Side-by-side comparison of LFM2.5-8B-A1B (Liquid AI) and Ling-2.6 Flash (inclusionAI · China) for self-hosted deployment of the open-weight model. LFM2.5-8B-A1B is rated conditional; Ling-2.6 Flash is conditional. They part ways on licence: LFM2.5-8B-A1B is "LFM Open License 1.0", Ling-2.6 Flash is "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 Per the published model card, Ling-2.6 Flash is an MIT-licensed 104B / 7.4B-active MoE built on a hybrid Lightning-Linear + MLA attention design, positioned for agentic and tool-use workflows. Permissive weights are deployable in EU infrastructure; the headline risks for regulated buyers are vendor jurisdiction (Ant Group's inclusionAI lab, headquartered in China) and the absence of any training-data disclosure in the model card. |
| Last reviewed | 2026-05-30 | 2026-05-03 |
| Open-weight | ||
| Licence | LFM Open License 1.0 | MIT |
| Commercial use | Free under $10M revenue | Unrestricted |
| Training data | Undisclosed | Undisclosed |
| Origin | USA (Boston) | China |
| Performance & pricing? | ||
| Quality index | — | 26/100 |
| Speed | — | 211 tok/s |
| Blended price | — | $0.15/M |
| Context window | — | — |
| Evidence | ||
| Sources | ||
No overlapping sources between the two entries.