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DeepSeek-V3 vs Gemini 2.0 Flash Thinking
Comprehensive side-by-side LLM comparison
DeepSeek-V3 leads with 53.4% higher average benchmark score. Gemini 2.0 Flash Thinking supports multimodal inputs. Overall, DeepSeek-V3 is the stronger choice for coding tasks.
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DeepSeek
DeepSeek-V3 is a language model developed by DeepSeek. It achieves strong performance with an average score of 67.2% across 20 benchmarks. It excels particularly in DROP (91.6%), CLUEWSC (90.9%), MATH-500 (90.2%). It supports a 262K token context window for handling large documents. The model is available through 1 API provider. 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.
27 days newer
DeepSeek-V3
DeepSeek
2024-12-25
Gemini 2.0 Flash Thinking
2025-01-21
Performance Metrics
Context window and performance specifications
Average performance across 21 common benchmarks
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DeepSeek-V3
Average Score:64.0%(+53.4%)
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Gemini 2.0 Flash Thinking
Average Score:10.6%
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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-V3
1 providers
DeepSeek
Throughput: 100 tok/s
Latency: 0.5ms
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Gemini 2.0 Flash Thinking
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DeepSeek-V3
Avg Score:64.0%(+53.4%)
Providers:1
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Gemini 2.0 Flash Thinking
Avg Score:10.6%
Providers:0