Phi-4 14B vs Qwen 3.6 27B
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | Phi-4 14B | Qwen 3.6 27B |
|---|---|---|
| Parameters | 14B | 27B |
| Author | Microsoft | Alibaba |
| License | MIT | Apache 2.0 |
| Context window | 0k | 0k |
| VRAM at Q4 | 9 GB | 16 GB |
| VRAM at Q5 | 11 GB | 19 GB |
| VRAM at Q8 | 16 GB | 29 GB |
| VRAM at FP16 | 28 GB | 54 GB |
| Use cases | chat, general, reasoning | chat, general, code, reasoning, vision, multilingual |
Verdict
Qwen 3.6 27B is significantly larger (27B vs 14B), so expect higher quality but heavier VRAM and slower throughput.
The two models at a glance
About Phi-4 14B
Microsoft's Phi-4 14B, trained on ultra-curated synthetic data with a heavy STEM bias. The 14B reasoning leader at the end of 2024. Strengths: Top-tier 14B reasoning at release, MIT license, Strong math, science, and code performance, Tight, well-formatted outputs.
About Qwen 3.6 27B
Dense 27B multimodal model from Alibaba (April 2026), scoring 77.2% on SWE-bench Verified with 262k native context (1M via YaRN). The Qwen 3.6 generation's developer-friendly workhorse. Strengths: 77.2% SWE-bench Verified — frontier coding accuracy, Native multimodal text + image, 262k context, extendable to 1M with YaRN, Apache 2.0.
How they compare
Phi-4 14B comes from Microsoft and Qwen 3.6 27B from Alibaba, they belong to the Phi and Qwen 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 14B vs 27B parameters, Qwen 3.6 27B is the larger of the two. At Q4, Phi-4 14B fits in about 9 GB of VRAM versus 16 GB for the other — a 7 GB difference that matters on consumer GPUs.
The two models target different sweet spots: Phi-4 14B is tuned for chat, general, reasoning, while Qwen 3.6 27B leans toward chat, general, code, reasoning, vision, multilingual. Match the model to your dominant workload rather than to raw size.
On a typical mid-range GPU, Phi-4 14B pushes roughly 20 tokens/sec versus 13, so it is the more responsive choice for interactive or high-volume use. For long-context work, Qwen 3.6 27B offers the bigger window (256k vs 16k tokens).
Memory, quantization & throughput
Across quantization levels, Phi-4 14B requires Q4 ≈ 9 GB, Q5 ≈ 11 GB, Q8 ≈ 16 GB, FP16 ≈ 28 GB, while Qwen 3.6 27B requires Q4 ≈ 16 GB, Q5 ≈ 19 GB, Q8 ≈ 29 GB, FP16 ≈ 54 GB. In practice Phi-4 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, Phi-4 14B needs roughly 16 GB of system RAM to run on CPU and Qwen 3.6 27B about 28 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 Phi-4 14B and 13 from Qwen 3.6 27B, scaling up to 55 and 32 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 Phi-4 14B or Qwen 3.6 27B to the card you actually own:
- On a 12 GB GPU: Phi-4 14B runs at Q5 (11 GB); Qwen 3.6 27B does not fit.
- On a 16 GB GPU: Phi-4 14B runs at Q8 (16 GB); Qwen 3.6 27B runs at Q4 (16 GB).
- On a 24 GB GPU: Phi-4 14B runs at Q8 (16 GB); Qwen 3.6 27B runs at Q5 (19 GB).
Benchmark scores
Reported benchmarks for Phi-4 14B: MMLU 84.8, MATH 80.4, HumanEval 82.6.
Reported benchmarks for Qwen 3.6 27B: SWE-bench Verified 77.2, Terminal-Bench 59.3, SkillsBench 48.2.
Bottom line: which should you pick?
- Pick Qwen 3.6 27B for long-context work (up to 256k tokens).
- Pick Phi-4 14B for lower VRAM and faster inference; pick Qwen 3.6 27B for maximum headline quality.
- Pick Qwen 3.6 27B if your workload is code, multilingual, vision.
Which GPU should you buy to run Qwen 3.6 27B?
To run Qwen 3.6 27B locally at Q4, you need ~16 GB of VRAM. The best value for this is a RTX 5070 Ti (16 GB VRAM).
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Frequently asked questions
What is the difference between Phi-4 14B and Qwen 3.6 27B?
The headline differences: Phi-4 14B is a 14B model and Qwen 3.6 27B is 27B; their context windows differ (16k vs 256k tokens); they ship under different licenses (MIT vs Apache 2.0). Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.
Can Phi-4 14B and Qwen 3.6 27B run on a 24 GB GPU?
At a Q4 quantization, Phi-4 14B needs about 9 GB of VRAM and fits comfortably on a 24 GB GPU; Qwen 3.6 27B needs about 16 GB and fits comfortably on a 24 GB GPU. Phi-4 14B is the lighter option for tight VRAM budgets.
Which is faster, Phi-4 14B or Qwen 3.6 27B?
Phi-4 14B is the smaller model (14B vs 27B), so on the same hardware it runs faster and uses less memory. The larger model trades speed for headline quality.
What licenses do Phi-4 14B and Qwen 3.6 27B use?
Phi-4 14B is licensed under MIT and Qwen 3.6 27B under Apache 2.0.
Which has the longer context window, Phi-4 14B or Qwen 3.6 27B?
Qwen 3.6 27B has the larger context window (256k vs 16k tokens), so it handles longer documents and codebases in a single prompt.