Model

Phi-4 Mini Instruct

by Microsofttext->text

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Phi-4-mini-instruct is a lightweight open model built upon synthetic data and filtered publicly available websites with a focus on high-quality, reasoning dense data. The model belongs to the Phi-4 model family and supports 128K token context length. It employs new architecture for efficiency, larger vocabulary for multilingual support, and better post-training techniques for instruction following and function calling.

Evaluations

across 6 benchmarks

Median latency

321ms

Context length

128k

tokens

Cost

$0.08 · $0.35

input · output per 1M tokens

Key takeaways

Phi-4 Mini Instruct is a lightweight, open-source Transformer-based language model (3.8B parameters) designed for instruction following and function calling, capable of a 128,000-token context length.
It was trained using high-quality, reasoning-dense synthetic data and filtered public web content, employing a new architecture for efficiency, a larger vocabulary for multilingual support, and improved post-training techniques.
The model demonstrates strengths in direct arithmetic, basic algebra, pattern recognition, and consistent adherence to output formats. It struggles significantly with complex reasoning, multi-step word problems, geometric calculations, and subtle contextual nuances.
Best performance is observed on direct mathematical calculations and proportional reasoning tasks from the AGIEval English dataset, particularly on SAT Math and Aqua-Rat. Specific accuracy was 43.40% on AGIEval English.
Worst performance is seen in advanced mathematical problem-solving, achieving 0% accuracy on the AIME 2025 dataset. This is primarily due to pervasive arithmetical errors, logical inconsistencies, and misapplication of complex mathematical principles.

Technical specifications

Context length: 128k tokens
Max output: —
Architecture: Transformer
Modalities: text->text
Parameters: ~3.8B
Open weights: Yes
Licence: MIT
Regions: USA
Provider: Microsoft
Provider model key: microsoft/phi-4-mini-instruct