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DeepSeek VL2 vs Qwen2.5-Omni-7B
Comprehensive side-by-side LLM comparison
Qwen2.5-Omni-7B leads with 2.6% 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
Qwen2.5-Omni 7B was created as a multimodal model supporting text, audio, and other modalities, designed to provide integrated understanding across diverse input types. Built with 7 billion parameters for efficient omni-modal processing, it extends AI capabilities beyond traditional text-only or vision-language boundaries.
3 months newer
DeepSeek VL2
DeepSeek
2024-12-13
Qwen2.5-Omni-7B
Alibaba Cloud / Qwen Team
2025-03-27
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Performance Metrics
Context window and performance specifications
Average performance across 9 common benchmarks
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DeepSeek VL2
Average Score:74.2%
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Qwen2.5-Omni-7B
Average Score:76.8%(+2.6%)
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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Qwen2.5-Omni-7B
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DeepSeek VL2
Avg Score:74.2%
Providers:1
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Qwen2.5-Omni-7B
Avg Score:76.8%(+2.6%)
Providers:0