# LBS-27B vs Nanbeige4.2-3B (slim)

> Nanbeige4.2-3B scores 61 (C) to LBS-27B's 55.8 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/lbs-27b-vs-nanbeige-4-2-3b.md (~9,400 tokens) · this version ~830 tokens · JSON https://www.anchorterminal.com/compare/lbs-27b-vs-nanbeige-4-2-3b.json · canonical https://www.anchorterminal.com/compare/lbs-27b-vs-nanbeige-4-2-3b
- 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
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

Nanbeige4.2-3B scores 61 (C) on agent readiness against LBS-27B's 55.8 (C), and leads in every scored category. Both do open-weight models you run yourself.

- LBS-27B: C 55.8, rank #118 of 160 open-weight models · https://www.anchorterminal.com/tools/lbs-27b.min.md
- Nanbeige4.2-3B: C 61, rank #78 of 160 open-weight models · https://www.anchorterminal.com/tools/nanbeige-4-2-3b.min.md
- LBS-27B, good for Agents with about 96 GB of GPU memory that need to find click targets on dense professional screenshots, or read fine detail in large images, and can run a small crop loop and wait for independent results.
- 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 60; Risk, 55 against 50; Agent interface, 85 against 70; Documentation and upkeep, 72 against 43.

| Category | LBS-27B | Nanbeige4.2-3B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 60 | 85 |
| Risk (20%) | 50 | 55 |
| Agent interface (10%) | 70 | 85 |
| Documentation and upkeep (10%) | 43 | 72 |

| Fact (where they differ) | LBS-27B | Nanbeige4.2-3B |
| --- | --- | --- |
| Vendor | inclusionAI (Ant Group) | Nanbeige LLM Lab (BOSS Zhipin) |
| Licence | Apache-2.0 (the card's metadata and licence section; the LICENSE file sits in the GitHub repository inclusionAI/UI-Venus on its LBS branch, not in the Hugging Face repository) | Apache-2.0 (Hub metadata only; the repository has no LICENSE file) |
| Last release | 2026-10-26 | 2026-09-11 |

Why the edge:
- Documentation and upkeep, Nanbeige4.2-3B +29: LBS-27B 43 (The model card earns 8, with intended use, one limitation and parameters, and no architecture, context, languages or cut-off.); 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…)
- Reach, Nanbeige4.2-3B +25: LBS-27B 60 (Memory earns 10, since the only build is 54.73 GB of BF16, about 66 GB with a fifth added, which fits the reference machine but not 64 GB.); 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.)
- Agent interface, Nanbeige4.2-3B +15: LBS-27B 70 (The chat template earns 20, Qwen's Jinja file in the repository.); 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…)
- Risk, Nanbeige4.2-3B +5: LBS-27B 50 (File safety earns 15, safetensors only but with the documented route running the publisher's scripts to serve it and to write its system prompt.); Nanbeige4.2-3B 55 (File safety 15, safetensors only, no .bin, .pt or .pth files (25).)