# Nanbeige4.3-33B vs WeVisOne-VL-2B (slim)

> WeVisOne-VL-2B 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. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-wevisone-vl-2b.md (~9,650 tokens) · this version ~780 tokens · JSON https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-wevisone-vl-2b.json · canonical https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-wevisone-vl-2b
- 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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WeVisOne-VL-2B 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.

- Nanbeige4.3-33B: C 60.8, rank #86 of 160 open-weight models · https://www.anchorterminal.com/tools/nanbeige-4-3-33b.min.md
- WeVisOne-VL-2B: C 61, rank #83 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-2b.min.md
- Nanbeige4.3-33B, good 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. Ahead on Reach, 80 against 70; Risk, 60 against 55; Agent interface, 85 against 70; Documentation and upkeep, 56 against 48.
- WeVisOne-VL-2B, good for Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence

| Category | Nanbeige4.3-33B | WeVisOne-VL-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 80 | 70 |
| Risk (20%) | 60 | 55 |
| Agent interface (10%) | 85 | 70 |
| Documentation and upkeep (10%) | 56 | 48 |

| Fact (where they differ) | Nanbeige4.3-33B | WeVisOne-VL-2B |
| --- | --- | --- |
| 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 | 2026-10-31 | 2026-10-28 |
| Terms last updated | no document linked | not read yet |

Why the edge:
- Agent interface, Nanbeige4.3-33B +15: Nanbeige4.3-33B 85 (The chat template earns 20, Jinja in tokenizer_config.json.); WeVisOne-VL-2B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)
- Reach, Nanbeige4.3-33B +10: 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.); WeVisOne-VL-2B 70 (Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 4.92 GB, and 4.92 GB × 1.2 = 5.9 GB, within 16 GB.)
- Documentation and upkeep, Nanbeige4.3-33B +8: Nanbeige4.3-33B 56 (The model card earns 11, with intended use, parameters, context and languages but no limitations, architecture or cut-off.); WeVisOne-VL-2B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)
- Risk, Nanbeige4.3-33B +5: 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.); WeVisOne-VL-2B 55 (File safety 15, the backbone is safetensors, but mask_decoder.pt, a PyTorch pickle the card calls a required component, sits beside it.)