# Cola2 vs LFM3-1.7B (slim)

> LFM3-1.7B scores 61 (C) to Cola2's 33.9 (F) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/cola2-vs-lfm3-1-7b.md (~8,500 tokens) · this version ~680 tokens · JSON https://www.anchorterminal.com/compare/cola2-vs-lfm3-1-7b.json · canonical https://www.anchorterminal.com/compare/cola2-vs-lfm3-1-7b
- 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 scores 61 (C) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.

- Cola2: F 33.9, rank #160 of 160 open-weight models · https://www.anchorterminal.com/tools/cola2.min.md
- LFM3-1.7B: C 61, rank #69 of 160 open-weight models · https://www.anchorterminal.com/tools/lfm3-1-7b.min.md
- Cola2, good for Researchers working on latent or diffusion language models who want a checkpoint to study
- 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 25; Agent interface, 100 against 20; Documentation and upkeep, 67 against 58.

| Category | Cola2 | LFM3-1.7B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 25 | 85 |
| Risk (20%) | 40 | 40 |
| Agent interface (10%) | 20 | 100 |
| Documentation and upkeep (10%) | 58 | 67 |

| Fact (where they differ) | Cola2 | LFM3-1.7B |
| --- | --- | --- |
| Vendor | ByteDance (Seed) | Liquid AI |
| Licence | None stated for the weights (the code is Apache 2.0) | LFM Open License v1.0 (free commercial use below $10M annual revenue) |
| llms.txt | no | yes |
| Last release | 2026-11-06 | 2026-11-09 |

Why the edge:
- Agent interface, LFM3-1.7B +80: Cola2 20 (Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer.); 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.)
- Reach, LFM3-1.7B +60: Cola2 25 (Memory 15, the only build is the F32 safetensors set, 35.07 GB, and 35.07 GB × 1.2 = 42.1 GB, within 64 GB.); 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.)
- Documentation and upkeep, LFM3-1.7B +9: Cola2 58 (Model card 8, Intended use and limitations (the out-of-scope list, and the unconverged latent model) and the architecture are on the card and the GitHub model c…); 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…)