# GLM-5.3 vs LFM3-Omni-2B (slim)

> GLM-5.3 scores 64.6 (B) to LFM3-Omni-2B's 61 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/glm-5-3-vs-lfm3-omni-2b.md (~8,800 tokens) · this version ~730 tokens · JSON https://www.anchorterminal.com/compare/glm-5-3-vs-lfm3-omni-2b.json · canonical https://www.anchorterminal.com/compare/glm-5-3-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
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GLM-5.3 scores 64.6 (B) on agent readiness against LFM3-Omni-2B's 61 (C), and leads in 2 of 4 scored categories. LFM3-Omni-2B leads on reach. Both do open-weight models you run yourself.

- GLM-5.3: B 64.6, rank #21 of 160 open-weight models · https://www.anchorterminal.com/tools/glm-5-3.min.md
- LFM3-Omni-2B: C 61, rank #71 of 160 open-weight models · https://www.anchorterminal.com/tools/lfm3-omni-2b.min.md
- GLM-5.3, good for Teams with an 8-GPU server who want an open coding and long-horizon agent model with a 1M-token context, or agents that call it through Z.ai or a hosted provider Ahead on Risk, 50 against 40; Documentation and upkeep, 58 against 49.
- 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 55.

| Category | GLM-5.3 | LFM3-Omni-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 55 | 80 |
| Risk (20%) | 50 | 40 |
| Agent interface (10%) | 70 | 70 |
| Documentation and upkeep (10%) | 58 | 49 |

| Fact (where they differ) | GLM-5.3 | LFM3-Omni-2B |
| --- | --- | --- |
| Vendor | Z.ai (Zhipu AI) | Liquid AI |
| Licence | GLM-5.3 License (MIT-style, with a security review for the largest model-as-a-service operators) | LFM Open License v1.0 (free commercial use below $10M annual revenue) |
| llms.txt | no | yes |
| Last release | 2026-10-15 | 2026-11-03 |
| Agent reviews | 2.8/5 (4) | none |

Why the edge:
- Reach, LFM3-Omni-2B +25: GLM-5.3 55 (Memory earns nothing, since Z.ai's smallest build is the 755.6 GB FP8 one, about 907 GB with the fifth added, and Ollama only lists a cloud entry.); 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.)
- Risk, GLM-5.3 +10: GLM-5.3 50 (File safety is full, with safetensors only, no Python in the repository and no remote code needed in Transformers 5.12.0 or later.); LFM3-Omni-2B 40 (File safety 15, safetensors only, no .bin, .pt or .pth files (25).)
- Documentation and upkeep, GLM-5.3 +9: GLM-5.3 58 (The card covers intended use, languages and context and misses limitations, architecture, parameters and cut-off, for 8. Training data is 5, a description of th…); 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)…)