# LBS-27B vs LFM3-VL-2B (slim)

> LFM3-VL-2B 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-lfm3-vl-2b.md (~9,200 tokens) · this version ~830 tokens · JSON https://www.anchorterminal.com/compare/lbs-27b-vs-lfm3-vl-2b.json · canonical https://www.anchorterminal.com/compare/lbs-27b-vs-lfm3-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
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

LFM3-VL-2B scores 61 (C) on agent readiness against LBS-27B's 55.8 (C), and leads in 3 of 4 scored categories. LBS-27B leads on risk. 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
- LFM3-VL-2B: C 61, rank #72 of 160 open-weight models · https://www.anchorterminal.com/tools/lfm3-vl-2b.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 40.
- LFM3-VL-2B, good for Agents on a laptop, phone or robot that need to read screens, documents, images and video from a model under 2 GB at 4 bits Ahead on Reach, 85 against 60; Agent interface, 90 against 70; Documentation and upkeep, 77 against 43.

| Category | LBS-27B | LFM3-VL-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 60 | 85 |
| Risk (20%) | 50 | 40 |
| Agent interface (10%) | 70 | 90 |
| Documentation and upkeep (10%) | 43 | 77 |

| Fact (where they differ) | LBS-27B | LFM3-VL-2B |
| --- | --- | --- |
| Vendor | inclusionAI (Ant Group) | Liquid AI |
| 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) | LFM Open License v1.0 (free commercial use below $10M annual revenue) |
| llms.txt | no | yes |
| Last release | 2026-10-26 | 2026-11-09 |

Why the edge:
- Documentation and upkeep, LFM3-VL-2B +34: LBS-27B 43 (The model card earns 8, with intended use, one limitation and parameters, and no architecture, context, languages or cut-off.); LFM3-VL-2B 77 (Model card 17, Intended use (questions about video, documents, charts and app screens, finding objects, spatial reasoning), a limitation (not recommended for co…)
- Reach, LFM3-VL-2B +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.); LFM3-VL-2B 85 (Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.20 GB projector file, and 1.30 GB × 1.2 = 1.6 GB, which fits 8 GB, well inside the 16 GB line.)
- Agent interface, LFM3-VL-2B +20: LBS-27B 70 (The chat template earns 20, Qwen's Jinja file in the repository.); LFM3-VL-2B 90 (Chat template 20, chat_template.jinja in the repository (12,129 characters), with roles, images and video, tools, deferred tools, tool results and the thinking…)
- Risk, LBS-27B +10: 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.); LFM3-VL-2B 40 (File safety 15, safetensors only, no .bin, .pt or .pth files (25).)