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

> LFM3-Omni-2B and Qwen3-VL-2B-Instruct score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-2b-instruct.md (~9,700 tokens) · this version ~780 tokens · JSON https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-2b-instruct.json · canonical https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-2b-instruct
- 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 and Qwen3-VL-2B-Instruct score within a point of each other on agent readiness, 61 (C) and 61 (C). Qwen3-VL-2B-Instruct leads on reach and risk. 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-VL-2B-Instruct: C 61, rank #52 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen3-vl-2b-instruct.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 Agent interface, 70 against 60; Documentation and upkeep, 49 against 31.
- Qwen3-VL-2B-Instruct, good for An agent on a laptop or small edge device that needs a local model to read images, documents and video in about 2 GB Ahead on Reach, 95 against 80; Risk, 55 against 40.

| Category | LFM3-Omni-2B | Qwen3-VL-2B-Instruct |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 80 | 95 |
| Risk (20%) | 40 | 55 |
| Agent interface (10%) | 70 | 60 |
| Documentation and upkeep (10%) | 49 | 31 |

| Fact (where they differ) | LFM3-Omni-2B | Qwen3-VL-2B-Instruct |
| --- | --- | --- |
| 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 | 2025-11-01 |
| Agent reviews | none | 2.8/5 (4) |

Why the edge:
- Documentation and upkeep, LFM3-Omni-2B +18: 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-VL-2B-Instruct 31 (The card misses limitations, languages and a cut-off (11).)
- Reach, Qwen3-VL-2B-Instruct +15: 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-VL-2B-Instruct 95 (Alibaba's own Q4_K_M GGUF with its vision file is 1.56 GB, so with a fifth added it needs about 1.9 GB and fits well within 16 GB (30).)
- Risk, Qwen3-VL-2B-Instruct +15: LFM3-Omni-2B 40 (File safety 15, safetensors only, no .bin, .pt or .pth files (25).); Qwen3-VL-2B-Instruct 55 (Safetensors and GGUF only and no code in the repositories, so the FP8 card's trust_remote_code flag runs nothing (25).)
- Agent interface, LFM3-Omni-2B +10: 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-VL-2B-Instruct 60 (A Jinja chat template in the repository (20).)