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DeepSeek VL2 Tiny vs Phi-4-multimodal-instruct
Comprehensive side-by-side LLM comparison
Phi-4-multimodal-instruct leads with 6.8% higher average benchmark score. Overall, Phi-4-multimodal-instruct is the stronger choice for coding tasks.
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DeepSeek
DeepSeek-VL2-Tiny was developed as an ultra-efficient vision-language model, designed for deployment in resource-constrained environments. Built to enable multimodal AI on edge devices and mobile applications, it distills vision-language capabilities into a minimal footprint for widespread accessibility.
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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.
1 month newer
DeepSeek VL2 Tiny
DeepSeek
2024-12-13
Phi-4-multimodal-instruct
Microsoft
2025-02-01
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Performance Metrics
Context window and performance specifications
Average performance across 9 common benchmarks
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DeepSeek VL2 Tiny
Average Score:70.3%
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Phi-4-multimodal-instruct
Average Score:77.1%(+6.8%)
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Knowledge Cutoff
Training data recency comparison
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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DeepSeek VL2 Tiny
0 providers
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Phi-4-multimodal-instruct
1 providers
DeepInfra
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DeepSeek VL2 Tiny
Avg Score:70.3%
Providers:0
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Phi-4-multimodal-instruct
Avg Score:77.1%(+6.8%)
Providers:1