Side-by-side comparison of Ling-2.6 1T (inclusionAI · China) and Ling-2.6 Flash (inclusionAI · China) for self-hosted deployment of the open-weight model. Ling-2.6 1T is rated conditional; Ling-2.6 Flash is conditional. On the four sovereignty dimensions we track, they score identically — the difference is in context and org posture.
| 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. | 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 | 2026-05-03 | 2026-05-03 |
| Open-weight | ||
| Licence | MIT | MIT |
| Commercial use | Unrestricted | Unrestricted |
| Training data | Undisclosed | Undisclosed |
| Origin | China | China |
| Performance & pricing? | ||
| Quality index | — | — |
| Speed | — | — |
| Blended price | — | — |
| Context window | — | — |
| Evidence | ||
| Sources | ||