Phi-4 Multimodal Instruct vs Phi-4 Mini Instruct

Comparing 2 AI models · 6 benchmarks · Microsoft Azure

Most Affordable
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Phi-4 Multimodal Instruct
$0.00/1M
Highest Intelligence
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Phi-4 Mini Instruct
33.1% GPQA
Best for Coding
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Phi-4 Mini Instruct
3.6 Coding Index
Price Difference
NaNx
input cost range

Composite Indices

Intelligence, Coding, Math

Standard Benchmarks

Academic and industry benchmarks

Benchmark Winners

6 tests
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Phi-4 Multimodal Instruct

3
  • MMLU Pro
  • HLE
  • LiveCodeBench
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Phi-4 Mini Instruct

3
  • GPQA
  • MATH 500
  • AIME 2025
Metric
Microsoft Azure logo Phi-4 Multimodal Instruct
Microsoft Azure
Microsoft Azure logo Phi-4 Mini Instruct
Microsoft Azure
Pricing
Per 1M tokens
Input Cost $0.00/1M $0.00/1M
Output Cost $0.00/1M $0.00/1M
Specifications
Organization Model creator
Microsoft Azure Microsoft Azure
Release Date Launch date
Feb 26, 2025 Feb 26, 2024
Performance & Speed
Throughput Output speed
17.0 tok/s 44.0 tok/s
Time to First Token (TTFT) Initial response delay
343ms 320ms
Latency Time to first answer token
343ms 320ms
Composite Indices
Intelligence Index Overall reasoning capability
10.0 8.3
Coding Index Programming ability
3.6
Math Index Mathematical reasoning
6.7
Standard Benchmarks
GPQA Graduate-level reasoning
31.5% 33.1%
MMLU Pro Advanced knowledge
48.5% 46.5%
HLE Hard language evaluation
4.4% 4.2%
LiveCodeBench Real-world coding tasks
13.1% 12.6%
MATH 500 Mathematical problems
69.3% 69.6%
AIME 2025 Advanced math competition
6.7%
AIME (Original) Math olympiad problems
9.3% 3.0%
SciCode Scientific code generation
11.0% 10.8%
LCR Code review capability
13.7%
IFBench Instruction-following
21.1%
TAU-bench v2 Tool use & agentic tasks
8.2%
TerminalBench Hard CLI command generation
0.0%

Key Takeaways

Phi-4 Multimodal Instruct offers the best value at $0.00/1M, making it ideal for high-volume applications and cost-conscious projects.

Phi-4 Mini Instruct leads in reasoning capabilities with a 33.1% GPQA score, excelling at complex analytical tasks and problem-solving.

Phi-4 Mini Instruct achieves a 3.6 coding index, making it the top choice for software development and code generation tasks.

All models support context windows of ∞+ tokens, suitable for processing lengthy documents and maintaining extended conversations.

When to Choose Each Model

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Phi-4 Multimodal Instruct

  • Cost-sensitive applications
  • High-volume processing
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Phi-4 Mini Instruct

  • Complex reasoning tasks
  • Research & analysis
  • Code generation
  • Software development

Cost Calculator

Microsoft Azure logo Phi-4 Multimodal Instruct
$0.00
per month
Microsoft Azure logo Phi-4 Mini Instruct
$0.00
per month

Costs are estimates based on API pricing. Actual costs may vary based on caching, batch processing, and volume discounts.

AI Model Comparison Guide

Compare large language models (LLMs) side-by-side with detailed benchmark scores, pricing, and performance metrics. Our interactive comparison tool helps you evaluate AI models from OpenAI, Anthropic, Google, Meta, DeepSeek, and other leading providers. Use our AI model leaderboard to discover more models to compare.

Understanding Composite Indices

  • Intelligence Index: Aggregated score combining MMLU-Pro, GPQA, and HLE benchmarks - measures overall reasoning and knowledge capabilities
  • Coding Index: Composite metric from LiveCodeBench, SciCode, and LiveCodeBench Review - evaluates programming proficiency across multiple languages
  • Math Index: Combined score from AIME, AIME 2025, and MATH-500 benchmarks - assesses mathematical reasoning from high school to competition level

Key Comparison Metrics

  • Benchmark Scores: Standardized tests measuring intelligence, coding, math, and specialized capabilities - higher percentages indicate better performance
  • Pricing Analysis: Compare input and output token costs across models - critical for budgeting API usage and scaling applications
  • Performance Metrics: Throughput (tokens/second) and latency measurements for real-time application planning
  • Context Windows: Maximum token capacity for processing documents and maintaining conversation history

How to Compare AI Models Effectively

Performance vs Cost

Balance benchmark scores against token pricing - flagship models offer 10-15% better performance but cost 5-10x more than smaller alternatives

Task-Specific Selection

Prioritize relevant indices: coding index for development tasks, math index for STEM applications, intelligence index for general reasoning

Real-World Testing

Use our free AI chat interface to test models with your specific prompts before committing to API integration

All benchmark scores, pricing data, and performance metrics are sourced from Artificial Analysis and updated daily. Compare models by intelligence, coding ability, math performance, speed, cost, or release date using our comprehensive AI model leaderboard.