# LFM3-1.7B vs Ornith-1.5-122B (slim)

> LFM3-1.7B and Ornith-1.5-122B score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ornith-1-5-122b.md (~9,600 tokens) · this version ~780 tokens · JSON https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ornith-1-5-122b.json · canonical https://www.anchorterminal.com/compare/lfm3-1-7b-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)

LFM3-1.7B and Ornith-1.5-122B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-122B leads on risk. Both do open-weight models you run yourself.

- LFM3-1.7B: C 61, rank #69 of 160 open-weight models · https://www.anchorterminal.com/tools/lfm3-1-7b.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
- LFM3-1.7B, good for Agents on a laptop, phone or small server that need tool calling and a long context from a model under 2 GB at 4 bits Ahead on Reach, 85 against 75; Agent interface, 100 against 85; Documentation and upkeep, 67 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 Risk, 50 against 40.

| Category | LFM3-1.7B | Ornith-1.5-122B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 85 | 75 |
| Risk (20%) | 40 | 50 |
| Agent interface (10%) | 100 | 85 |
| Documentation and upkeep (10%) | 67 | 36 |

| Fact (where they differ) | LFM3-1.7B | Ornith-1.5-122B |
| --- | --- | --- |
| Vendor | Liquid AI | Ornith |
| Licence | LFM Open License v1.0 (free commercial use below $10M annual revenue) | MIT |
| llms.txt | yes | no |
| Last release | 2026-11-09 | 2026-10-24 |

Why the edge:
- Documentation and upkeep, LFM3-1.7B +31: LFM3-1.7B 67 (Model card 17, Intended use (local agents, tool calling, structured outputs, fine-tuning), a limitation (not recommended for agentic coding or complex multi-ste…); 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.)
- Agent interface, LFM3-1.7B +15: LFM3-1.7B 100 (Chat template 20, chat_template.jinja in the repository (12,129 characters), with roles, tools, deferred tools, tool results and the thinking switch.); Ornith-1.5-122B 85 (Chat template 20, a Jinja file in the repository.)
- Reach, LFM3-1.7B +10: LFM3-1.7B 85 (Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB, and 1.10 GB × 1.2 = 1.3 GB, which fits 8 GB, well inside the 16 GB line.); 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, Ornith-1.5-122B +10: LFM3-1.7B 40 (File safety 15, safetensors only, no .bin, .pt or .pth files (25).); 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…)