Phi-4
MicrosoftLarge Language ModelOpen SourceMicrosoft's compact but powerful model designed to maximize quality-per-parameter. Excels at reasoning and coding despite its small size.
Abilities
Use Cases
Available in Tools
Availability
How to Use
Pros
- Fully open source with MIT license — maximum freedom for any use case
- Impressive quality for 14B — punches well above its weight on reasoning and STEM
- Runs on consumer GPUs (8GB VRAM) and even CPUs with quantization
- Ideal for edge deployment — laptops, phones, IoT devices
- Excellent training data quality — curated synthetic data approach
Cons
- Limited context window (16K) — much shorter than frontier models
- Weaker on creative writing, open-ended conversation, and nuanced tasks
- Cannot compete with 70B+ models on complex multi-step reasoning
- No vision/multimodal capabilities in the base model
- Small model size means lower factual knowledge coverage
What to Use It For
Perfect For
Runs on CPUs with quantization — small enough for laptops, phones, and IoT devices without GPU
MIT license, small size, and easy setup make it the best model for hands-on AI learning
All data stays on your machine — no API calls, no data sharing, complete privacy
Good For
Surprisingly strong code generation for its size — handles common programming tasks well
Good reasoning on STEM topics and fast enough for interactive use on modest hardware
Not Recommended
14B parameters cannot match the depth of 70B+ models on hard reasoning chains
Try instead: Claude Opus 4, o3
16K context window is far too short for book-length or multi-document tasks
Try instead: Gemini 2.5 Pro
Do Not Use For
Quality gap with frontier models is too large for customer-facing enterprise applications
Try instead: Claude Sonnet 4
Small model lacks the nuance, style range, and coherence needed for professional-grade creative text
Try instead: Claude Opus 4