Compare / DeepSeek-R1-0528-Qwen3-8B vs Nemotron Nano 9B v2
DeepSeek-R1-0528-Qwen3-8B vs Nemotron Nano 9B v2
Updated November 2026
Verdict
DeepSeek-R1-0528-Qwen3-8B and Nemotron Nano 9B v2 are roughly tied on the Small LM Index (53.1 vs 52.5). On the 3 benchmarks both labs report, DeepSeek-R1-0528-Qwen3-8B wins 1 and Nemotron Nano 9B v2 wins 2. 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 need math and reasoning
- you need a permissive license (MIT)
Pick Nemotron Nano 9B v2 if…
- you mostly write code
Specifications
| DeepSeek-R1-0528-Qwen3-8B | Nemotron Nano 9B v2 | |
|---|---|---|
| Small LM Index | 53.1 (likely 48–58) | 52.5 (likely 48–57) |
| Lab | DeepSeek · China | NVIDIA · United States |
| Released | 2025-05-29 | 2025-08-18 |
| Parameters | 8.2B (dense) | 8.9B (dense) |
| Memory at 4-bit | ~6.8 GB | ~6.2 GB |
| Context window | 128K tokens | 128K tokens |
| Input | text | text |
| Reasoning | Always thinks | Hybrid (thinking on/off) |
| License | MIT | NVIDIA Open Model |
| API price / 1M tokens | Self-host only | Self-host only |
| Hugging Face downloads (30 days) | 357.7K | 141.1K |
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 | Nemotron Nano 9B v2 |
|---|---|---|
| GPQA Diamond Knowledge | 61.1 | 64.0 |
| AIME Math | 76.3 | 72.1 |
| LiveCodeBench Coding | 60.5 | 71.1 |
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 Nemotron Nano 9B v2?
DeepSeek-R1-0528-Qwen3-8B and Nemotron Nano 9B v2 are roughly tied on the Small LM Index (53.1 vs 52.5). On the 3 benchmarks both labs report, DeepSeek-R1-0528-Qwen3-8B wins 1 and Nemotron Nano 9B v2 wins 2.
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?
Both have a 128K-token context window.
Can I use DeepSeek-R1-0528-Qwen3-8B and Nemotron Nano 9B v2 commercially?
DeepSeek-R1-0528-Qwen3-8B is released under MIT and Nemotron Nano 9B v2 under NVIDIA Open Model. Apache 2.0 and MIT allow commercial use; other licenses can add conditions, so read the license before shipping a product.
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