# Nanbeige4.2-3B vs WeVisOne-VL-4B (slim)

> Nanbeige4.2-3B and WeVisOne-VL-4B score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-4b.md (~9,350 tokens) · this version ~680 tokens · JSON https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-4b.json · canonical https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-4b
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-11-10
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Nanbeige4.2-3B and WeVisOne-VL-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). Both do open-weight models you run yourself.

- Nanbeige4.2-3B: C 61, rank #78 of 160 open-weight models · https://www.anchorterminal.com/tools/nanbeige-4-2-3b.min.md
- WeVisOne-VL-4B: C 61, rank #84 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-4b.min.md
- Nanbeige4.2-3B, good for Local agents that need tool calling and a long context on a laptop or a small GPU Ahead on Reach, 85 against 70; Agent interface, 85 against 70; Documentation and upkeep, 72 against 48.
- WeVisOne-VL-4B, good for Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence

| Category | Nanbeige4.2-3B | WeVisOne-VL-4B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 85 | 70 |
| Risk (20%) | 55 | 55 |
| Agent interface (10%) | 85 | 70 |
| Documentation and upkeep (10%) | 72 | 48 |

| Fact (where they differ) | Nanbeige4.2-3B | WeVisOne-VL-4B |
| --- | --- | --- |
| Vendor | Nanbeige LLM Lab (BOSS Zhipin) | Tencent (WeChat Vision) |
| Licence | Apache-2.0 (Hub metadata only; the repository has no LICENSE file) | Apache-2.0 |
| Last release | 2026-09-11 | 2026-10-27 |
| Terms last updated | no document linked | not read yet |

Why the edge:
- Documentation and upkeep, Nanbeige4.2-3B +24: Nanbeige4.2-3B 72 (Model card 17, Intended use, limitations, the architecture, the parameters (3B outside the embeddings), the context and the languages (in the metadata) are on t…); WeVisOne-VL-4B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)
- Reach, Nanbeige4.2-3B +15: Nanbeige4.2-3B 85 (Memory 30, the smallest build from Nanbeige is the FP8 repository (the 8-bit GPTQ one is the same size), 5.19 GB, and 5.19 GB × 1.2 = 6.2 GB, within 16 GB.); 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.)
- Agent interface, Nanbeige4.2-3B +15: Nanbeige4.2-3B 85 (Chat template 20, A Jinja chat template in tokenizer_config.json (7,961 characters), with enable_thinking, preserve_thinking, tools and tool_call_format switche…); WeVisOne-VL-4B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)