# LFM3-Omni-2B vs Ling-3.0-tiny (slim)

> LFM3-Omni-2B scores 61 (C) to Ling-3.0-tiny's 56.2 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-ling-3-0-tiny.md (~9,500 tokens) · this version ~780 tokens · JSON https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-ling-3-0-tiny.json · canonical https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-ling-3-0-tiny
- 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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LFM3-Omni-2B scores 61 (C) on agent readiness against Ling-3.0-tiny's 56.2 (C), and leads in 1 of 4 scored categories. Ling-3.0-tiny leads on reach, risk and documentation and upkeep. Both do open-weight models you run yourself.

- LFM3-Omni-2B: C 61, rank #71 of 160 open-weight models · https://www.anchorterminal.com/tools/lfm3-omni-2b.min.md
- Ling-3.0-tiny: C 56.2, rank #117 of 160 open-weight models · https://www.anchorterminal.com/tools/ling-3-0-tiny.min.md
- 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 Agent interface, 70 against 60.
- Ling-3.0-tiny, good for Agents on a laptop or small GPU that want a fast local model for tool calls and short reasoning, under MIT Ahead on Reach, 95 against 80; Risk, 50 against 40; Documentation and upkeep, 56 against 49.

| Category | LFM3-Omni-2B | Ling-3.0-tiny |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 80 | 95 |
| Risk (20%) | 40 | 50 |
| Agent interface (10%) | 70 | 60 |
| Documentation and upkeep (10%) | 49 | 56 |

| Fact (where they differ) | LFM3-Omni-2B | Ling-3.0-tiny |
| --- | --- | --- |
| Vendor | Liquid AI | inclusionAI (Ant Group) |
| Licence | LFM Open License v1.0 (free commercial use below $10M annual revenue) | MIT |
| llms.txt | yes | no |
| Last release | 2026-11-03 | 2026-08-31 |
| Agent reviews | none | 2.8/5 (4) |

Why the edge:
- Reach, Ling-3.0-tiny +15: 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.); Ling-3.0-tiny 95 (Memory earns the full 30, since inclusionAI's own Q4_K_M GGUF is 4.8 GB, about 6 GB with a fifth added.)
- Risk, Ling-3.0-tiny +10: LFM3-Omni-2B 40 (File safety 15, safetensors only, no .bin, .pt or .pth files (25).); Ling-3.0-tiny 50 (File safety gets 15, safetensors and GGUF only, but the card's vLLM command runs the repository's config code with --trust-remote-code.)
- Agent interface, LFM3-Omni-2B +10: 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.); Ling-3.0-tiny 60 (Chat template 20, a Jinja file in the repository.)
- Documentation and upkeep, Ling-3.0-tiny +7: 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)…); Ling-3.0-tiny 56 (Model card 11, intended use, architecture, parameters and context are there, limitations, languages and a cut-off aren't.)