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

> LFM3-Omni-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-omni-2b-vs-qwen3-4b-instruct-2507.md (~10,050 tokens) · this version ~830 tokens · JSON https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-4b-instruct-2507.json · canonical https://www.anchorterminal.com/compare/lfm3-omni-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-Omni-2B scores 61 (C) on agent readiness against Qwen3-4B-Instruct-2507's 54.5 (C), and leads in 1 of 4 scored categories. Qwen3-4B-Instruct-2507 leads on reach, risk and agent interface. Both do open-weight models you run yourself.

- LFM3-Omni-2B: C 61, rank #71 of 160 open-weight models · https://www.anchorterminal.com/tools/lfm3-omni-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-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 Documentation and upkeep, 49 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 80; Risk, 55 against 40; Agent interface, 75 against 70.

| Category | LFM3-Omni-2B | Qwen3-4B-Instruct-2507 |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 80 | 90 |
| Risk (20%) | 40 | 55 |
| Agent interface (10%) | 70 | 75 |
| Documentation and upkeep (10%) | 49 | 26 |

| Fact (where they differ) | LFM3-Omni-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 | 2026-11-03 | 2025-09-17 |
| Agent reviews | none | 3/5 (4) |

Why the edge:
- Documentation and upkeep, LFM3-Omni-2B +23: 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)…); 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-Omni-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.)
- Reach, Qwen3-4B-Instruct-2507 +10: 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.); 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.)
- Agent interface, Qwen3-4B-Instruct-2507 +5: LFM3-Omni-2B 70 (Chat template 20, chat_template.jinja in the repository (13,513 characters), with roles, images, video and audio, tools, deferred tools and tool results.); Qwen3-4B-Instruct-2507 75 (Chat template 20, a Jinja template in tokenizer_config.json.)