Phi 3 mini 4k instruct

Name: Phi 3 mini 4k instruct
Author: Microsoft

by MicrosoftPhi 3

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

Released April 20244K context≈ 3,072 words3.8B params

Phi-3 Mini punches above its weight class for a 3.8B parameter model, showing surprisingly capable reasoning and instruction-following for its size. It reflects Microsoft's research focus on training efficiency — getting strong language understanding from small, carefully curated datasets rather than raw scale. The trade-off is a 4k context window and the natural ceiling that comes with fewer parameters, so complex multi-step tasks may hit limits.

Capabilities

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

Instruction Following

Moderate

Factual Knowledge

Phi 3 mini 4k instruct

by MicrosoftPhi 3

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

Released April 20244K context≈ 3,072 words3.8B params

Phi-3 Mini punches above its weight class for a 3.8B parameter model, showing surprisingly capable reasoning and instruction-following for its size. It reflects Microsoft's research focus on training efficiency — getting strong language understanding from small, carefully curated datasets rather than raw scale. The trade-off is a 4k context window and the natural ceiling that comes with fewer parameters, so complex multi-step tasks may hit limits.

Capabilities

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

Instruction Following

Moderate

Factual Knowledge

Glossary

Context WindowThe maximum number of tokens a model can process in a single conversation or prompt.Instruction-FollowingThe ability of a model to understand and execute specific tasks or commands given in natural language prompts.Multi-Step TasksProblems or workflows that require a model to perform multiple sequential operations or reasoning steps to reach a final answer.Parameter ModelA neural network described by the number of learnable weights it contains; more parameters generally mean greater capacity to learn complex patterns, but also require more computational resources.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.Training EfficiencyThe ability to achieve strong model performance while using less computational resources, data, or time during the training process.

Phi 3 mini 4k instruct

Capabilities

Phi 3 mini 4k instruct

Capabilities

Benchmark Scores

Use Case Fit

Glossary