DeepSeek R1 Distill 32B vs Phi-4 Reasoning 14B
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | DeepSeek R1 Distill 32B | Phi-4 Reasoning 14B |
|---|---|---|
| Parameters | 32B | 14B |
| Author | DeepSeek | Microsoft |
| License | MIT | MIT |
| Context window | 0k | 0k |
| VRAM at Q4 | 19 GB | 9 GB |
| VRAM at Q5 | 23 GB | 11 GB |
| VRAM at Q8 | 35 GB | 16 GB |
| VRAM at FP16 | 64 GB | 28 GB |
| Use cases | reasoning | reasoning |
Verdict
DeepSeek R1 Distill 32B is significantly larger (32B vs 14B), so expect higher quality but heavier VRAM and slower throughput.
The two models at a glance
About DeepSeek R1 Distill 32B
The 32B DeepSeek R1 distill — the best accessible open-weight reasoner we've tested. Explicit chain-of-thought, MIT-licensed, runs on a single 24GB GPU. Strengths: Best open-weight reasoner that fits on one consumer GPU, Excellent math and science performance, Explicit step-by-step thinking, MIT license.
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 32B 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.
At 32B vs 14B parameters, DeepSeek R1 Distill 32B is the larger of the two. At Q4, Phi-4 Reasoning 14B fits in about 9 GB of VRAM versus 19 GB for the other — a 10 GB difference that matters on consumer GPUs.
The two models target different sweet spots: DeepSeek R1 Distill 32B is tuned for reasoning, while Phi-4 Reasoning 14B leans toward reasoning. Match the model to your dominant workload rather than to raw size.
On a typical mid-range GPU, Phi-4 Reasoning 14B pushes roughly 20 tokens/sec versus 12, so it is the more responsive choice for interactive or high-volume use.
Memory, quantization & throughput
Across quantization levels, DeepSeek R1 Distill 32B requires Q4 ≈ 19 GB, Q5 ≈ 23 GB, Q8 ≈ 35 GB, FP16 ≈ 64 GB, while Phi-4 Reasoning 14B requires Q4 ≈ 9 GB, Q5 ≈ 11 GB, Q8 ≈ 16 GB, FP16 ≈ 28 GB. In practice DeepSeek R1 Distill 32B wants a 24 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 32B needs roughly 32 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 12 tokens/sec from DeepSeek R1 Distill 32B and 20 from Phi-4 Reasoning 14B, scaling up to 30 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 32B or Phi-4 Reasoning 14B to the card you actually own:
- On a 12 GB GPU: DeepSeek R1 Distill 32B does not fit; Phi-4 Reasoning 14B runs at Q5 (11 GB).
- On a 16 GB GPU: DeepSeek R1 Distill 32B does not fit; Phi-4 Reasoning 14B runs at Q8 (16 GB).
- On a 24 GB GPU: DeepSeek R1 Distill 32B runs at Q5 (23 GB); Phi-4 Reasoning 14B runs at Q8 (16 GB).
Benchmark scores
Reported benchmarks for DeepSeek R1 Distill 32B: AIME 2024 72.6, MATH-500 94.3, GPQA 62.1.
Bottom line: which should you pick?
- Pick Phi-4 Reasoning 14B for lower VRAM and faster inference; pick DeepSeek R1 Distill 32B for maximum headline quality.
Which GPU should you buy to run DeepSeek R1 Distill 32B?
To run DeepSeek R1 Distill 32B locally at Q4, you need ~19 GB of VRAM. The best value for this is a RTX 4090 (24 GB VRAM).
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Frequently asked questions
What is the difference between DeepSeek R1 Distill 32B and Phi-4 Reasoning 14B?
The headline differences: DeepSeek R1 Distill 32B is a 32B model and Phi-4 Reasoning 14B is 14B. 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 32B and Phi-4 Reasoning 14B run on a 24 GB GPU?
At a Q4 quantization, DeepSeek R1 Distill 32B needs about 19 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. Phi-4 Reasoning 14B is the lighter option for tight VRAM budgets.
Which is faster, DeepSeek R1 Distill 32B or Phi-4 Reasoning 14B?
Phi-4 Reasoning 14B is the smaller model (14B vs 32B), so on the same hardware it runs faster and uses less memory. The larger model trades speed for headline quality.
What licenses do DeepSeek R1 Distill 32B and Phi-4 Reasoning 14B use?
DeepSeek R1 Distill 32B is licensed under MIT and Phi-4 Reasoning 14B under MIT.