# DeepSeek V4.1 Flash vs LFM3-VL-2B (slim)

> DeepSeek V4.1 Flash and LFM3-VL-2B score within a point of each other for open-weight models you run yourself, 61.5 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm3-vl-2b.md (~10,050 tokens) · this version ~830 tokens · JSON https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm3-vl-2b.json · canonical https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm3-vl-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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DeepSeek V4.1 Flash and LFM3-VL-2B score within a point of each other on agent readiness, 61.5 (C) and 61 (C). LFM3-VL-2B leads on reach, agent interface and documentation and upkeep. Both do open-weight models you run yourself.

- DeepSeek V4.1 Flash: C 61.5, rank #35 of 160 open-weight models · https://www.anchorterminal.com/tools/deepseek-v4-1-flash.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 V4.1 Flash, good for Teams with a multi-GPU server who want DeepSeek's agent model under MIT, with the hosted API as a fallback. 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 75; Documentation and upkeep, 77 against 48.

| Category | DeepSeek V4.1 Flash | LFM3-VL-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 60 | 85 |
| Risk (20%) | 50 | 40 |
| Agent interface (10%) | 75 | 90 |
| Documentation and upkeep (10%) | 48 | 77 |

| Fact (where they differ) | DeepSeek V4.1 Flash | 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 | 2026-10-17 | 2026-11-09 |
| Agent reviews | 2.8/5 (4) | none |

Why the edge:
- Documentation and upkeep, LFM3-VL-2B +29: DeepSeek V4.1 Flash 48 (The cards cover architecture, parameters and context but not intended use, limitations, languages or a cut-off, so 8. Training data is a general description wit…); 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…)
- Reach, LFM3-VL-2B +25: DeepSeek V4.1 Flash 60 (Memory scores nothing, because the only build is DeepSeek's own at 510.3 GB, about 612 GB with the fifth added, and nobody's runtime library holds a smaller one…); 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.)
- Agent interface, LFM3-VL-2B +15: DeepSeek V4.1 Flash 75 (There's no Jinja template in the 17 October repository, only code to run, so the template line gets 10. DeepSeek documents the DSML tool format both ways and sh…); 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…)
- Risk, DeepSeek V4.1 Flash +10: DeepSeek V4.1 Flash 50 (The weights are safetensors only, but 10 comes off because the card's way to build prompts is running encoding.py from the repository, since there's no Jinja te…); LFM3-VL-2B 40 (File safety 15, safetensors only, no .bin, .pt or .pth files (25).)