Side-by-side comparison of DeepSeek R1 (DeepSeek · China) and Gemma 4 E4B Instruct (Google DeepMind · United States) for self-hosted deployment of the open-weight model. DeepSeek R1 is rated conditional; Gemma 4 E4B Instruct is conditional. They part ways on licence: DeepSeek R1 is "MIT", Gemma 4 E4B Instruct is "Apache 2.0".
| Field | ||
|---|---|---|
| Summary | ||
| Verdict | Conditional Frontier reasoning model at o1-class performance. MIT licence makes weights legally clean. Same Chinese-origin alignment/supply-chain considerations as DeepSeek V3. Distilled Qwen/Llama versions inherit their base licence. | Conditional Based on published licence terms, Gemma 4 E4B is an edge-optimised multimodal variant under pure Apache 2.0 with no prohibited-use carve-outs. Audio input (30s) and on-device deployment push GDPR biometric, AI Act emotion-recognition, and Art. 25 data-protection-by-design obligations entirely onto the integrator with no Google-side telemetry or kill-switch. |
| Last reviewed | 2026-04-15 | 2026-04-17 |
| Open-weight | ||
| Licence | MIT | Apache 2.0 |
| Commercial use | Yes | Unrestricted |
| Training data | Undisclosed | Domain-level summary |
| Origin | China | United States |
| Performance & pricing? | ||
| Quality index | 27/100 | 15/100 |
| Speed | — | — |
| Blended price | $2.36/M | — |
| Context window | — | — |
| Evidence | ||
| Sources | ||
No overlapping sources between the two entries.