Compare / DeepSeek-R1-Distill-Qwen-7B vs Gemma 3n E4B
DeepSeek-R1-Distill-Qwen-7B vs Gemma 3n E4B
Updated November 2026
Verdict
DeepSeek-R1-Distill-Qwen-7B scores higher on the Small LM Index: 39.8 vs 18.2. On the 3 benchmarks both labs report, DeepSeek-R1-Distill-Qwen-7B wins 3 and Gemma 3n E4B wins 0. Both need about 5.7 GB of memory at 4-bit quantization. Neither model has a hosted API on OpenRouter, so you would run both yourself.
Pick DeepSeek-R1-Distill-Qwen-7B if…
- you want the strongest model overall
- you mostly write code
- you need math and reasoning
- you work with long documents
- you need a permissive license (MIT)
- you want the more widely used model (more quantized builds and community support)
Pick Gemma 3n E4B if…
- you need image input
Specifications
| DeepSeek-R1-Distill-Qwen-7B | Gemma 3n E4B | |
|---|---|---|
| Small LM Index | 39.8 (likely 35–45) | 18.2 (likely 16–21) |
| Lab | DeepSeek · China | Google · United States |
| Released | 2025-01-20 | 2025-06-26 |
| Parameters | 7.6B (dense) | 7.8B (dense) |
| Memory at 4-bit | ~5.7 GB | ~5.5 GB |
| Context window | 128K tokens | 32K tokens |
| Input | text | text, image, audio, video |
| Reasoning | Always thinks | No thinking |
| License | MIT | Gemma |
| API price / 1M tokens | Self-host only | Self-host only |
| Hugging Face downloads (30 days) | 321.5K | 12.7K |
Benchmarks
Higher is better. Bold marks the winner where both labs report a score. ≈ marks the index model's estimate for a score that wasn't reported, ✓ an independent measurement and * a score measured by another lab.
| Benchmark | DeepSeek-R1-Distill-Qwen-7B | Gemma 3n E4B |
|---|---|---|
| MMLU-Pro Knowledge | ≈ 65 | 50.6 |
| GPQA Diamond Knowledge | 49.1 | 23.7 |
| AIME Math | 55.5 | 11.6 |
| LiveCodeBench Coding | 37.6 | 25.7 |
| MMLU Knowledge | — | 64.9 |
| Multilingual MMLU Multilingual | — | 60.3 |
Scores are mostly reported by each lab with its own settings, so small gaps are noise. See the methodology. Open in the interactive comparison to add more models.
Questions
Which is better, DeepSeek-R1-Distill-Qwen-7B or Gemma 3n E4B?
DeepSeek-R1-Distill-Qwen-7B scores higher on the Small LM Index: 39.8 vs 18.2. On the 3 benchmarks both labs report, DeepSeek-R1-Distill-Qwen-7B wins 3 and Gemma 3n E4B wins 0.
Which is cheaper to run?
Neither model has a hosted API on OpenRouter, so you would run both yourself. Both need about 5.7 GB of memory at 4-bit quantization.
Which has the longer context window?
DeepSeek-R1-Distill-Qwen-7B has the longer context window: 128K tokens against 32K.
Can I use DeepSeek-R1-Distill-Qwen-7B and Gemma 3n E4B commercially?
DeepSeek-R1-Distill-Qwen-7B is released under MIT and Gemma 3n E4B under Gemma. Apache 2.0 and MIT allow commercial use; other licenses can add conditions, so read the license before shipping a product.
Related comparisons
- DeepSeek-R1-Distill-Qwen-7B vs Falcon H1R 7B
- DeepSeek-R1-Distill-Qwen-7B vs Hunyuan 7B Instruct
- DeepSeek-R1-Distill-Qwen-7B vs OLMo 3 7B Think
- DeepSeek-R1-Distill-Qwen-7B vs MiMo-7B-RL
- Falcon H1R 7B vs Gemma 3n E4B
- Gemma 3n E4B vs Ling 3.0 Tiny
- Edge0 8B-A1B (preview) vs Gemma 3n E4B
- Gemma 3n E4B vs MiMo-7B-RL
- Gemma 3n E4B vs Granite 4.2 8B