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DeepSeek-R1 vs Gemini 2.0 Flash Thinking
Comprehensive side-by-side LLM comparison
Gemini 2.0 Flash Thinking supports multimodal inputs. DeepSeek-R1 is available on 5 providers. Both models have their strengths depending on your specific coding needs.
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DeepSeek
DeepSeek-R1 is a language model developed by DeepSeek. It supports a 262K token context window for handling large documents. The model is available through 5 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.
1 days newer
DeepSeek-R1
DeepSeek
2025-01-20
Gemini 2.0 Flash Thinking
2025-01-21
Performance Metrics
Context window and performance specifications
Average performance across 3 common benchmarks
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DeepSeek-R1
Average Score:0.0%
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Gemini 2.0 Flash Thinking
Average Score:74.3%(+74.3%)
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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-R1
5 providers
DeepInfra
Throughput: 0.9 tok/s
Latency: 0.3ms
DeepSeek
Throughput: 9 tok/s
Latency: 0.3ms
Fireworks
Throughput: 2.1 tok/s
Latency: 0.3ms
Together
Throughput: 4 tok/s
Latency: 0.6ms
ZeroEval
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DeepSeek-R1
Avg Score:0.0%
Providers:5
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Gemini 2.0 Flash Thinking
Avg Score:74.3%(+74.3%)
Providers:0