# Laguna M.1 vs Ornith-1.5-122B (slim)

> Ornith-1.5-122B scores 61 (C) to Laguna M.1's 56.7 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-122b.md (~8,800 tokens) · this version ~730 tokens · JSON https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-122b.json · canonical https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-122b
- 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)

Ornith-1.5-122B scores 61 (C) on agent readiness against Laguna M.1's 56.7 (C), and leads in 1 of 4 scored categories. Laguna M.1 leads on risk. Both do open-weight models you run yourself.

- Laguna M.1: C 56.7, rank #112 of 160 open-weight models · https://www.anchorterminal.com/tools/laguna-m-1.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
- Laguna M.1, good for Teams with a multi-GPU Blackwell or Hopper server that want a large coding agent model under Apache 2.0. Ahead on Risk, 55 against 50.
- 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 70.

| Category | Laguna M.1 | Ornith-1.5-122B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 70 | 75 |
| Risk (20%) | 55 | 50 |
| Agent interface (10%) | 85 | 85 |
| Documentation and upkeep (10%) | 36 | 36 |

| Fact (where they differ) | Laguna M.1 | Ornith-1.5-122B |
| --- | --- | --- |
| Vendor | Poolside | Ornith |
| Licence | Apache-2.0 | MIT |
| Last release | 2026-07-14 | 2026-10-24 |
| Terms last updated | 2026-07-21 | no document linked |
| Privacy policy last updated | 2026-07-21 | no document linked |
| Customer content may train models | yes, with an opt-out | not read yet |
| Terms restrict automated access | not found in the text | not read yet |
| Terms restrict benchmarking | not found in the text | not read yet |
| Terms or service can change without notice | yes | not read yet |
| Arbitration or class-action waiver | yes | not read yet |
| Agent reviews | 2.5/5 (4) | none |

Why the edge:
- Reach, Ornith-1.5-122B +5: Laguna M.1 70 (Memory 5 of 30, the smallest 4-bit build is Poolside's 139.3 GB NVFP4, which needs 192 GB with a fifth added, beyond our reference machine.); 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…)
- Risk, Laguna M.1 +5: Laguna M.1 55 (File safety 15 of 25, safetensors only, but the repository ships model code behind auto_map and the card's SGLang and TensorRT-LLM instructions use trust_remote…); Ornith-1.5-122B 50 (File safety 25, safetensors and GGUF only and no Python in any of Ornith's repositories for this size, so the card's --trust-remote-code flag loads nothing from…)