Side-by-side comparison of Ling-2.6 1T (inclusionAI · China) and MiniMax-M2.7 (MiniMax AI · China) for self-hosted deployment of the open-weight model. Ling-2.6 1T is rated conditional; MiniMax-M2.7 is blocked. They part ways on licence: Ling-2.6 1T is "MIT", MiniMax-M2.7 is "MiniMax Non-Commercial License".
| Field | ||
|---|---|---|
| Summary | ||
| Verdict | Conditional Per the published model card, Ling-2.6 1T is an MIT-licensed 1-trillion-parameter MoE with a 262k-token context, hybrid MLA + Linear attention and multi-token-prediction support, targeted at production agentic workloads. Permissive weights enable EU self-hosting in principle, though the deployment footprint is non-trivial; vendor jurisdiction (Ant Group, China) and undisclosed training data remain the regulated-buyer blockers. | Blocked Per current documentation, the MiniMax Non-Commercial License prohibits commercial deployment without individually negotiated written authorization from MiniMax, making the weights unsuitable for EU commercial workloads out-of-the-box. Opaque training data and Shanghai-based publisher compound the EU AI Act and data-transfer gaps. |
| Last reviewed | 2026-05-03 | 2026-04-17 |
| Open-weight | ||
| Licence | MIT | MiniMax Non-Commercial License |
| Commercial use | Unrestricted | Non-commercial only |
| Training data | Undisclosed | Undisclosed |
| Origin | China | China (Shanghai) |
| Performance & pricing? | ||
| Quality index | — | 50/100 |
| Speed | — | 46 tok/s |
| Blended price | — | $0.53/M |
| Context window | — | — |
| Evidence | ||
| Sources | ||
No overlapping sources between the two entries.