QwQ 32B vs Phi-4 Reasoning 14B

The two models at a glance

About QwQ 32B

Alibaba's dedicated 32B reasoner, trained with reinforcement learning rather than distillation. Hits 79.5 on AIME24 and 90.6 on MATH-500 — a direct Apache-licensed alternative to DeepSeek R1. Strengths: Direct competitor to DeepSeek R1 at a fraction of the size, 131K context for long thinking traces, Trained with RL, not just distilled, Apache 2.0.

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

QwQ 32B comes from Alibaba and Phi-4 Reasoning 14B from Microsoft, they belong to the Qwen 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, QwQ 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: QwQ 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. For long-context work, QwQ 32B offers the bigger window (128k vs 32k tokens).

Memory, quantization & throughput

Across quantization levels, QwQ 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 QwQ 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, QwQ 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 QwQ 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 QwQ 32B or Phi-4 Reasoning 14B to the card you actually own:

• On a 12 GB GPU: QwQ 32B does not fit; Phi-4 Reasoning 14B runs at Q5 (11 GB).
• On a 16 GB GPU: QwQ 32B does not fit; Phi-4 Reasoning 14B runs at Q8 (16 GB).
• On a 24 GB GPU: QwQ 32B runs at Q5 (23 GB); Phi-4 Reasoning 14B runs at Q8 (16 GB).

Benchmark scores

Reported benchmarks for QwQ 32B: AIME 2024 79.5, MATH-500 90.6.

Bottom line: which should you pick?

• Pick QwQ 32B for long-context work (up to 128k tokens).
• Pick Phi-4 Reasoning 14B for lower VRAM and faster inference; pick QwQ 32B for maximum headline quality.

Which GPU should you buy to run QwQ 32B?

To run QwQ 32B locally at Q4, you need ~19 GB of VRAM. The best value for this is a RTX 4090 (24 GB VRAM).

Frequently asked questions

What is the difference between QwQ 32B and Phi-4 Reasoning 14B?

The headline differences: QwQ 32B is a 32B model and Phi-4 Reasoning 14B is 14B; their context windows differ (128k vs 32k tokens); they ship under different licenses (Apache 2.0 vs MIT). Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.

Can QwQ 32B and Phi-4 Reasoning 14B run on a 24 GB GPU?

At a Q4 quantization, QwQ 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, QwQ 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 QwQ 32B and Phi-4 Reasoning 14B use?

QwQ 32B is licensed under Apache 2.0 and Phi-4 Reasoning 14B under MIT.

Which has the longer context window, QwQ 32B or Phi-4 Reasoning 14B?

QwQ 32B has the larger context window (128k vs 32k tokens), so it handles longer documents and codebases in a single prompt.