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Gemini 2.5 Pro vs Phi-4-multimodal-instruct
Comprehensive side-by-side LLM comparison
Gemini 2.5 Pro leads with 27.2% higher average benchmark score. Gemini 2.5 Pro offers 858.1K more tokens in context window than Phi-4-multimodal-instruct. Phi-4-multimodal-instruct is $11.10 cheaper per million tokens. Overall, Gemini 2.5 Pro is the stronger choice for coding tasks.
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Gemini 2.5 Pro was developed as Google's most intelligent AI model, designed to comprehend vast datasets and challenging problems from diverse information sources including text, audio, images, and video. Built to handle complex reasoning and multi-step problem solving, it represents Google's flagship offering for enterprise and advanced applications.
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Microsoft
Phi-4 Multimodal was created to handle multiple input modalities including text, images, and potentially other formats. Built to extend Phi-4's efficiency into multimodal applications, it demonstrates that compact models can successfully integrate diverse information types.
3 months newer
Phi-4-multimodal-instruct
Microsoft
2025-02-01
Gemini 2.5 Pro
2025-05-20
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Pricing Comparison
Cost per million tokens (USD)
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Gemini 2.5 Pro
Input:$1.25
Output:$10.00
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Phi-4-multimodal-instruct
Input:$0.05
Output:$0.10($11.10 cheaper)
Performance Metrics
Context window and performance specifications
Average performance across 2 common benchmarks
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Gemini 2.5 Pro
Average Score:82.2%(+27.2%)
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Phi-4-multimodal-instruct
Average Score:55.0%
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Knowledge Cutoff
Training data recency comparison
Phi-4-multimodal-instruct
2024-06-01
Gemini 2.5 Pro
2025-01-31
More recent knowledge cutoff means awareness of newer technologies and frameworks
Provider Availability & Performance
Available providers and their performance metrics
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Gemini 2.5 Pro
2 providers
Throughput: 85 tok/s
Latency: 0.7ms
ZeroEval
Throughput: 85 tok/s
Latency: 0.7ms
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Gemini 2.5 Pro
Avg Score:82.2%(+27.2%)
Providers:2
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Phi-4-multimodal-instruct
Avg Score:55.0%
Providers:1