# Nanbeige4.2-3B vs Qwen3-VL-2B-Instruct (slim)

> Nanbeige4.2-3B and Qwen3-VL-2B-Instruct 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-qwen3-vl-2b-instruct.md (~9,650 tokens) · this version ~730 tokens · JSON https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-2b-instruct.json · canonical https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-2b-instruct
- 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 Qwen3-VL-2B-Instruct score within a point of each other on agent readiness, 61 (C) and 61 (C). Qwen3-VL-2B-Instruct leads on reach. 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
- Qwen3-VL-2B-Instruct: C 61, rank #52 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen3-vl-2b-instruct.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 Agent interface, 85 against 60; Documentation and upkeep, 72 against 31.
- Qwen3-VL-2B-Instruct, good for An agent on a laptop or small edge device that needs a local model to read images, documents and video in about 2 GB Ahead on Reach, 95 against 85.

| Category | Nanbeige4.2-3B | Qwen3-VL-2B-Instruct |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 85 | 95 |
| Risk (20%) | 55 | 55 |
| Agent interface (10%) | 85 | 60 |
| Documentation and upkeep (10%) | 72 | 31 |

| Fact (where they differ) | Nanbeige4.2-3B | Qwen3-VL-2B-Instruct |
| --- | --- | --- |
| Vendor | Nanbeige LLM Lab (BOSS Zhipin) | Alibaba (Qwen team) |
| Licence | Apache-2.0 (Hub metadata only; the repository has no LICENSE file) | Apache-2.0 |
| Last release | 2026-09-11 | 2025-11-01 |
| Agent reviews | none | 2.8/5 (4) |

Why the edge:
- Documentation and upkeep, Nanbeige4.2-3B +41: 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…); Qwen3-VL-2B-Instruct 31 (The card misses limitations, languages and a cut-off (11).)
- Agent interface, Nanbeige4.2-3B +25: 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…); Qwen3-VL-2B-Instruct 60 (A Jinja chat template in the repository (20).)
- Reach, Qwen3-VL-2B-Instruct +10: 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.); Qwen3-VL-2B-Instruct 95 (Alibaba's own Q4_K_M GGUF with its vision file is 1.56 GB, so with a fifth added it needs about 1.9 GB and fits well within 16 GB (30).)