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

> LFM3-VL-2B 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.

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- 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-VL-2B 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-VL-2B: C 61, rank #72 of 160 open-weight models · https://www.anchorterminal.com/tools/lfm3-vl-2b.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-VL-2B, good for Agents on a laptop, phone or robot that need to read screens, documents, images and video from a model under 2 GB at 4 bits Ahead on Reach, 85 against 60; Agent interface, 90 against 60; Documentation and upkeep, 77 against 34.

| Category | DeepSeek-V3.2 | LFM3-VL-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 60 | 85 |
| Risk (20%) | 50 | 40 |
| Agent interface (10%) | 60 | 90 |
| Documentation and upkeep (10%) | 34 | 77 |

| Fact (where they differ) | DeepSeek-V3.2 | LFM3-VL-2B |
| --- | --- | --- |
| 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:
- Documentation and upkeep, LFM3-VL-2B +43: 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-VL-2B 77 (Model card 17, Intended use (questions about video, documents, charts and app screens, finding objects, spatial reasoning), a limitation (not recommended for co…)
- Agent interface, LFM3-VL-2B +30: DeepSeek-V3.2 60 (The chat template earns 10, as the format exists only as Python code to run.); LFM3-VL-2B 90 (Chat template 20, chat_template.jinja in the repository (12,129 characters), with roles, images and video, tools, deferred tools, tool results and the thinking…)
- Reach, LFM3-VL-2B +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-VL-2B 85 (Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.20 GB projector file, and 1.30 GB × 1.2 = 1.6 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-VL-2B 40 (File safety 15, safetensors only, no .bin, .pt or .pth files (25).)