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Phi 3 Mini Instruct 3.8B
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Phi 3 Mini Instruct 3.8B

Phi-3.5-mini-instruct is a 3.8B parameter model that supports up to 128K context tokens and has improved multilingual capabilities across more than 20 languages. It has received additional training and safety post-training to enhance instruction following, reasoning, mathematics, and code generation. It uses the MIT license and is well-suited for environments with memory or latency constraints.
Intelligence(Relatively Weak)
Speed(Slow)
Input Supported Modalities
No
Is Reasoning Model
4,096
Context Window
128,000
Maximum Output Tokens
2023-10-01
Knowledge Cutoff
Go Compare

Pricing

￥0.72 /M tokens
Input
￥0.72 /M tokens
Output
- /M tokens
Blended Price

Quick Simple Comparison

Input

Output

Phi-4 Mini Instruct
Phi-4 Multimodal Instruct
￥0.05
Phi-3 Medium Instruct 14B
￥0.1

Basic Parameters

Phi-3 Mini Instruct 3.8BTechnical Parameters
Parameter Count
3,800.0M
Context Length
4,096 tokens
Training Data Cutoff
2023-10-01
Open Source Category
Open Weights (Permissive License)
Multimodal Support
Text Only
Throughput
23
Release Date
2024-04-23
Response Speed
0 tokens/s

Benchmark Scores

Below is the performance of Phi-3 Mini Instruct 3.8B in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.
Intelligence Index
22.1
Large Language Model Intelligence Level
Coding Index
10.33
Indicator of AI model performance on coding tasks
Math Index
24.83
Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks
MMLU Pro
43.5
Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video
GPQA
31.9
Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions
HLE
4.4
The model's comprehensive average score on the Hugging Face Open LLM Leaderboard
LiveCodeBench
11.6
Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems
SciCode
9
The model's capability in code generation for scientific computing or specific scientific domains
HumanEval
25.1
Score achieved by the AI model on the specific HumanEval benchmark test set
Math 500 Score
45.7
Score on the first 500 larger, more well-known mathematical benchmark tests
AIME Score
4
An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)
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