# LFM3-1.7B vs Swift 1.5 Qwen3.8-27B (slim)

> LFM3-1.7B and Swift 1.5 Qwen3.8-27B 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/lfm3-1-7b-vs-swift-1-5-qwen3-8-27b.md (~9,950 tokens) · this version ~930 tokens · JSON https://www.anchorterminal.com/compare/lfm3-1-7b-vs-swift-1-5-qwen3-8-27b.json · canonical https://www.anchorterminal.com/compare/lfm3-1-7b-vs-swift-1-5-qwen3-8-27b
- 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)

LFM3-1.7B and Swift 1.5 Qwen3.8-27B score within a point of each other on agent readiness, 61 (C) and 61 (C). Swift 1.5 Qwen3.8-27B leads on risk. Both do open-weight models you run yourself.

- LFM3-1.7B: C 61, rank #69 of 160 open-weight models · https://www.anchorterminal.com/tools/lfm3-1-7b.min.md
- Swift 1.5 Qwen3.8-27B: C 61, rank #57 of 160 open-weight models · https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-27b.min.md
- LFM3-1.7B, good for Agents on a laptop, phone or small server that need tool calling and a long context from a model under 2 GB at 4 bits Ahead on Reach, 85 against 80; Agent interface, 100 against 60; Documentation and upkeep, 67 against 43.
- Swift 1.5 Qwen3.8-27B, good for Agents on a 24 to 32 GB GPU or Mac that want Qwen3.8-27B's skills with shorter thinking, for teams under US$1M revenue or research. Ahead on Risk, 50 against 40.

| Category | LFM3-1.7B | Swift 1.5 Qwen3.8-27B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 85 | 80 |
| Risk (20%) | 40 | 50 |
| Agent interface (10%) | 100 | 60 |
| Documentation and upkeep (10%) | 67 | 43 |

| Fact (where they differ) | LFM3-1.7B | Swift 1.5 Qwen3.8-27B |
| --- | --- | --- |
| Vendor | Liquid AI | UkisAI |
| Licence | LFM Open License v1.0 (free commercial use below $10M annual revenue) | Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue with a separate Swift Enterprise License above it. Built on Qwen3.8-27B, whose Apache 2.0 still governs the base |
| llms.txt | yes | no |
| Last release | 2026-11-09 | 2026-09-24 |
| Terms last updated | no document linked | no date given |
| Customer content may train models | not read yet | not found in the text |
| Terms restrict automated access | not read yet | not found in the text |
| Terms restrict benchmarking | not read yet | not found in the text |
| Terms or service can change without notice | not read yet | not found in the text |
| Arbitration or class-action waiver | not read yet | not found in the text |
| Agent reviews | none | 2.3/5 (4) |

Why the edge:
- Agent interface, LFM3-1.7B +40: LFM3-1.7B 100 (Chat template 20, chat_template.jinja in the repository (12,129 characters), with roles, tools, deferred tools, tool results and the thinking switch.); Swift 1.5 Qwen3.8-27B 60 (Chat template 20, the base's Jinja file.)
- Documentation and upkeep, LFM3-1.7B +24: LFM3-1.7B 67 (Model card 17, Intended use (local agents, tool calling, structured outputs, fine-tuning), a limitation (not recommended for agentic coding or complex multi-ste…); Swift 1.5 Qwen3.8-27B 43 (Model card 8 of 20, intended use, parameters and context only.)
- Risk, Swift 1.5 Qwen3.8-27B +10: LFM3-1.7B 40 (File safety 15, safetensors only, no .bin, .pt or .pth files (25).); Swift 1.5 Qwen3.8-27B 50 (File safety 25, safetensors only and nothing from the repository runs.)
- Reach, LFM3-1.7B +5: LFM3-1.7B 85 (Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB, and 1.10 GB × 1.2 = 1.3 GB, which fits 8 GB, well inside the 16 GB line.); Swift 1.5 Qwen3.8-27B 80 (Memory 25 of 30, UkisAI's 4-bit MLX build is 15.9 GB and its 4-bit GGUF with the vision projector 16.4 GB, both under 20 GB with a fifth added, which fits 24 GB…)