QwQ 32BvsPhi-4 Reasoning 14B
Complete comparison between QwQ 32B (32B parameters, Alibaba) 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 | QwQ 32B | Phi-4 Reasoning 14B |
|---|---|---|
| Settings | 32B | 14B |
| Family | Qwen | Phi |
| Author | Alibaba | Microsoft |
| Origin | CN | US |
| License | Apache 2.0 | MIT |
| Context | 131,072 tokens | 32 768 tokens |
| Output | March 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 | QwQ 32B | Phi-4 Reasoning 14B |
|---|---|---|
| Q4_K_M (lightweight) | 19 GB | 9 GB |
| Q5_K_M (balanced) | 23 GB | 11 GB |
| Q8 (near-lossless) | 35 GB | 16 GB |
| FP16 (max quality) | 64 GB | 28 GB |
| CPU-only RAM | 32 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 | QwQ 32B | Phi-4 Reasoning 14B |
|---|---|---|
| RTX 4090 (24 GB) | 30 tok/s · Q5_K_M | 55 tok/s · Q8 |
| RTX 4080 (16 GB) | ✗ too heavy | 20 tok/s · Q8 |
| RTX 3060 12GB (12 GB) | ✗ too heavy | 6 tok/s · Q5_K_M |
| Apple M4 Pro (48 GB) (36 GB) | 12 tok/s · Q8 | 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
QwQ 32B
Apache 2.0 RL reasoner. AIME24 79.5, MATH-500 90.6. Direct competitor to DeepSeek R1.
- Direct competitor to R1
- 131k ctx
- Apache 2.0
- Verbose in thinking mode
- YaRN required beyond 8k
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): QwQ 32B or Phi-4 Reasoning 14B?
On a RTX 4090, QwQ 32B runs at Q5_K_M (~30 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?
In Q4_K_M, Phi-4 Reasoning 14B fits in 9 GB versus 19 GB for its rival — a 10 GB gap, significant if you are targeting an RTX 3060 or an 8 GB 4060 Ti.
Can these models be used in commercial production?
QwQ 32B is under Apache 2.0 — 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: Phi-4 Reasoning 14B (14B). More capable: QwQ 32B (32B). If you are 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.