Qwen 3 14BvsDeepSeek R1 Distill Qwen 14B
Complete comparison between Qwen 3 14B (14B parameters, Alibaba) and DeepSeek R1 Distill Qwen 14B (14B, 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 copying and pasting between pages.
In brief
| Feature | Qwen 3 14B | DeepSeek R1 Distill Qwen 14B |
|---|---|---|
| Settings | 14B | 14B |
| Family | Qwen | DeepSeek |
| Author | Alibaba | DeepSeek |
| Origin | CN | CN |
| License | Apache 2.0 | MIT |
| Context | 131,072 tokens | 131,072 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 14B | DeepSeek R1 Distill Qwen 14B |
|---|---|---|
| Q4_K_M (lightweight) | 9 GB | 9 GB |
| Q5_K_M (balanced) | 11 GB | 11 GB |
| Q8 (near-lossless) | 16 GB | 16 GB |
| FP16 (max quality) | 28 GB | 28 GB |
| CPU-only RAM | 16 GB | 16 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 14B | DeepSeek R1 Distill Qwen 14B |
|---|---|---|
| RTX 4090 (24 GB) | 55 tok/s · Q8 | 55 tok/s · Q8 |
| RTX 4080 (16 GB) | 20 tok/s · Q8 | 20 tok/s · Q8 |
| RTX 3060 12GB (12 GB) | 6 tok/s · Q5_K_M | 6 tok/s · Q5_K_M |
| Apple M4 Pro (48 GB) (36 GB) | 20 tok/s · FP16 | 20 tok/s · FP16 |
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 14B
Dense 14B with hybrid thinking. Equals Qwen 2.5 32B Based on STEM/code.
- Dense 14B matching Qwen 2.5 32B Base
- Hybrid thinking
- 131k ctx
- Not yet at the level of specialized reasoners on AIME
Installation
DeepSeek R1 Distill Qwen 14B
Distilled R1 Qwen 14B. AIME24 69.7, MATH-500 93.9. Outperforms o1-mini on many benchmarks.
- AIME24 69.7
- MATH-500 93.9
- MIT
- Beats o1-mini
- Verbose (CoT)
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Qwen 3 14B or DeepSeek R1 Distill Qwen 14B?
On an RTX 4090, Qwen 3 14B runs at Q8 (~55 tok/s), DeepSeek R1 Distill Qwen 14B in Q8 (~55 tok/s). In raw throughput, DeepSeek R1 Distill Qwen 14B wins. See the configurator to test your exact GPU.
Which uses the least VRAM?
Both require the same VRAM in Q4 (9 GB). The choice then comes down to the license or perceived quality.
Can these models be used in commercial production?
Qwen 3 14B is under Apache 2.0 — free for commercial use. DeepSeek R1 Distill Qwen 14B 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 14B (14B). More capable: Qwen 3 14B (14B). 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.