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DeepSeek VL2 vs Qwen2-VL-72B-Instruct
Comprehensive side-by-side LLM comparison
Qwen2-VL-72B-Instruct leads with 4.9% higher average benchmark score. Both models have their strengths depending on your specific coding needs.
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DeepSeek
DeepSeek-VL2 was developed as a vision-language model, designed to handle both visual and textual inputs for multimodal understanding tasks. Built to extend DeepSeek's capabilities beyond text-only processing, it enables applications requiring integrated analysis of images and language.
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Alibaba Cloud / Qwen Team
Qwen2-VL 72B was developed as a large vision-language model, designed to handle multimodal tasks combining visual and textual understanding. Built with 72 billion parameters for integrated vision and language processing, it enables applications requiring sophisticated analysis of images alongside text.
3 months newer
Qwen2-VL-72B-Instruct
Alibaba Cloud / Qwen Team
2024-08-29
DeepSeek VL2
DeepSeek
2024-12-13
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Performance Metrics
Context window and performance specifications
Average performance across 4 common benchmarks
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DeepSeek VL2
Average Score:79.9%
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Qwen2-VL-72B-Instruct
Average Score:84.8%(+4.9%)
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Knowledge Cutoff
Training data recency comparison
Qwen2-VL-72B-Instruct
2023-06-30
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
1 providers
Replicate
Throughput: 22 tok/s
Latency: 0.5ms
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Qwen2-VL-72B-Instruct
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DeepSeek VL2
Avg Score:79.9%
Providers:1
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Qwen2-VL-72B-Instruct
Avg Score:84.8%(+4.9%)
Providers:0