# Cola2 vs LFM3-Audio-2B (slim)

> LFM3-Audio-2B 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-audio-2b.md (~8,700 tokens) · this version ~680 tokens · JSON https://www.anchorterminal.com/compare/cola2-vs-lfm3-audio-2b.json · canonical https://www.anchorterminal.com/compare/cola2-vs-lfm3-audio-2b
- 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-Audio-2B scores 61 (C) on agent readiness against Cola2's 33.9 (F), and leads in 2 of 4 scored categories. Cola2 leads on documentation and upkeep. 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-Audio-2B: C 61, rank #70 of 160 open-weight models · https://www.anchorterminal.com/tools/lfm3-audio-2b.min.md
- Cola2, good for Researchers working on latent or diffusion language models who want a checkpoint to study Ahead on Documentation and upkeep, 58 against 52.
- LFM3-Audio-2B, good for Voice agents on a laptop or phone that need speech in, speech out and tool calls from one small model Ahead on Reach, 80 against 25; Agent interface, 85 against 20.

| Category | Cola2 | LFM3-Audio-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 25 | 80 |
| Risk (20%) | 40 | 40 |
| Agent interface (10%) | 20 | 85 |
| Documentation and upkeep (10%) | 58 | 52 |

| Fact (where they differ) | Cola2 | LFM3-Audio-2B |
| --- | --- | --- |
| 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 | 2026-11-03 |

Why the edge:
- Agent interface, LFM3-Audio-2B +65: Cola2 20 (Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer.); LFM3-Audio-2B 85 (Chat template 20, chat_template.jinja in the repository (9,328 characters), with roles, audio, tools, deferred tools and tool results.)
- Reach, LFM3-Audio-2B +55: 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-Audio-2B 80 (Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.17 GB projector file, and 1.27 GB × 1.2 = 1.5 GB, which fits 8 GB, well inside the 16 GB line.)
- Documentation and upkeep, Cola2 +6: 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-Audio-2B 52 (Model card 17, Intended use (local agents, tool calling, structured outputs, fine-tuning), limitations (not recommended for agentic coding or complex multi-step…)