Compare / Qwen3 32B vs DeepSeek-R1-Distill-Qwen-32B
Qwen3 32B vs DeepSeek-R1-Distill-Qwen-32B
Updated November 2026
Verdict
Qwen3 32B scores higher on the Small LM Index: 52.4 vs 50.9, although their likely ranges overlap, so the lead is not certain. On the 2 benchmarks both labs report, Qwen3 32B wins 2 and DeepSeek-R1-Distill-Qwen-32B wins 0. Both need about 23.2 GB of memory at 4-bit quantization. Only Qwen3 32B has a hosted API on OpenRouter ($0.13 per million tokens); the other has to be self-hosted.
Pick Qwen3 32B if…
- you want the strongest model overall
- you mostly write code
- you need math and reasoning
- you want a hosted API rather than running it yourself
- you work with long documents
- you want the more widely used model (more quantized builds and community support)
Pick DeepSeek-R1-Distill-Qwen-32B if…
No clear advantage in the data we have.
Specifications
| Qwen3 32B | DeepSeek-R1-Distill-Qwen-32B | |
|---|---|---|
| Small LM Index | 52.4 (likely 47–57) | 50.9 (likely 46–56) |
| Lab | Alibaba (Qwen) · China | DeepSeek · China |
| Released | 2025-04-29 | 2025-01-20 |
| Parameters | 32.8B (dense) | 32.8B (dense) |
| Memory at 4-bit | ~23.2 GB | ~23.2 GB |
| Context window | 131K tokens | 128K tokens |
| Input | text | text |
| Reasoning | Hybrid (thinking on/off) | Always thinks |
| License | Apache-2.0 | MIT |
| API price / 1M tokens | $0.13 ($0.080 in, $0.28 out) | Self-host only |
| Hugging Face downloads (30 days) | 3M | 348.2K |
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 | Qwen3 32B | DeepSeek-R1-Distill-Qwen-32B |
|---|---|---|
| GPQA Diamond Knowledge | ≈ 66 | 62.1 |
| AIME Math | 72.9 | 72.6 |
| LiveCodeBench Coding | 65.7 | 57.2 |
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, Qwen3 32B or DeepSeek-R1-Distill-Qwen-32B?
Qwen3 32B scores higher on the Small LM Index: 52.4 vs 50.9, although their likely ranges overlap, so the lead is not certain. On the 2 benchmarks both labs report, Qwen3 32B wins 2 and DeepSeek-R1-Distill-Qwen-32B wins 0.
Which is cheaper to run?
Only Qwen3 32B has a hosted API on OpenRouter ($0.13 per million tokens); the other has to be self-hosted. Both need about 23.2 GB of memory at 4-bit quantization.
Which has the longer context window?
Qwen3 32B has the longer context window: 131K tokens against 128K.
Can I use Qwen3 32B and DeepSeek-R1-Distill-Qwen-32B commercially?
Qwen3 32B is released under Apache-2.0 and DeepSeek-R1-Distill-Qwen-32B under MIT. Apache 2.0 and MIT allow commercial use; other licenses can add conditions, so read the license before shipping a product.