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DeepSeek VL2 Tiny vs Phi-3.5-vision-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-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-3.5 Vision was developed as a multimodal variant of Phi-3.5, designed to understand and reason about both images and text. Built to extend the Phi family's efficiency into vision-language tasks, it enables compact multimodal AI for practical applications.
3 months newer
Phi-3.5-vision-instruct
Microsoft
2024-08-23
DeepSeek VL2 Tiny
DeepSeek
2024-12-13
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Average performance across 6 common benchmarks
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DeepSeek VL2 Tiny
Average Score:66.1%
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Phi-3.5-vision-instruct
Average Score:66.8%(+0.6%)
Provider Availability & Performance
Available providers and their performance metrics
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DeepSeek VL2 Tiny
0 providers
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Phi-3.5-vision-instruct
0 providers
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DeepSeek VL2 Tiny
Avg Score:66.1%
Providers:0
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Phi-3.5-vision-instruct
Avg Score:66.8%(+0.6%)
Providers:0