# DeepSeek-V3.2 vs LFM3-1.7B (slim)

> LFM3-1.7B scores 61 (C) to DeepSeek-V3.2's 58.6 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm3-1-7b.md (~9,500 tokens) · this version ~780 tokens · JSON https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm3-1-7b.json · canonical https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm3-1-7b
- 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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LFM3-1.7B scores 61 (C) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 3 of 4 scored categories. DeepSeek-V3.2 leads on risk. Both do open-weight models you run yourself.

- DeepSeek-V3.2: C 58.6, rank #102 of 160 open-weight models · https://www.anchorterminal.com/tools/deepseek-v3-2.min.md
- LFM3-1.7B: C 61, rank #69 of 160 open-weight models · https://www.anchorterminal.com/tools/lfm3-1-7b.min.md
- DeepSeek-V3.2, good for Teams with an eight-GPU server who want an MIT agent model with broad runtime support, or agents calling it through a hosted provider Ahead on Risk, 50 against 40.
- 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 60; Agent interface, 100 against 60; Documentation and upkeep, 67 against 34.

| Category | DeepSeek-V3.2 | LFM3-1.7B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 60 | 85 |
| Risk (20%) | 50 | 40 |
| Agent interface (10%) | 60 | 100 |
| Documentation and upkeep (10%) | 34 | 67 |

| Fact (where they differ) | DeepSeek-V3.2 | LFM3-1.7B |
| --- | --- | --- |
| Vendor | DeepSeek | Liquid AI |
| Licence | MIT | LFM Open License v1.0 (free commercial use below $10M annual revenue) |
| llms.txt | no | yes |
| Last release | 2025-12-01 | 2026-11-09 |
| Agent reviews | 2.3/5 (4) | none |

Why the edge:
- Agent interface, LFM3-1.7B +40: DeepSeek-V3.2 60 (The chat template earns 10, as the format exists only as Python code to run.); 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.)
- Documentation and upkeep, LFM3-1.7B +33: DeepSeek-V3.2 34 (The model card earns 14 when read with DeepSeek's own V3.2 model card PDF, which adds intended use and a parameter count, leaving languages and a knowledge cut-…); 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…)
- Reach, LFM3-1.7B +25: DeepSeek-V3.2 60 (Memory earns nothing, because the only build from DeepSeek or a runtime's library is the 689.5 GB FP8 release, which needs about 827 GB with the fifth added.); 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.)
- Risk, DeepSeek-V3.2 +10: DeepSeek-V3.2 50 (File safety earns 15, safetensors only but 10 off because prompts are built by running DeepSeek's Python from the repository.); LFM3-1.7B 40 (File safety 15, safetensors only, no .bin, .pt or .pth files (25).)