Phi 3.5 mini instruct

Name: Phi 3.5 mini instruct
Author: Microsoft

by MicrosoftPhi 3.5

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

Released August 2024131K context≈ 98,304 words3.8B params

Phi 3.5 Mini punches well above its weight for a 3.8B parameter model, handling reasoning, coding, and instruction-following with surprising competence. It reflects Microsoft's research focus on data quality over raw scale — trained on carefully curated datasets rather than simply more parameters. The trade-off is real though: complex multi-step tasks or deep domain knowledge will expose its size limits compared to larger siblings.

Capabilities

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

Factual Knowledge

Moderate

Coding

Phi 3.5 mini instruct

by MicrosoftPhi 3.5

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

Released August 2024131K context≈ 98,304 words3.8B params

Phi 3.5 Mini punches well above its weight for a 3.8B parameter model, handling reasoning, coding, and instruction-following with surprising competence. It reflects Microsoft's research focus on data quality over raw scale — trained on carefully curated datasets rather than simply more parameters. The trade-off is real though: complex multi-step tasks or deep domain knowledge will expose its size limits compared to larger siblings.

Capabilities

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

Factual Knowledge

Moderate

Coding

Glossary

Data QualityThe relevance, accuracy, and usefulness of training data, which can be more important for model performance than simply having more data.Domain KnowledgeSpecialized expertise and facts about a particular field or subject area that an AI model has learned during training.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.

Phi 3.5 mini instruct

Capabilities

Phi 3.5 mini instruct

Capabilities

Benchmark Scores

Use Case Fit

Glossary