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DeepSeek VL2 Small vs Phi-4-multimodal-instruct
Comprehensive side-by-side LLM comparison
Both models show comparable benchmark performance. Both models have their strengths depending on your specific coding needs.
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DeepSeek
DeepSeek-VL2-Small was created as a compact vision-language variant, designed to bring multimodal capabilities to applications with limited computational resources. Built to provide visual and textual understanding in a more efficient package, it serves use cases requiring practical deployment of vision-language AI.
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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 Small
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 Small
Average Score:76.5%
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Phi-4-multimodal-instruct
Average Score:77.1%(+0.6%)
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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 Small
0 providers
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Phi-4-multimodal-instruct
1 providers
DeepInfra
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DeepSeek VL2 Small
Avg Score:76.5%
Providers:0
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Phi-4-multimodal-instruct
Avg Score:77.1%(+0.6%)
Providers:1