DeepSeek R1 Distill Qwen 14B vs Phi-4 Reasoning 14B
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | DeepSeek R1 Distill Qwen 14B | Phi-4 Reasoning 14B |
|---|---|---|
| Parameters | 14B | 14B |
| Author | DeepSeek | Microsoft |
| License | MIT | MIT |
| Context window | 0k | 0k |
| VRAM at Q4 | 9 GB | 9 GB |
| VRAM at Q5 | 11 GB | 11 GB |
| VRAM at Q8 | 16 GB | 16 GB |
| VRAM at FP16 | 28 GB | 28 GB |
| Use cases | reasoning | reasoning |
Verdict
Both models sit in a similar size class. The pick depends on tags, license, and benchmarks rather than raw parameter count.
The two models at a glance
About DeepSeek R1 Distill Qwen 14B
DeepSeek's R1 reasoning distilled into Qwen 14B under MIT. AIME24 69.7 and MATH-500 93.9 — beats o1-mini on most reasoning benchmarks. Strengths: AIME24 69.7 and MATH-500 93.9, Beats o1-mini on multiple reasoning benchmarks, MIT license — no usage restrictions, 131k context.
About Phi-4 Reasoning 14B
Microsoft's 14B reasoner that beats R1-Distill-Llama-70B on AIME and GPQA with 50x fewer parameters. MIT-licensed, English-first, with a 32K context. Strengths: Beats R1-Distill-Llama-70B on AIME and GPQA with 50x fewer parameters, MIT license, Increased RoPE base frequency improves long-form reasoning, Practical hardware footprint for a frontier-class reasoner.
How they compare
DeepSeek R1 Distill Qwen 14B comes from DeepSeek and Phi-4 Reasoning 14B from Microsoft, they belong to the DeepSeek and Phi families respectively. This comparison is built entirely from structured specs — parameter count, VRAM by quantization, context window, license, and published benchmark scores — so the verdict below reflects measurable differences rather than marketing claims.
DeepSeek R1 Distill Qwen 14B and Phi-4 Reasoning 14B share the same 14B parameter class. Both need about 9 GB of VRAM at a Q4 quantization, so they fit the same GPU tier.
The two models target different sweet spots: DeepSeek R1 Distill Qwen 14B is tuned for reasoning, while Phi-4 Reasoning 14B leans toward reasoning. Match the model to your dominant workload rather than to raw size.
For long-context work, DeepSeek R1 Distill Qwen 14B offers the bigger window (128k vs 32k tokens).
Memory, quantization & throughput
Across quantization levels, DeepSeek R1 Distill Qwen 14B requires Q4 ≈ 9 GB, Q5 ≈ 11 GB, Q8 ≈ 16 GB, FP16 ≈ 28 GB, while Phi-4 Reasoning 14B requires Q4 ≈ 9 GB, Q5 ≈ 11 GB, Q8 ≈ 16 GB, FP16 ≈ 28 GB. In practice DeepSeek R1 Distill Qwen 14B needs a 12 GB card at Q4, so plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity of Q8 or FP16.
Without a GPU, DeepSeek R1 Distill Qwen 14B needs roughly 16 GB of system RAM to run on CPU and Phi-4 Reasoning 14B about 16 GB — workable for offline use but far slower than GPU inference. On a mid-range GPU you can expect on the order of 20 tokens/sec from DeepSeek R1 Distill Qwen 14B and 20 from Phi-4 Reasoning 14B, scaling up to 55 and 55 tokens/sec on high-end hardware.
Which fits your GPU
Here is the highest-quality quantization of each model that fits common GPU memory budgets, so you can match DeepSeek R1 Distill Qwen 14B or Phi-4 Reasoning 14B to the card you actually own:
- On a 12 GB GPU: DeepSeek R1 Distill Qwen 14B runs at Q5 (11 GB); Phi-4 Reasoning 14B runs at Q5 (11 GB).
- On a 16 GB GPU: DeepSeek R1 Distill Qwen 14B runs at Q8 (16 GB); Phi-4 Reasoning 14B runs at Q8 (16 GB).
- On a 24 GB GPU: DeepSeek R1 Distill Qwen 14B runs at Q8 (16 GB); Phi-4 Reasoning 14B runs at Q8 (16 GB).
Benchmark scores
Reported benchmarks for DeepSeek R1 Distill Qwen 14B: AIME 2024 69.7, MATH-500 93.9, GPQA 59.1.
Bottom line: which should you pick?
- Pick DeepSeek R1 Distill Qwen 14B for long-context work (up to 128k tokens).
Which GPU should you buy to run DeepSeek R1 Distill Qwen 14B?
To run DeepSeek R1 Distill Qwen 14B locally at Q4, you need ~9 GB of VRAM. The best value for this is a RTX 5070 (12 GB VRAM).
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Frequently asked questions
What is the difference between DeepSeek R1 Distill Qwen 14B and Phi-4 Reasoning 14B?
The headline differences: both are 14B models; their context windows differ (128k vs 32k tokens). Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.
Can DeepSeek R1 Distill Qwen 14B and Phi-4 Reasoning 14B run on a 24 GB GPU?
At a Q4 quantization, DeepSeek R1 Distill Qwen 14B needs about 9 GB of VRAM and fits comfortably on a 24 GB GPU; Phi-4 Reasoning 14B needs about 9 GB and fits comfortably on a 24 GB GPU. Both have the same Q4 footprint.
What licenses do DeepSeek R1 Distill Qwen 14B and Phi-4 Reasoning 14B use?
DeepSeek R1 Distill Qwen 14B is licensed under MIT and Phi-4 Reasoning 14B under MIT.
Which has the longer context window, DeepSeek R1 Distill Qwen 14B or Phi-4 Reasoning 14B?
DeepSeek R1 Distill Qwen 14B has the larger context window (128k vs 32k tokens), so it handles longer documents and codebases in a single prompt.