Capability · Comparison
Microsoft Phi-4 vs Phi-3.5-mini
Microsoft's Phi family leans on curated 'textbook-quality' data instead of raw scale. Phi-3.5-mini (3.8B) is the one you put on a phone; Phi-4 (14B) is the one you put on a single consumer GPU. Both punch well above their size on reasoning, but they're optimising for different deployment realities.
Side-by-side
| Criterion | Phi-3.5-mini | Phi-4 |
|---|---|---|
| Parameters | 3.8B | 14B |
| License | MIT | MIT |
| Context window | 128,000 tokens | 16,000 tokens |
| Reasoning (MMLU / GPQA) | Strong for size | Competitive with 30B-class models |
| Math benchmarks | Decent | Clearly stronger — textbook-style reasoning |
| Hardware to serve fp16 | 8GB GPU or mobile NPU | Single 24GB GPU |
| Best deployment | Edge / on-device / laptop | Self-hosted assistants, lab setups |
| Best fit | Mobile apps and offline demos | Cost-efficient backend reasoning |
Verdict
If you need an LLM to ship inside a mobile app or run offline on a student laptop, Phi-3.5-mini is astonishing for its size and should be your default small model. If you have a single 24GB GPU and want strong reasoning without renting an API, Phi-4 closes a lot of the gap to 30B-class open models. Both are MIT-licensed, so commercial use is straightforward.
When to choose each
Choose Phi-3.5-mini if…
- You need to run on-device (phone, laptop, Jetson).
- You need 128k context at tiny model size.
- You're building an offline demo for a VSET hackathon booth.
- Battery and memory matter more than raw accuracy.
Choose Phi-4 if…
- You have a single 24GB GPU and want the strongest reasoning per GPU.
- Your workload is math, logic, or structured problem-solving.
- You'd rather self-host than pay per-token for easy tasks.
- 16k context is enough for your use case.
Frequently asked questions
Why is Phi-4 only 16k context when Phi-3.5-mini is 128k?
Phi-4 was tuned for reasoning quality with denser attention, and Microsoft explicitly traded context length for reasoning depth. Future Phi releases may extend it.
Can I fine-tune Phi-4 on a single GPU?
LoRA / QLoRA fine-tuning fits comfortably on a single 24GB GPU for Phi-4. Full fine-tuning needs an 80GB card or offloading.
Which Phi model is better for a VSET major project?
For reasoning-style projects (tutoring, math, code help), Phi-4 on an IDEA Lab GPU is the strong pick. For edge and offline demos, Phi-3.5-mini is easier to show off on a laptop.
Sources
- Microsoft — Phi-4 technical report — accessed 2026-04-20
- Microsoft — Phi-3.5 blog — accessed 2026-04-20