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DeepSeek-V3 vs Qwen2-VL-72B-Instruct
Comprehensive side-by-side LLM comparison
Qwen2-VL-72B-Instruct supports multimodal inputs. Both models have their strengths depending on your specific coding needs.
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DeepSeek
DeepSeek-V3 was introduced as a major architectural advancement, developed with 671B mixture-of-experts parameters and trained on 14.8 trillion tokens. Built to be three times faster than V2 while maintaining open-source availability, it demonstrates competitive performance against frontier closed-source models and represents a significant leap in efficient large-scale model design.
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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-V3
DeepSeek
2024-12-25
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Performance Metrics
Context window and performance specifications
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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-V3
1 providers
DeepSeek
Throughput: 100 tok/s
Latency: 0.5ms
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Qwen2-VL-72B-Instruct
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DeepSeek-V3
Avg Score:0.0%
Providers:1
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Qwen2-VL-72B-Instruct
Avg Score:0.0%
Providers:0