Compare
Phi-4 14B vs Qwen3 14B
Same hardware, same arithmetic, side by side. Every memory figure is calculated from published model geometry rather than quoted from a marketing page.
Estimated
Side by side
On GeForce RTX 4090
| Property | Phi-4 14B | Qwen3 14B |
|---|---|---|
| Organization | Microsoft | Alibaba Qwen |
| Parameters | 14.7B | 14.8B |
| Architecture | Phi3 · dense | Qwen3 · dense |
| Context | 16k | 128k |
| Layers | 40 | 40 |
| Hidden size | 5,120 | 5,120 |
| KV heads | 10 of 40 | 8 of 40 |
| Licence | MIT | Apache 2.0 |
| Commercial use | Yes | Yes |
| Modalities | text | text |
| Released | 2024-12-12 | 2025-04-29 |
| Recommended quantization | Q8_0 | Q6_K |
| Memory needed | 17 GB | 13 GB |
| Estimated tok/s | 50 | 64 |
| Fit | Good fit | Excellent fit |
| Fine-tune here | Yes | Yes |
| MMLU (reported) | 84.8% | — |
Benchmark rows are figures the model's authors published, not ModelLM measurements, and the two models may not have been evaluated under identical conditions.
Trade-offs
Phi-4 14B
Trained largely on curated synthetic data. Punches far above its size on reasoning and maths; the short context limits what you can do with it.
Strengths
- Exceptional STEM reasoning for 14B
- MIT licensed
- Fits 12 GB at Q4_K_M
Limitations
- 16k context
- Narrower world knowledge than web-trained peers
- Weaker multilingual coverage
Qwen3 14B
The Qwen3 mid-size. Long context and a reasoning mode inside a footprint a 24 GB card handles comfortably.
Strengths
- 128k context at 14B
- Strong tool-call reliability
- Apache 2.0
Limitations
- Long context costs a large KV cache
- Thinking mode is slow for interactive chat
Common comparisons