Side-by-side comparison of Ling-2.6 1T (inclusionAI · China) and Step-3.7-Flash (StepFun) for self-hosted deployment of the open-weight model. Ling-2.6 1T is rated conditional; Step-3.7-Flash is conditional. They part ways on licence: Ling-2.6 1T is "MIT", Step-3.7-Flash is "Apache 2.0".
| 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 Apache 2.0 LICENSE, the Step-3.7-Flash weights ship without commercial restriction — a 198B / 11B-active vision-language MoE with a 256k-token context aimed at tool-heavy and agentic workflows. The remaining EU-readiness gaps are the entirely undisclosed training corpus and StepFun's Shanghai-based vendor jurisdiction; deploy on self-managed EU infrastructure and document the Art. 50 transparency story for any synthetic or biometric output. |
| Last reviewed | 2026-05-03 | 2026-05-30 |
| Open-weight | ||
| Licence | MIT | Apache 2.0 |
| Commercial use | Unrestricted | Unrestricted |
| Training data | Undisclosed | Undisclosed |
| Origin | China | China (Shanghai) |
| Performance & pricing? | ||
| Quality index | 34/100 | — |
| Speed | — | — |
| Blended price | $0.85/M | — |
| Context window | — | — |
| Evidence | ||
| Sources | ||
No overlapping sources between the two entries.