# LBS-27B vs Ornith-1.5-122B (slim)

> Ornith-1.5-122B 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.

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- 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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Ornith-1.5-122B scores 61 (C) on agent readiness against LBS-27B's 55.8 (C), and leads in 2 of 4 scored categories. LBS-27B 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
- Ornith-1.5-122B: C 61, rank #81 of 160 open-weight models · https://www.anchorterminal.com/tools/ornith-1-5-122b.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 Documentation and upkeep, 43 against 36.
- Ornith-1.5-122B, good for Local coding and terminal agents on a 128 GB unified-memory machine or a 96 GB GPU setup, where a clear licence file and an answering publisher aren't required Ahead on Reach, 75 against 60; Agent interface, 85 against 70.

| Category | LBS-27B | Ornith-1.5-122B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 60 | 75 |
| Risk (20%) | 50 | 50 |
| Agent interface (10%) | 70 | 85 |
| Documentation and upkeep (10%) | 43 | 36 |

| Fact (where they differ) | LBS-27B | Ornith-1.5-122B |
| --- | --- | --- |
| Vendor | inclusionAI (Ant Group) | Ornith |
| 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 | 2026-10-24 |

Why the edge:
- Reach, Ornith-1.5-122B +15: 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.); Ornith-1.5-122B 75 (Memory 10, since Ornith's own MLX 4-bit build is 69.7 GB and needs about 83.6 GB with the fifth added, over 64 GB and inside the reference machine's 107.4 GB, a…)
- Agent interface, Ornith-1.5-122B +15: LBS-27B 70 (The chat template earns 20, Qwen's Jinja file in the repository.); Ornith-1.5-122B 85 (Chat template 20, a Jinja file in the repository.)
- Documentation and upkeep, LBS-27B +7: LBS-27B 43 (The model card earns 8, with intended use, one limitation and parameters, and no architecture, context, languages or cut-off.); Ornith-1.5-122B 36 (Model card 11, it covers intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off.)