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DeepSeek-V3.1 vs Gemini 2.0 Flash Thinking
Comprehensive side-by-side LLM comparison
DeepSeek-V3.1 leads with 41.9% higher average benchmark score. Gemini 2.0 Flash Thinking supports multimodal inputs. DeepSeek-V3.1 is available on 2 providers. Overall, DeepSeek-V3.1 is the stronger choice for coding tasks.
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DeepSeek
DeepSeek-V3.1 is a language model developed by DeepSeek. The model shows competitive results across 16 benchmarks. It excels particularly in SimpleQA (93.4%), MMLU-Redux (91.8%), MMLU-Pro (83.7%). It supports a 328K token context window for handling large documents. The model is available through 2 API providers. It's licensed for commercial use, making it suitable for enterprise applications. Released in 2025, 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.
11 days newer
DeepSeek-V3.1
DeepSeek
2025-01-10
Gemini 2.0 Flash Thinking
2025-01-21
Performance Metrics
Context window and performance specifications
Average performance across 17 common benchmarks
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DeepSeek-V3.1
Average Score:55.0%(+41.9%)
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Gemini 2.0 Flash Thinking
Average Score:13.1%
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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
2 providers
DeepInfra
Novita
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Gemini 2.0 Flash Thinking
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DeepSeek-V3.1
Avg Score:55.0%(+41.9%)
Providers:2
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Gemini 2.0 Flash Thinking
Avg Score:13.1%
Providers:0