Qwen 3 32BvsDeepSeek R1 Distill 32B
Complete comparison between Qwen 3 32B (32B parameters, Alibaba) and DeepSeek R1 Distill 32B (32B, DeepSeek). VRAM required by quantization, tokens per second measured on 4 reference GPUs, verdict by use case, license, installation commands. All figures are calculated from catalog data—no copy-pasting between pages.
In brief
| Feature | Qwen 3 32B | DeepSeek R1 Distill 32B |
|---|---|---|
| Settings | 32B | 32B |
| Family | Qwen | DeepSeek |
| Author | Alibaba | DeepSeek |
| Origin | CN | CN |
| License | Apache 2.0 | MIT |
| Context | 131,072 tokens | 32 768 tokens |
| Output | April 2025 | January 2025 |
Memory footprint
Approximate VRAM required for inference with an average context window. The winner (in green) is the model that uses less — smaller models have the edge.
| Quantization | Qwen 3 32B | DeepSeek R1 Distill 32B |
|---|---|---|
| Q4_K_M (lightweight) | 19 GB | 19 GB |
| Q5_K_M (balanced) | 23 GB | 23 GB |
| Q8 (near-lossless) | 35 GB | 35 GB |
| FP16 (max quality) | 64 GB | 64 GB |
| CPU-only RAM | 32 GB | 32 GB |
Estimated speed (tokens/second)
Estimates based on the best quantization that can fit on each GPU. Actual figures depend on the prompt, context, and engine (llama.cpp, vLLM, MLX). Methodology.
| Reference GPU | Qwen 3 32B | DeepSeek R1 Distill 32B |
|---|---|---|
| RTX 4090 (24 GB) | 30 tok/s · Q5_K_M | 30 tok/s · Q5_K_M |
| RTX 4080 (16 GB) | ✗ too heavy | ✗ too heavy |
| RTX 3060 12GB (12 GB) | ✗ too heavy | ✗ too heavy |
| Apple M4 Pro (48 GB) (36 GB) | 12 tok/s · Q8 | 12 tok/s · Q8 |
Verdict by use case
For each common use case, we indicate which of the two is better suited based on its tags, size, and specialization.
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Strengths and weaknesses
Qwen 3 32B
Dense 32B with thinking mode. MMLU-Pro 65.5, SuperGPQA 39.8.
- Solid reasoning in thinking mode
- 131k ctx
- Apache 2.0
- Worse than QwQ-32B dedicated to pure reasoning
Installation
DeepSeek R1 Distill 32B
The best accessible open-weight reasoner.
- Explicit step-by-step reasoning
- Excellent at math and science
- 32k context
- MIT License
- Very verbose (thinking tokens)
- Slower to respond
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Qwen 3 32B or DeepSeek R1 Distill 32B?
On an RTX 4090, Qwen 3 32B runs at Q5_K_M (~30 tok/s), DeepSeek R1 Distill 32B in Q5_K_M (~30 tok/s). In pure throughput, DeepSeek R1 Distill 32B wins. See the configurator to test your exact GPU.
Which uses the least VRAM?
Both require the same VRAM in Q4 (19 GB). The choice then comes down to the license or perceived quality.
Can these models be used in commercial production?
Qwen 3 32B is under Apache 2.0 — free for commercial use. DeepSeek R1 Distill 32B is also free under the MIT license. For SaaS, favor Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: Qwen 3 32B (32B). More capable: Qwen 3 32B (32B). If you're unsure, run it configurator with your GPU and use case — it will decide based on both.
Alternatives to consider
If neither works for you, here are some neighboring models you could explore.