Side-by-side comparison of LFM2.5-8B-A1B (Liquid AI) and MiniMax M2 (MiniMax · China) for self-hosted deployment of the open-weight model. LFM2.5-8B-A1B is rated conditional; MiniMax M2 is conditional. They part ways on licence: LFM2.5-8B-A1B is "LFM Open License 1.0", MiniMax M2 is "Modified MIT".
| 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 229B agent-focused model from MiniMax, Modified MIT. Strong software-engineering and tool-use benchmarks. Family has iterated fast (M2 / M2.1 / M2.5 / M2.7 across 2025-2026). Same Chinese-origin alignment and supply-chain considerations as DeepSeek, Qwen, Kimi. |
| Last reviewed | 2026-05-30 | 2026-04-16 |
| Open-weight | ||
| Licence | LFM Open License 1.0 | Modified MIT |
| Commercial use | Free under $10M revenue | Yes |
| Training data | Undisclosed | Undisclosed |
| Origin | USA (Boston) | China |
| Performance & pricing? | ||
| Quality index | — | 36/100 |
| Speed | — | 72 tok/s |
| Blended price | — | $0.53/M |
| Context window | — | — |
| Evidence | ||
| Sources | ||
No overlapping sources between the two entries.