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Phi 4 Mini Instruct
Phi 4 Mini Instruct
Phi 4 Mini Instruct is a lightweight (3.8B parameters) open-source model built on synthetic data and filtered web data, focusing on high-quality reasoning. It supports a context length of up to 128K tokens and enhances instruction-following ability and security through supervised fine-tuning and direct preference optimization.
Intelligence(Relatively Weak)
Speed(Relatively Slow)
Input Supported Modalities
No
Is Reasoning Model
128,000
Context Window
128,000
Maximum Output Tokens
2024-06-01
Knowledge Cutoff
Pricing
- /M tokens
Input
- /M tokens
Output
- /M tokens
Blended Price
Quick Simple Comparison
Phi-4 Mini Instruct
Phi-3 Medium Instruct 14B
￥0.1
Phi-4 Multimodal Instruct
￥0.05
Basic Parameters
GPT-4.1 Technical Parameters
Parameter Count
3,840.0M
Context Length
128.00k tokens
Training Data Cutoff
2024-06-01
Open Source Category
Open Weights (Permissive License)
Multimodal Support
Text Only
Throughput
Release Date
2024-02-26
Response Speed
56.57,084 tokens/s
Benchmark Scores
Below is the performance of claude-monet in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.
Intelligence Index
2596
Large Language Model Intelligence Level
Coding Index
1168
Indicator of AI model performance on coding tasks
Math Index
-
Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks
MMLU Pro
46.5
Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video
GPQA
33.1
Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions
HLE
4.2
The model's comprehensive average score on the Hugging Face Open LLM Leaderboard
LiveCodeBench
12.6
Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems
SciCode
10.8
The model's capability in code generation for scientific computing or specific scientific domains
HumanEval
74.3
Score achieved by the AI model on the specific HumanEval benchmark test set
Math 500 Score
69.6
Score on the first 500 larger, more well-known mathematical benchmark tests
AIME Score
3
An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)
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