DeepSeek R1 Distill Qwen 14BvsPhi-4 Reasoning 14B
Complete comparison between DeepSeek R1 Distill Qwen 14B (14B parameters, DeepSeek) and Phi-4 Reasoning 14B (14B, Microsoft). VRAM required for each quantization, tokens per second measured on 4 reference GPUs, use-case verdict, license, and installation commands. All figures are calculated from catalog data—no copy-pasting between pages.
In brief
| Feature | DeepSeek R1 Distill Qwen 14B | Phi-4 Reasoning 14B |
|---|---|---|
| Settings | 14B | 14B |
| Family | DeepSeek | Phi |
| Author | DeepSeek | Microsoft |
| Origin | CN | US |
| License | MIT | MIT |
| Context | 131,072 tokens | 32 768 tokens |
| Output | January 2025 | April 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 | DeepSeek R1 Distill Qwen 14B | Phi-4 Reasoning 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 | DeepSeek R1 Distill Qwen 14B | Phi-4 Reasoning 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
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
Phi-4 Reasoning 14B
MIT 14B reasoner. Beats R1-Distill-Llama-70B on AIME/GPQA with 50× fewer parameters.
- Beats R1-Distill-Llama-70B on AIME/GPQA
- MIT
- R1 approach with 50× fewer parameters
- Anglais-first
- Weak on non-Python code
- Ctx limited to 32k
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): DeepSeek R1 Distill Qwen 14B or Phi-4 Reasoning 14B?
On a RTX 4090, DeepSeek R1 Distill Qwen 14B runs at Q8 (~55 tok/s), Phi-4 Reasoning 14B in Q8 (~55 tok/s). In raw throughput, Phi-4 Reasoning 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?
DeepSeek R1 Distill Qwen 14B is under the MIT license — free for commercial use. Phi-4 Reasoning 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: DeepSeek R1 Distill Qwen 14B (14B). More capable: DeepSeek R1 Distill Qwen 14B (14B). If you're unsure, run the 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.