Side-by-side comparison of LFM2.5-8B-A1B (Liquid AI) and MiniCPM5-1B (OpenBMB) for self-hosted deployment of the open-weight model. LFM2.5-8B-A1B is rated conditional; MiniCPM5-1B is conditional. They part ways on licence: LFM2.5-8B-A1B is "LFM Open License 1.0", MiniCPM5-1B is "Apache 2.0".
| Field | ||
|---|---|---|
| Summary | ||
| Verdict | Conditional Per the published LFM Open License v1.0, Liquid AI's terms are Apache-2.0-derived but cap free commercial use at USD 10M annual company revenue — above that threshold deployers must negotiate a paid licence with [email protected]. The model itself is a strong 8.3B / 1.5B-active MoE built for CPU and edge inference, but the 38-trillion-token training corpus is undisclosed and Liquid AI is a US-based MIT spinoff, so EU regulated buyers should run their own DPIA and document the data-transfer story alongside any commercial-tier negotiation. | Conditional Per the published Apache 2.0 LICENSE, the 1.08B-parameter weights carry no use restrictions and are well suited to on-device or edge deployments. Unusually for a Chinese-origin release, OpenBMB ships a full data card naming every pre-training and SFT corpus, which closes the AI Act Art. 53 transparency gap most peers leave open. The remaining hedge for regulated EU buyers is vendor jurisdiction (Tsinghua-affiliated lab, mainland China) — self-host the weights and treat any vendor-side service as out of scope. |
| Last reviewed | 2026-05-30 | 2026-05-30 |
| Open-weight | ||
| Licence | LFM Open License 1.0 | Apache 2.0 |
| Commercial use | Free under $10M revenue | Unrestricted |
| Training data | Undisclosed | Documented |
| Origin | USA (Boston) | China |
| Performance & pricing? | ||
| Quality index | — | — |
| Speed | — | — |
| Blended price | — | — |
| Context window | — | — |
| Evidence | ||
| Sources | ||
No overlapping sources between the two entries.