Phi-4 is a 14-billion parameter small language model from Microsoft Research, part of the Phi series known for achieving outsized reasoning capability relative to model size. Microsoft trained Phi-4 with a heavy emphasis on synthetic data quality, specifically targeting mathematical reasoning, multi-step problem solving, and instruction-following benchmarks where it competes with models several times larger.
It is positioned as a practical choice for developers who need strong reasoning in a model that can run efficiently on constrained hardware or be fine-tuned without enterprise-scale compute. Phi-4 supports coding, logical deduction, and analytical tasks particularly well, and its small footprint makes local deployment or fine-tuning accessible.
Key Features
Strong mathematical and multi-step reasoning for a 14B model
High performance on coding and logical deduction tasks
Efficient inference enabling deployment on consumer or edge hardware
Quality synthetic-data training methodology for reasoning robustness
Fine-tuning friendly due to manageable parameter count
Competitive instruction-following against larger open-weight models
Ideal Use Cases
On-premise or local deployment where a capable but small model is needed
Math tutoring, problem-solving assistance, and STEM education tools
Code generation and debugging in resource-constrained environments
Fine-tuning base for domain-specific reasoning applications
Research into data-efficient training and small-model capabilities
Example Prompts for Phi-4
Technical Specifications
| Provider | Microsoft |
| Category | Text |
| Modality | Text -> Text |
| Context Window | 16K tokens |
Frequently Asked Questions
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