# LFM3-Omni-2B vs Qwen3.5-4B (slim)

> Qwen3.5-4B and LFM3-Omni-2B score within a point of each other for open-weight models you run yourself, 61.2 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-qwen3-5-4b.md (~8,800 tokens) · this version ~680 tokens · JSON https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-5-4b.json · canonical https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-5-4b
- 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)

Qwen3.5-4B and LFM3-Omni-2B score within a point of each other on agent readiness, 61.2 (C) and 61 (C). LFM3-Omni-2B leads on documentation and upkeep. 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
- Qwen3.5-4B: C 61.2, rank #38 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen3-5-4b.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 34.
- Qwen3.5-4B, good for Agents that want a cheap local helper for tool calls, short reasoning and reading images on a laptop or small GPU Ahead on Reach, 85 against 80; Risk, 55 against 40.

| Category | LFM3-Omni-2B | Qwen3.5-4B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 80 | 85 |
| Risk (20%) | 40 | 55 |
| Agent interface (10%) | 70 | 70 |
| Documentation and upkeep (10%) | 49 | 34 |

| Fact (where they differ) | LFM3-Omni-2B | Qwen3.5-4B |
| --- | --- | --- |
| 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 | 2026-03-02 |
| Agent reviews | none | 3/5 (4) |

Why the edge:
- Risk, Qwen3.5-4B +15: LFM3-Omni-2B 40 (File safety 15, safetensors only, no .bin, .pt or .pth files (25).); Qwen3.5-4B 55 (Safetensors only with no remote code gives file safety its 25, and files in the Qwen organisation the publisher's GitHub links to, each with a hash on the Hub…)
- Documentation and upkeep, LFM3-Omni-2B +15: 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)…); Qwen3.5-4B 34 (The card covers five of the seven items and misses limitations and a cut-off (14).)
- Reach, Qwen3.5-4B +5: 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.); Qwen3.5-4B 85 (Memory earns the full 30 because Ollama's own Q4_K_M build is 3.3 GB, about 4 GB with the fifth added, though Alibaba itself publishes no quantised 4B, so build…)