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DeepSeek VL2 vs Gemini 2.0 Flash Thinking
Comprehensive side-by-side LLM comparison
DeepSeek VL2 leads with 48.1% higher average benchmark score. Overall, DeepSeek VL2 is the stronger choice for coding tasks.
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DeepSeek
DeepSeek VL2 is a multimodal language model developed by DeepSeek. It achieves strong performance with an average score of 70.9% across 14 benchmarks. It excels particularly in DocVQA (93.3%), ChartQA (86.0%), TextVQA (84.2%). It supports a 259K token context window for handling large documents. The model is available through 1 API provider. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. Released in 2024, it represents DeepSeek's latest advancement in AI technology.
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Gemini 2.0 Flash Thinking is a multimodal language model developed by Google. It achieves strong performance with an average score of 74.3% across 3 benchmarks. Notable strengths include MMMU (75.4%), GPQA (74.2%), AIME 2024 (73.3%). As a multimodal model, it can process and understand text, images, and other input formats seamlessly. Released in 2025, it represents Google's latest advancement in AI technology.
1 month newer
DeepSeek VL2
DeepSeek
2024-12-13
Gemini 2.0 Flash Thinking
2025-01-21
Performance Metrics
Context window and performance specifications
Average performance across 16 common benchmarks
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DeepSeek VL2
Average Score:62.0%(+48.1%)
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Gemini 2.0 Flash Thinking
Average Score:13.9%
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Knowledge Cutoff
Training data recency comparison
Gemini 2.0 Flash Thinking
2024-08-01
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
1 providers
Replicate
Throughput: 22 tok/s
Latency: 0.5ms
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Gemini 2.0 Flash Thinking
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DeepSeek VL2
Avg Score:62.0%(+48.1%)
Providers:1
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Gemini 2.0 Flash Thinking
Avg Score:13.9%
Providers:0