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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 1.2% 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. Both models have their strengths depending on your specific coding needs.
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Gemini 2.5 Pro is a multimodal language model developed by Google. It achieves strong performance with an average score of 69.6% across 16 benchmarks. It excels particularly in MRCR (93.0%), AIME 2024 (92.0%), Global-MMLU-Lite (88.6%). With a 1.1M token context window, it can handle extensive documents and complex multi-turn conversations. The model is available through 2 API providers. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. Released in 2025, it represents Google's latest advancement in AI technology.
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Microsoft
Phi-4-multimodal-instruct is a multimodal language model developed by Microsoft. It achieves strong performance with an average score of 72.0% across 15 benchmarks. It excels particularly in ScienceQA Visual (97.5%), DocVQA (93.2%), MMBench (86.7%). It supports a 256K token context window for handling large documents. The model is available through 1 API provider. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. It's licensed for commercial use, making it suitable for enterprise applications. Released in 2025, it represents Microsoft's latest advancement in AI technology.
3 months newer
Phi-4-multimodal-instruct
Microsoft
2025-02-01
Gemini 2.5 Pro
2025-05-20
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 29 common benchmarks
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Gemini 2.5 Pro
Average Score:38.4%(+1.2%)
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Phi-4-multimodal-instruct
Average Score:37.3%
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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:38.4%(+1.2%)
Providers:2
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Phi-4-multimodal-instruct
Avg Score:37.3%
Providers:1