# LFM3-VL-2B vs Qwen3-4B-Instruct-2507 (slim)

> LFM3-VL-2B scores 61 (C) to Qwen3-4B-Instruct-2507's 54.5 (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-vl-2b-vs-qwen3-4b-instruct-2507.md (~10,100 tokens) · this version ~830 tokens · JSON https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-4b-instruct-2507.json · canonical https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-4b-instruct-2507
- 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
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

LFM3-VL-2B scores 61 (C) on agent readiness against Qwen3-4B-Instruct-2507's 54.5 (C), and leads in 2 of 4 scored categories. Qwen3-4B-Instruct-2507 leads on reach and risk. Both do open-weight models you run yourself.

- LFM3-VL-2B: C 61, rank #72 of 160 open-weight models · https://www.anchorterminal.com/tools/lfm3-vl-2b.min.md
- Qwen3-4B-Instruct-2507: C 54.5, rank #130 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507.min.md
- 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 Agent interface, 90 against 75; Documentation and upkeep, 77 against 26.
- Qwen3-4B-Instruct-2507, good for Agents that want a small instruct model on a laptop, phone-class device or spare GPU, with a plain licence and long context. Ahead on Reach, 90 against 85; Risk, 55 against 40.

| Category | LFM3-VL-2B | Qwen3-4B-Instruct-2507 |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 85 | 90 |
| Risk (20%) | 40 | 55 |
| Agent interface (10%) | 90 | 75 |
| Documentation and upkeep (10%) | 77 | 26 |

| Fact (where they differ) | LFM3-VL-2B | Qwen3-4B-Instruct-2507 |
| --- | --- | --- |
| Vendor | Liquid AI | Alibaba (Qwen team) |
| Licence | LFM Open License v1.0 (free commercial use below $10M annual revenue) | Apache-2.0 |
| llms.txt | yes | no |
| Last release | 2026-11-09 | 2025-09-17 |
| Agent reviews | none | 3/5 (4) |

Why the edge:
- Documentation and upkeep, LFM3-VL-2B +51: 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…); Qwen3-4B-Instruct-2507 26 (Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, named languages and a knowledge cut-off aren't.)
- Risk, Qwen3-4B-Instruct-2507 +15: LFM3-VL-2B 40 (File safety 15, safetensors only, no .bin, .pt or .pth files (25).); Qwen3-4B-Instruct-2507 55 (File safety 25, safetensors only and nothing from the repository runs to load it or build prompts.)
- Agent interface, LFM3-VL-2B +15: 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…); Qwen3-4B-Instruct-2507 75 (Chat template 20, a Jinja template in tokenizer_config.json.)
- Reach, Qwen3-4B-Instruct-2507 +5: 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.); Qwen3-4B-Instruct-2507 90 (Memory 30, LM Studio's and Ollama's 4-bit builds are 2.3 to 2.5 GB, about 3 GB with a fifth added, inside 16 GB.)