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Gemini 1.0 Pro vs Phi-4-multimodal-instruct
Comprehensive side-by-side LLM comparison
Phi-4-multimodal-instruct leads with 11.5% higher average benchmark score. Phi-4-multimodal-instruct offers 215.0K more tokens in context window than Gemini 1.0 Pro. Phi-4-multimodal-instruct is $1.85 cheaper per million tokens. Phi-4-multimodal-instruct supports multimodal inputs. Overall, Phi-4-multimodal-instruct is the stronger choice for coding tasks.
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Gemini 1.0 Pro was developed as Google's initial production-ready multimodal model, designed to handle text and provide strong performance across diverse tasks. Built to serve as a versatile foundation for applications requiring reliable language understanding and generation, it introduced the Gemini architecture to developers and enterprises.
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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.
11 months newer
Gemini 1.0 Pro
2024-02-15
Phi-4-multimodal-instruct
Microsoft
2025-02-01
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Pricing Comparison
Cost per million tokens (USD)
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Gemini 1.0 Pro
Input:$0.50
Output:$1.50
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Phi-4-multimodal-instruct
Input:$0.05
Output:$0.10($1.85 cheaper)
Performance Metrics
Context window and performance specifications
Average performance across 2 common benchmarks
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Gemini 1.0 Pro
Average Score:47.3%
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Phi-4-multimodal-instruct
Average Score:58.8%(+11.5%)
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Knowledge Cutoff
Training data recency comparison
Gemini 1.0 Pro
2024-02-01
Phi-4-multimodal-instruct
2024-06-01
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 1.0 Pro
1 providers
Throughput: 120 tok/s
Latency: 0.4ms
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Phi-4-multimodal-instruct
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Gemini 1.0 Pro
Avg Score:47.3%
Providers:1
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Phi-4-multimodal-instruct
Avg Score:58.8%(+11.5%)
Providers:1