Phi-4 vs Qwen3 14B — Microsoft’s reasoning model versus Alibaba’s mid-size dense model. Below is the full side-by-side: specifications, API pricing, context window, local hardware requirements, and a clear, data-driven recommendation on which to pick.
| Spec | Phi-4 | Qwen3 14B |
|---|---|---|
| Developer | Microsoft | Alibaba |
| Type | LLM (dense) | LLM (dense) |
| Parameters | 14B | 14B |
| Context window | 16K | 128K |
| Modality | Text → Text | Text → Text |
| License | MIT (open) | Apache 2.0 (open) |
| Open weights | ✅ Yes | ✅ Yes |
| Input price ($/1M) | $0.07 | $0.12 |
| Output price ($/1M) | $0.14 | $0.24 |
| VRAM (4-bit) | ~9 GB | ~9 GB |
| Min GPU (local) | RTX 4070 12GB / RTX 3060 12GB | RTX 4070 12GB (Q4) |
| Released | 2025 | 2025 |
Key differences
- Cost: Phi-4 is 71% cheaper than Qwen3 14B on a blended-token basis.
- Context: Qwen3 14B wins on context window (128K vs 16K) — better for long documents, large codebases and big RAG inputs.
- Openness: both are open-weight, so either can be self-hosted or fine-tuned. Compare their VRAM needs above to see what your GPU can run.
- Run Phi-4 locally: ~~9 GB at 4-bit (min RTX 4070 12GB / RTX 3060 12GB).
- Run Qwen3 14B locally: ~~9 GB at 4-bit (min RTX 4070 12GB (Q4)).
Which should you choose?
Choose Phi-4 if you want the lower per-token cost for high-volume workloads.
Choose Qwen3 14B if you need the larger context window.
→ Estimate real costs in the API cost calculator · check local hardware in the VRAM calculator · browse all 30+ models.
All specs and prices are pulled live from our AI models database and kept current. Compare either model against others, or estimate your own monthly spend with the free calculators above.