# Laguna M.1 vs LFM3-Omni-2B (slim)

> LFM3-Omni-2B 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-lfm3-omni-2b.md (~9,300 tokens) · this version ~880 tokens · JSON https://www.anchorterminal.com/compare/laguna-m-1-vs-lfm3-omni-2b.json · canonical https://www.anchorterminal.com/compare/laguna-m-1-vs-lfm3-omni-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-Omni-2B scores 61 (C) on agent readiness against Laguna M.1's 56.7 (C), and leads in 2 of 4 scored categories. Laguna M.1 leads on risk and agent interface. 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
- LFM3-Omni-2B: C 61, rank #71 of 160 open-weight models · https://www.anchorterminal.com/tools/lfm3-omni-2b.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 40; Agent interface, 85 against 70.
- LFM3-Omni-2B, good for Voice and camera agents on a laptop or phone that want one small model for hearing, seeing, speaking and calling tools Ahead on Reach, 80 against 70; Documentation and upkeep, 49 against 36.

| Category | Laguna M.1 | LFM3-Omni-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 70 | 80 |
| Risk (20%) | 55 | 40 |
| Agent interface (10%) | 85 | 70 |
| Documentation and upkeep (10%) | 36 | 49 |

| Fact (where they differ) | Laguna M.1 | LFM3-Omni-2B |
| --- | --- | --- |
| Vendor | Poolside | Liquid AI |
| Licence | Apache-2.0 | LFM Open License v1.0 (free commercial use below $10M annual revenue) |
| llms.txt | no | yes |
| Last release | 2026-07-14 | 2026-11-03 |
| 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:
- Risk, Laguna M.1 +15: 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…); LFM3-Omni-2B 40 (File safety 15, safetensors only, no .bin, .pt or .pth files (25).)
- Agent interface, Laguna M.1 +15: Laguna M.1 85 (Chat template 20, a Jinja file in the repository.); LFM3-Omni-2B 70 (Chat template 20, chat_template.jinja in the repository (13,513 characters), with roles, images, video and audio, tools, deferred tools and tool results.)
- Documentation and upkeep, LFM3-Omni-2B +13: Laguna M.1 36 (Model card 11 of 20, intended use, architecture, parameters and context are there, limitations, languages and a cut-off aren't.); LFM3-Omni-2B 49 (Model card 14, Intended use (one on-device model in place of separate speech recognition, vision, language and speech models), architecture, parameters (2.17B)…)
- Reach, LFM3-Omni-2B +10: 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.); LFM3-Omni-2B 80 (Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.29 GB projector file, and 1.39 GB × 1.2 = 1.7 GB, which fits 8 GB, well inside the 16 GB line.)