Side-by-side comparison of DeepSeek-V4-Pro (DeepSeek · China) and LFM2.5-8B-A1B (Liquid AI) for self-hosted deployment of the open-weight model. DeepSeek-V4-Pro is rated conditional; LFM2.5-8B-A1B is conditional. They part ways on licence: DeepSeek-V4-Pro is "MIT", LFM2.5-8B-A1B is "LFM Open License 1.0".
| Field | ||
|---|---|---|
| Summary | ||
| Verdict | Conditional Per the published LICENSE file, DeepSeek-V4-Pro ships under MIT with no commercial restrictions, so the weights themselves are deployable. The caveats are non-EU jurisdiction and a training corpus described only by aggregate token count (32T+) without a dataset list — both should be documented in any GDPR or AI Act compliance file before regulated use. | 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. |
| Last reviewed | 2026-04-28 | 2026-05-30 |
| Open-weight | ||
| Licence | MIT | LFM Open License 1.0 |
| Commercial use | Unrestricted | Free under $10M revenue |
| Training data | Categories only | Undisclosed |
| Origin | China | USA (Boston) |
| Performance & pricing? | ||
| Quality index | 52/100 | — |
| Speed | 36 tok/s | — |
| Blended price | $2.17/M | — |
| Context window | — | — |
| Evidence | ||
| Sources | ||
No overlapping sources between the two entries.