# LBS-27B vs Ling-3.1-flash-1019 (slim)

> LBS-27B scores 55.8 (C) to Ling-3.1-flash-1019's 54.8 (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-ling-3-1-flash-1019.md (~9,400 tokens) · this version ~730 tokens · JSON https://www.anchorterminal.com/compare/lbs-27b-vs-ling-3-1-flash-1019.json · canonical https://www.anchorterminal.com/compare/lbs-27b-vs-ling-3-1-flash-1019
- 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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LBS-27B scores 55.8 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 2 of 4 scored categories. Ling-3.1-flash-1019 leads on documentation and upkeep. 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
- Ling-3.1-flash-1019: C 54.8, rank #126 of 160 open-weight models · https://www.anchorterminal.com/tools/ling-3-1-flash-1019.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. Ahead on Risk, 50 against 45; Agent interface, 70 against 60.
- Ling-3.1-flash-1019, good for Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding Ahead on Documentation and upkeep, 56 against 43.

| Category | LBS-27B | Ling-3.1-flash-1019 |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 60 | 60 |
| Risk (20%) | 50 | 45 |
| Agent interface (10%) | 70 | 60 |
| Documentation and upkeep (10%) | 43 | 56 |

| Fact (where they differ) | LBS-27B | Ling-3.1-flash-1019 |
| --- | --- | --- |
| 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) | MIT |
| Last release | 2026-10-26 | 2026-11-04 |

Why the edge:
- Documentation and upkeep, Ling-3.1-flash-1019 +13: LBS-27B 43 (The model card earns 8, with intended use, one limitation and parameters, and no architecture, context, languages or cut-off.); Ling-3.1-flash-1019 56 (Model card 11, Intended use (complex reasoning, multi-step tasks and agentic workflows), architecture, parameters (560B total, 25B active) and context (1M claim…)
- Agent interface, LBS-27B +10: LBS-27B 70 (The chat template earns 20, Qwen's Jinja file in the repository.); Ling-3.1-flash-1019 60 (Chat template 20, chat_template.jinja in the repository (6,042 characters), which handles tools, the thinking switch and preserved reasoning.)
- Risk, LBS-27B +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.); Ling-3.1-flash-1019 45 (File safety 15, safetensors only, no .bin, .pt or .pth files (25), less 10 because the card's documented vLLM command passes --trust-remote-code, which runs the…)