Phi 4 mini instruct

Name: Phi 4 mini instruct
Author: Microsoft

by MicrosoftPhi 4

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released February 2025131K context≈ 98,304 words3.8B params

Phi 4 mini instruct punches above its weight for a sub-4B model, handling instruction-following, reasoning, and coding tasks with surprising coherence. It reflects Microsoft's research focus on data quality over raw scale — trained on carefully curated synthetic and web data rather than simply more parameters. Expect solid performance on focused, well-scoped tasks, but it will show its size limits on complex multi-step reasoning or broad world knowledge.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Long Context

Strong

Instruction Following

Phi 4 mini instruct

by MicrosoftPhi 4

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released February 2025131K context≈ 98,304 words3.8B params

Phi 4 mini instruct punches above its weight for a sub-4B model, handling instruction-following, reasoning, and coding tasks with surprising coherence. It reflects Microsoft's research focus on data quality over raw scale — trained on carefully curated synthetic and web data rather than simply more parameters. Expect solid performance on focused, well-scoped tasks, but it will show its size limits on complex multi-step reasoning or broad world knowledge.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Long Context

Strong

Instruction Following

Glossary

CoherenceThe quality of maintaining consistent meaning and logical flow across multiple sentences or exchanges in a conversation.Data QualityThe relevance, accuracy, and usefulness of training data, which can be more important for model performance than simply having more data.Instruction-FollowingThe ability of a model to understand and execute specific tasks or commands given in natural language prompts.Multi-Step ReasoningThe ability to break down complex problems into smaller steps and solve them sequentially, rather than jumping directly to an answer.ParametersThe learned numerical values in a model — more parameters generally means more capacity but higher compute cost.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.World KnowledgeA model's learned understanding of facts, concepts, and relationships about the real world, typically acquired during training on diverse text data.

Phi 4 mini instruct

Capabilities

Phi 4 mini instruct

Capabilities

Benchmark Scores

Use Case Fit

Glossary