Phi-4 14BvsQwen 3.6 27B
Complete comparison between Phi-4 14B (14B parameters, Microsoft) and Qwen 3.6 27B (27B, Alibaba). VRAM required by quantization, tokens/second measured on 4 reference GPUs, use-case verdict, license, installation commands. All figures are calculated from catalog data—no copy-pasting between pages.
In brief
| Feature | Phi-4 14B | Qwen 3.6 27B |
|---|---|---|
| Settings | 14B | 27B |
| Family | Phi | Qwen |
| Author | Microsoft | Alibaba |
| Origin | US | CN |
| License | MIT | Apache 2.0 |
| Context | 16,384 tokens | 262,144 tokens |
| Output | December 2024 | April 2026 |
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 | Phi-4 14B | Qwen 3.6 27B |
|---|---|---|
| Q4_K_M (lightweight) | 9 GB | 16 GB |
| Q5_K_M (balanced) | 11 GB | 19 GB |
| Q8 (near-lossless) | 16 GB | 29 GB |
| FP16 (max quality) | 28 GB | 54 GB |
| CPU-only RAM | 16 GB | 28 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 | Phi-4 14B | Qwen 3.6 27B |
|---|---|---|
| RTX 4090 (24 GB) | 55 tok/s · Q8 | 32 tok/s · Q5_K_M |
| RTX 4080 (16 GB) | 20 tok/s · Q8 | 13 tok/s · Q4_K_M |
| RTX 3060 12GB (12 GB) | 6 tok/s · Q5_K_M | ✗ too heavy |
| Apple M4 Pro (48 GB) (36 GB) | 20 tok/s · FP16 | 13 tok/s · Q8 |
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
Phi-4 14B
Exceptional reasoning for its size. STEM-focused.
- Best 14B for reasoning in late 2024
- MIT License
- Very strong in math/science
- Good at coding
- 16k context only — major limitation
- Less multilingual than Qwen
Installation
Qwen 3.6 27B
Dense multimodal 27B released April 22, 2026. 262k ctx (1M YaRN). SWE-bench Verified 77.2%.
- SWE-bench Verified 77.2%
- Native multimodal
- 262k context (1M YaRN)
- Apache 2.0
- 16+ GB in Q4
- Hybrid architecture (recent llama.cpp support required)
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Phi-4 14B or Qwen 3.6 27B?
On a RTX 4090, Phi-4 14B runs at Q8 (~55 tok/s), Qwen 3.6 27B in Q5_K_M (~32 tok/s). In pure throughput terms, Phi-4 14B wins. See the configurator to test your exact GPU.
Which uses the least VRAM?
In Q4_K_M, Phi-4 14B fits in 9 GB versus 16 GB for its rival—a 7 GB gap, significant if you're targeting an RTX 3060 or an 8 GB 4060 Ti.
Phi-4 14B or Qwen 3.6 27B for coding?
Qwen 3.6 27B is the best fit (code-specialized, top HumanEval scores). For an IDE copilot, pair it with Continue or Aider.
Can these models be used in commercial production?
Phi-4 14B is under the MIT license — free for commercial use. Qwen 3.6 27B is under Apache 2.0 — also free to use. For SaaS, prefer Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: Phi-4 14B (14B). More capable: Qwen 3.6 27B (27B). 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.