Compare / DeepSeek-R1-0528-Qwen3-8B vs Qwen3 8B
DeepSeek-R1-0528-Qwen3-8B vs Qwen3 8B
Updated November 2026
Verdict
DeepSeek-R1-0528-Qwen3-8B scores higher on the Small LM Index: 53.1 vs 48.5, although their likely ranges overlap, so the lead is not certain. On the 3 benchmarks both labs report, DeepSeek-R1-0528-Qwen3-8B wins 2 and Qwen3 8B wins 1. Both need about 6.8 GB of memory at 4-bit quantization. Neither model has a hosted API on OpenRouter, so you would run both yourself.
Pick DeepSeek-R1-0528-Qwen3-8B if…
- you want the strongest model overall
- you mostly write code
- you need math and reasoning
Pick Qwen3 8B if…
- you work with long documents
- you want the more widely used model (more quantized builds and community support)
Specifications
| DeepSeek-R1-0528-Qwen3-8B | Qwen3 8B | |
|---|---|---|
| Small LM Index | 53.1 (likely 48–58) | 48.5 (likely 44–53) |
| Lab | DeepSeek · China | Alibaba (Qwen) · China |
| Released | 2025-05-29 | 2025-04-29 |
| Parameters | 8.2B (dense) | 8.2B (dense) |
| Memory at 4-bit | ~6.8 GB | ~6.8 GB |
| Context window | 128K tokens | 131K tokens |
| Input | text | text |
| Reasoning | Always thinks | Hybrid (thinking on/off) |
| License | MIT | Apache-2.0 |
| API price / 1M tokens | Self-host only | Self-host only |
| Hugging Face downloads (30 days) | 357.7K | 7.5M |
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-R1-0528-Qwen3-8B | Qwen3 8B |
|---|---|---|
| GPQA Diamond Knowledge | 61.1 | 62.0 |
| AIME Math | 76.3 | 67.3 |
| LiveCodeBench Coding | 60.5 | 57.5 |
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-R1-0528-Qwen3-8B or Qwen3 8B?
DeepSeek-R1-0528-Qwen3-8B scores higher on the Small LM Index: 53.1 vs 48.5, although their likely ranges overlap, so the lead is not certain. On the 3 benchmarks both labs report, DeepSeek-R1-0528-Qwen3-8B wins 2 and Qwen3 8B wins 1.
Which is cheaper to run?
Neither model has a hosted API on OpenRouter, so you would run both yourself. Both need about 6.8 GB of memory at 4-bit quantization.
Which has the longer context window?
Qwen3 8B has the longer context window: 131K tokens against 128K.
Can I use DeepSeek-R1-0528-Qwen3-8B and Qwen3 8B commercially?
DeepSeek-R1-0528-Qwen3-8B is released under MIT and Qwen3 8B under Apache-2.0. Apache 2.0 and MIT allow commercial use; other licenses can add conditions, so read the license before shipping a product.
Related comparisons
- DeepSeek-R1-0528-Qwen3-8B vs Qwen3.5 9B
- DeepSeek-R1-0528-Qwen3-8B vs Granite 4.2 8B
- DeepSeek-R1-0528-Qwen3-8B vs Ministral 3 8B
- DeepSeek-R1-0528-Qwen3-8B vs Gemma 4 E4B
- DeepSeek-R1-0528-Qwen3-8B vs MiniCPM-SALA
- DeepSeek-R1-0528-Qwen3-8B vs Nemotron Nano 9B v2
- Apertus 1.5 8B vs DeepSeek-R1-0528-Qwen3-8B
- Granite 4.2 8B vs Qwen3 8B
- Ministral 3 8B vs Qwen3 8B
- Gemma 4 E4B vs Qwen3 8B