of 30 discussions. Version policy earns 5 for a first release, with a config change made and reverted under bare commit titles. File health earns 10, the files and the card agreeing.", "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 27 October 2026, added links to the paper and project page, 14 days before this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (23 and 26 October 2026) has a 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, 14 days old. 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": 56, "b": 48 } }, { "by": 5, "category": "risk", "leads": "nanbeige-4-3-33b", "name": "Risk", "reasons": { "a": "File safety earns 25, safetensors only and no repository code, so the card's trust_remote_code has nothing to run. Provenance earns 15, the lab's own organisation as its arXiv report links it, with a hash for every file. Safety evaluation earns 0 and agent guidance 0, since neither card says anything on either. Licence risk keeps all 15 under Apache 2.0. The security programme earns 5 for BOSS Zhipin's security response centre, which isn't linked to the model.", "b": "File safety 15, the backbone is safetensors, but mask_decoder.pt, a PyTorch pickle the card calls a required component, sits beside it. Provenance 15, the files sit in the tencent organisation, which Tencent's GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card or in the README. Agent guidance 5, General cautions: the card says the release is for research, isn't for autonomous high-impact decisions or identifying individuals, and lists where masks go wrong. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide, as the Hy3 listing records (5). 15 + 15 + 0 + 5 + 15 + 5 = 55." }, "scores": { "a": 60, "b": 55 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Nanbeige LLM Lab (BOSS Zhipin)", "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 (Hub metadata only; the repository has no LICENSE file)", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-31", "b": "2026-10-27", "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-4B and Nanbeige4.3-33B score within a point of each other on agent readiness, 61 (C) and 60.8 (C). Nanbeige4.3-33B leads on reach, risk, agent interface and documentation and upkeep.", "question": "Which is better for AI agents, Nanbeige4.3-33B or WeVisOne-VL-4B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 80 against 70", "Risk, 60 against 55", "Agent interface, 85 against 70", "Documentation and upkeep, 56 against 48" ], "also": null, "goodFor": "Agents with a 32 GB GPU (INT4) or more, or the 128 GB reference machine at BF16, that want an Apache-tagged 33B model for long coding and tool loops.", "slug": "nanbeige-4-3-33b", "watchFor": "Independent results so far are Artificial Analysis's alone (Intelligence Index v5.0 20.4, Terminal-Bench 4.0 29.8 per cent); the card's other agentic numbers are Nanbeige's own runs" }, { "aheadOn": null, "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-4b", "watchFor": "The pixel decoder ships as a pickle file" } ], "history": { "nanbeige-4-3-33b": [ { "causes": [ "product", "evidence" ], "date": "2026-11-03", "delta": -0.2, "from": 61, "fromGrade": "C", "to": 60.8, "toGrade": "C" } ], "wevisone-vl-4b": null }, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nanbeige-4-3-33b.json", "title": "Agents-A1-4B vs Nanbeige4.3-33B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nanbeige-4-3-33b" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-wevisone-vl-4b.json", "title": "Agents-A1-4B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nanbeige-4-3-33b.json", "title": "Apertus v1.5 8B vs Nanbeige4.3-33B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nanbeige-4-3-33b" }, { "json": 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"https://www.anchorterminal.com/compare/qwen3-5-2b-vs-wevisone-vl-4b.json", "title": "Qwen3.5-2B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-wevisone-vl-4b.json", "title": "Qwen3.5-397B-A17B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-wevisone-vl-4b.json", "title": "Qwen3.5-4B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-4b.json", "title": "Qwen3.5-9B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-wevisone-vl-4b.json", "title": "Qwen3.6-27B vs WeVisOne-VL-4B", "url": 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"json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-wevisone-vl-4b.json", "title": "Qwen3-Coder-Next vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-wevisone-vl-4b.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-wevisone-vl-4b.json", "title": "Qwen3-VL-2B-Instruct vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-wevisone-vl-4b.json", "title": "Qwen3-VL-30B-A3B-Instruct vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-wevisone-vl-4b.json", "title": "Qwen3-VL-32B-Instruct vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-4b.json", "title": "Qwen3-VL-4B-Instruct vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-wevisone-vl-4b.json", "title": "Qwen3-VL-8B-Instruct vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-wevisone-vl-4b.json", "title": "Qwopus3.8-27B-Flash-V2 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-wevisone-vl-4b.json", "title": "Recursive Flint vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-wevisone-vl-4b.json", "title": "Ripple-65B Preview vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-4b.json", "title": "SenseNova-Loop-9B-Preview vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-4b.json", "title": "SmolLM3-3B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-4b.json", "title": "Spark-X2.6-4B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-4b.json", "title": "Step 3.7 Flash vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-wevisone-vl-4b.json", "title": "Swift 1.5 Qwen3.8-27B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-wevisone-vl-4b.json", "title": "Swift 1.5 Qwen3.8-Flash-Next vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-wevisone-vl-4b.json", "title": "ThinkingCap (Qwen3.8 27B) vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-wevisone-vl-4b" }, { "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-4b-vs-zaya1-8b.json", "title": "WeVisOne-VL-4B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/wevisone-vl-4b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-4b.json", "title": "Baidu Unlimited-OCR-2 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-4b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 10, "edge": "nanbeige-4-3-33b", "key": "reach", "name": "Reach", "nanbeige-4-3-33b": 80, "weight": 25, "wevisone-vl-4b": 70 }, { "by": 5, "edge": "nanbeige-4-3-33b", "key": "risk", "name": "Risk", "nanbeige-4-3-33b": 60, "weight": 20, "wevisone-vl-4b": 55 }, { "by": 15, "edge": "nanbeige-4-3-33b", "key": "interface", "name": "Agent interface", "nanbeige-4-3-33b": 85, "weight": 10, "wevisone-vl-4b": 70 }, { "by": 8, "edge": "nanbeige-4-3-33b", "key": "upkeep", "name": "Documentation and upkeep", "nanbeige-4-3-33b": 56, "weight": 10, "wevisone-vl-4b": 48 } ], "summary": "WeVisOne-VL-4B and Nanbeige4.3-33B score within a point of each other on agent readiness, 61 (C) and 60.8 (C). Nanbeige4.3-33B leads on reach, risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "nanbeige-4-3-33b": "A dense 33B Llama-architecture cousin for long agentic coding and office tasks, with per-request thinking, XML tool calls that stock vLLM and SGLang parse, and 512K of context. Nanbeige's INT4 build needs about 27 GB. Artificial Analysis's first independent runs give it 20.4 on its Intelligence Index, and there are no safety results, training data notes or licence file.", "wevisone-vl-4b": "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/nanbeige-4-3-33b-vs-wevisone-vl-4b", "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-wevisone-vl-4b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-wevisone-vl-4b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-wevisone-vl-4b.min.md" }, "markdown": "WeVisOne-VL-4B and Nanbeige4.3-33B score within a point of each other on agent readiness, 61 (C) and 60.8 (C). Nanbeige4.3-33B leads on reach, risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Nanbeige4.3-33B: grade C, 60.8/100, rank #86 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nanbeige-4-3-33b.md · JSON https://www.anchorterminal.com/api/v1/tools/nanbeige-4-3-33b.json\n- WeVisOne-VL-4B: grade C, 61/100, rank #84 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/wevisone-vl-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/wevisone-vl-4b.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### Nanbeige4.3-33B (C)\n\nGood for: Agents with a 32 GB GPU (INT4) or more, or the 128 GB reference machine at BF16, that want an Apache-tagged 33B model for long coding and tool loops.\n\nAhead on:\n- Reach, 80 against 70\n- Risk, 60 against 55\n- Agent interface, 85 against 70\n- Documentation and upkeep, 56 against 48\n\nWatch for: Independent results so far are Artificial Analysis's alone (Intelligence Index v5.0 20.4, Terminal-Bench 4.0 29.8 per cent); the card's other agentic numbers are Nanbeige's own runs\n\n### WeVisOne-VL-4B (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\nWatch for: The pixel decoder ships as a pickle file\n\n\n## Why the edge\n\n### Agent interface: Nanbeige4.3-33B +15\n\n- Nanbeige4.3-33B (85): The chat template earns 20, Jinja in tokenizer_config.json. The tool-calling format earns 25, with the XML call format in the template and the qwen3_coder parser named for vLLM and SGLang. Trained tool calling earns 20, several calls per turn in the template. Structured output earns 0, as nothing mentions it. Reasoning control earns 10, thinking switched per request with the qwen3 reasoning parser named. Context earns 10 for 512K set in the config with no scaling.\n- WeVisOne-VL-4B (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: Nanbeige4.3-33B +10\n\n- Nanbeige4.3-33B (80): Memory earns 20, since Nanbeige's INT4 build is 22.57 GB, about 27 GB with a fifth added, which fits 32 GB. Runtimes earn 20 from released vLLM, SGLang, Transformers and llama.cpp, which all run the Llama architecture, and Ollama and LM Studio have no build. Builds earn 15 for Nanbeige's INT4, INT8 and FP8 builds of 31 October 2026. Terms earn 15 for Apache 2.0 with nothing added, though only the metadata names it. Download earns 10, with no gate. Hosted earns 0, as no provider or Nanbeige API serves it.\n- WeVisOne-VL-4B (70): Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 9.7 GB, and 9.7 GB × 1.2 = 11.6 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### Documentation and upkeep: Nanbeige4.3-33B +8\n\n- Nanbeige4.3-33B (56): The model card earns 11, with intended use, parameters, context and languages but no limitations, architecture or cut-off. Training data earns 0, as nothing describes it. Last change earns 20 for the quantised builds of 31 October 2026. Responsiveness earns 10, a Nanbeige member answering nine of 30 discussions. Version policy earns 5 for a first release, with a config change made and reverted under bare commit titles. File health earns 10, the files and the card agreeing.\n- WeVisOne-VL-4B (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 27 October 2026, added links to the paper and project page, 14 days before this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (23 and 26 October 2026) has a 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, 14 days old. 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### Risk: Nanbeige4.3-33B +5\n\n- Nanbeige4.3-33B (60): File safety earns 25, safetensors only and no repository code, so the card's trust_remote_code has nothing to run. Provenance earns 15, the lab's own organisation as its arXiv report links it, with a hash for every file. Safety evaluation earns 0 and agent guidance 0, since neither card says anything on either. Licence risk keeps all 15 under Apache 2.0. The security programme earns 5 for BOSS Zhipin's security response centre, which isn't linked to the model.\n- WeVisOne-VL-4B (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## Score by category\n\n| Category | Weight | Nanbeige4.3-33B | WeVisOne-VL-4B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 80 | 70 | Nanbeige4.3-33B +10 |\n| Risk | 20% (20 this run) | 60 | 55 | Nanbeige4.3-33B +5 |\n| Agent interface | 10% (10 this run) | 85 | 70 | Nanbeige4.3-33B +15 |\n| Documentation and upkeep | 10% (10 this run) | 56 | 48 | Nanbeige4.3-33B +8 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **60.8 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Nanbeige4.3-33B | WeVisOne-VL-4B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Nanbeige LLM Lab (BOSS Zhipin) | 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 (Hub metadata only; the repository has no LICENSE file) | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-31 | 2026-10-27 |\n| Terms last updated | no document linked | not read yet |\n| Privacy policy last updated | no document linked | no document linked |\n\n## How the scores have moved\n\n### Nanbeige4.3-33B\n\n- -0.2 on 3 November 2026: product, new evidence\n\n### WeVisOne-VL-4B\n\nNo score change since it was listed.\n\n## Verdicts\n\n**Nanbeige4.3-33B.** A dense 33B Llama-architecture cousin for long agentic coding and office tasks, with per-request thinking, XML tool calls that stock vLLM and SGLang parse, and 512K of context. Nanbeige's INT4 build needs about 27 GB. Artificial Analysis's first independent runs give it 20.4 on its Intelligence Index, and there are no safety results, training data notes or licence file.\n\n**WeVisOne-VL-4B.** 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### Nanbeige4.3-33B\n\n1. Serve with `vllm serve Nanbeige/Nanbeige4.3-33B --enable-auto-tool-choice --reasoning-parser qwen3 --tool-call-parser qwen3_coder`, as the card says, or SGLang with the same two parsers\n2. On a 32 GB GPU use Nanbeige/Nanbeige4.3-33B-INT4 with the same vLLM or SGLang flags; the INT8 and FP8 builds need about 43 GB\n3. Keep `tool_call_format` at its default `xml`, since qwen3_coder parses that format; the `json` option writes Hermes-style JSON calls, which need a different parser such as vLLM's hermes\n4. Pass `chat_template_kwargs` `{\"preserve_thinking\": true}` for multi-turn tool use and coding, and `{\"enable_thinking\": false}` to skip reasoning, as the card recommends\n5. Use temperature 1.0, top_p 0.95 and up to 65,536 new tokens for agent work, and temperature 0.6 for reasoning and chat, per the card\n6. Send your own system prompt, since without one the template injects a Chinese identity line, or a Chinese tool-expert prompt when tools are passed\n7. Send one system message at the start only, as the template raises an error for a later one\n8. In plain Transformers pass `use_cache=True` to generate, since config.json and generation_config.json set it false; trust_remote_code isn't needed\n\n### WeVisOne-VL-4B\n\n1. Use Tencent's package: `WeVisOneVL(\"tencent/WeVisOne-VL-4B\", 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, Nanbeige4.3-33B or WeVisOne-VL-4B?\n\nWeVisOne-VL-4B and Nanbeige4.3-33B score within a point of each other on agent readiness, 61 (C) and 60.8 (C). Nanbeige4.3-33B leads on reach, risk, agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-wevisone-vl-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-wevisone-vl-4b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"nanbeige-4-3-33b\", \"b\": \"wevisone-vl-4b\"}`. From a terminal: `anchor compare nanbeige-4-3-33b wevisone-vl-4b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nanbeige-4-3-33b.json and https://www.anchorterminal.com/api/v1/tools/wevisone-vl-4b.json\n\n## Other comparisons with Nanbeige4.3-33B or WeVisOne-VL-4B\n\n- [Agents-A1-4B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nanbeige-4-3-33b.md)\n- [Agents-A1-4B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-wevisone-vl-4b.md)\n- [Apertus v1.5 8B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nanbeige-4-3-33b.md)\n- [Apertus v1.5 8B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-wevisone-vl-4b.md)\n- [Apodex-1.1-mini vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nanbeige-4-3-33b.md)\n- [Apodex-1.1-mini vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-wevisone-vl-4b.md)\n- [Arete-27B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/arete-27b-vs-nanbeige-4-3-33b.md)\n- [Arete-27B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-4b.md)\n- [BigBang-v1 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/bigbang-v1-vs-nanbeige-4-3-33b.md)\n- [BigBang-v1 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/bigbang-v1-vs-wevisone-vl-4b.md)\n- [Carbon-X vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/carbon-x-vs-nanbeige-4-3-33b.md)\n- [Carbon-X vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/carbon-x-vs-wevisone-vl-4b.md)\n- [Cola2 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/cola2-vs-nanbeige-4-3-33b.md)\n- [Cola2 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-4b.md)\n- [Cosmos-Reason2-2B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nanbeige-4-3-33b.md)\n- [Cosmos-Reason2-2B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-wevisone-vl-4b.md)\n- [Darwin-36B-Opus vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nanbeige-4-3-33b.md)\n- [Darwin-36B-Opus vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-wevisone-vl-4b.md)\n- [DeepSeek-V3.2 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nanbeige-4-3-33b.md)\n- [DeepSeek-V3.2 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-wevisone-vl-4b.md)\n- [DeepSeek V4.1 Flash vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nanbeige-4-3-33b.md)\n- [DeepSeek V4.1 Flash vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-wevisone-vl-4b.md)\n- [DeepSeek-V4-Flash-0731 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nanbeige-4-3-33b.md)\n- [DeepSeek-V4-Flash-0731 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-wevisone-vl-4b.md)\n- [Devstral Small 2 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/devstral-small-2-vs-nanbeige-4-3-33b.md)\n- [Devstral Small 2 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/devstral-small-2-vs-wevisone-vl-4b.md)\n- [DiffusionGemma 26B A4B IT vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nanbeige-4-3-33b.md)\n- [DiffusionGemma 26B A4B IT vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-wevisone-vl-4b.md)\n- [EXAONE 4.5 33B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nanbeige-4-3-33b.md)\n- [EXAONE 4.5 33B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-wevisone-vl-4b.md)\n- [Gemma 4 12B IT vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nanbeige-4-3-33b.md)\n- [Gemma 4 12B IT vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-wevisone-vl-4b.md)\n- [Gemma 4 26B A4B IT vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nanbeige-4-3-33b.md)\n- [Gemma 4 26B A4B IT vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-wevisone-vl-4b.md)\n- [Gemma 4 31B IT vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nanbeige-4-3-33b.md)\n- [Gemma 4 31B IT vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-wevisone-vl-4b.md)\n- [Gemma 4 E2B IT vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nanbeige-4-3-33b.md)\n- [Gemma 4 E2B IT vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-wevisone-vl-4b.md)\n- [Gemma 4 E4B IT vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nanbeige-4-3-33b.md)\n- [Gemma 4 E4B IT vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-wevisone-vl-4b.md)\n- [GLM-4.5-Air vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-nanbeige-4-3-33b.md)\n- [GLM-4.5-Air vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-wevisone-vl-4b.md)\n- [GLM-4.7-Flash vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nanbeige-4-3-33b.md)\n- [GLM-4.7-Flash vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-wevisone-vl-4b.md)\n- [GLM-5.3-Flash vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nanbeige-4-3-33b.md)\n- [GLM-5.3-Flash vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-wevisone-vl-4b.md)\n- [GLM-5.3 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/glm-5-3-vs-nanbeige-4-3-33b.md)\n- [GLM-5.3 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/glm-5-3-vs-wevisone-vl-4b.md)\n- [gpt-oss-120b vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nanbeige-4-3-33b.md)\n- [gpt-oss-120b vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-wevisone-vl-4b.md)\n- [gpt-oss-20b vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nanbeige-4-3-33b.md)\n- [gpt-oss-20b vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-wevisone-vl-4b.md)\n- [Granite 4.2 30B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-nanbeige-4-3-33b.md)\n- [Granite 4.2 30B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-wevisone-vl-4b.md)\n- [Granite 4.2 3B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nanbeige-4-3-33b.md)\n- [Granite 4.2 3B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-wevisone-vl-4b.md)\n- [Granite 4.2 8B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nanbeige-4-3-33b.md)\n- [Granite 4.2 8B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-wevisone-vl-4b.md)\n- [Hy-Memory-30B-A3B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nanbeige-4-3-33b.md)\n- [Hy-Memory-30B-A3B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-4b.md)\n- [Hy3 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/hy3-vs-nanbeige-4-3-33b.md)\n- [Hy3 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/hy3-vs-wevisone-vl-4b.md)\n- [Inkling-Small vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/inkling-small-vs-nanbeige-4-3-33b.md)\n- [Inkling-Small vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/inkling-small-vs-wevisone-vl-4b.md)\n- [Inkling vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/inkling-vs-nanbeige-4-3-33b.md)\n- [Inkling vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/inkling-vs-wevisone-vl-4b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nanbeige-4-3-33b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-wevisone-vl-4b.md)\n- [Kimi K3.1 Pro vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nanbeige-4-3-33b.md)\n- [Kimi K3.1 Pro vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-4b.md)\n- [Kimi K3 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/kimi-k3-vs-nanbeige-4-3-33b.md)\n- [Kimi K3 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/kimi-k3-vs-wevisone-vl-4b.md)\n- [Kolibri-1 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/kolibri-1-vs-nanbeige-4-3-33b.md)\n- [Kolibri-1 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/kolibri-1-vs-wevisone-vl-4b.md)\n- [Kronos vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/kronos-vs-nanbeige-4-3-33b.md)\n- [Kronos vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/kronos-vs-wevisone-vl-4b.md)\n- [Laguna M.1 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/laguna-m-1-vs-nanbeige-4-3-33b.md)\n- [Laguna M.1 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/laguna-m-1-vs-wevisone-vl-4b.md)\n- [Laguna S 2.1 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nanbeige-4-3-33b.md)\n- [Laguna S 2.1 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-wevisone-vl-4b.md)\n- [Laguna XS 2.1 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nanbeige-4-3-33b.md)\n- [Laguna XS 2.1 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-wevisone-vl-4b.md)\n- [LBS-27B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/lbs-27b-vs-nanbeige-4-3-33b.md)\n- [LBS-27B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/lbs-27b-vs-wevisone-vl-4b.md)\n- [LFM2.5-2.6B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nanbeige-4-3-33b.md)\n- [LFM2.5-2.6B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-wevisone-vl-4b.md)\n- [LFM3-1.7B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nanbeige-4-3-33b.md)\n- [LFM3-1.7B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-wevisone-vl-4b.md)\n- [LFM3-Audio-2B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nanbeige-4-3-33b.md)\n- [LFM3-Audio-2B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-wevisone-vl-4b.md)\n- [LFM3-Omni-2B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nanbeige-4-3-33b.md)\n- [LFM3-Omni-2B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-wevisone-vl-4b.md)\n- [LFM3-VL-2B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nanbeige-4-3-33b.md)\n- [LFM3-VL-2B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-4b.md)\n- [Ling-3.0-tiny vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-nanbeige-4-3-33b.md)\n- [Ling-3.0-tiny vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-wevisone-vl-4b.md)\n- [Ling-3.1-flash-1019 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-nanbeige-4-3-33b.md)\n- [Ling-3.1-flash-1019 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-wevisone-vl-4b.md)\n- [LongCat-2.5 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/longcat-2-5-vs-nanbeige-4-3-33b.md)\n- [LongCat-2.5 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-4b.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-nanbeige-4-3-33b.md)\n- [MiMo-V2.6 Distill Qwen 9B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-wevisone-vl-4b.md)\n- [MiMo-V2.6-Flash-RL vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nanbeige-4-3-33b.md)\n- [MiMo-V2.6-Flash-RL vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-4b.md)\n- [MiniCPM-o 4.5 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nanbeige-4-3-33b.md)\n- [MiniCPM-o 4.5 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-wevisone-vl-4b.md)\n- [MiniCPM-V 4.7 1B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-nanbeige-4-3-33b.md)\n- [MiniCPM-V 4.7 1B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-4b.md)\n- [MiniCPM-V 4.7 35B-A3B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-nanbeige-4-3-33b.md)\n- [MiniCPM-V 4.7 35B-A3B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-wevisone-vl-4b.md)\n- [MiniCPM5-2B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/minicpm5-2b-vs-nanbeige-4-3-33b.md)\n- [MiniCPM5-2B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/minicpm5-2b-vs-wevisone-vl-4b.md)\n- [MiniMax-M2.5 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-nanbeige-4-3-33b.md)\n- [MiniMax-M2.5 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-wevisone-vl-4b.md)\n- [MiniMax-M2.7 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-nanbeige-4-3-33b.md)\n- [MiniMax-M2.7 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-4b.md)\n- [MiniMax-M3.1-Flash vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nanbeige-4-3-33b.md)\n- [MiniMax-M3.1-Flash vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-wevisone-vl-4b.md)\n- [Mistral Large 4 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/mistral-large-4-vs-nanbeige-4-3-33b.md)\n- [Mistral Large 4 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/mistral-large-4-vs-wevisone-vl-4b.md)\n- [Muse Glimmer 30B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nanbeige-4-3-33b.md)\n- [Muse Glimmer 30B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-wevisone-vl-4b.md)\n- [Nanbeige4.2-3B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nanbeige-4-3-33b.md)\n- [Nanbeige4.2-3B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-4b.md)\n- [Nanbeige4.3-33B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Nanbeige4.3-33B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Nanbeige4.3-33B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-nano-30b-a3b.md)\n- [Nanbeige4.3-33B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-nano-4b.md)\n- [Nanbeige4.3-33B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Nanbeige4.3-33B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-super-120b-a12b.md)\n- [Nanbeige4.3-33B vs Nemotron 3 Ultra 550B 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WeVisOne-VL-4B](https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-4b.md)\n- [Swift 1.5 Qwen3.8-27B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-wevisone-vl-4b.md)\n- [Swift 1.5 Qwen3.8-Flash-Next vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-wevisone-vl-4b.md)\n- [ThinkingCap (Qwen3.8 27B) vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-wevisone-vl-4b.md)\n- [WeVisOne-VL-2B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-wevisone-vl-4b.md)\n- [WeVisOne-VL-4B vs ZAYA1-8B](https://www.anchorterminal.com/compare/wevisone-vl-4b-vs-zaya1-8b.md)\n- [Baidu Unlimited-OCR-2 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-4b.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": "https://www.anchorterminal.com/compare/" }, { "name": "Nanbeige4.3-33B vs WeVisOne-VL-4B", "url": "" } ], "description": "WeVisOne-VL-4B and Nanbeige4.3-33B score within a point of each other for open-weight models you run yourself, 61 and 60.8 out of 100. 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