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DeepSeek-R1 vs Gemini 2.0 Flash
Comprehensive side-by-side LLM comparison
Gemini 2.0 Flash offers 794.6K more tokens in context window than DeepSeek-R1. Gemini 2.0 Flash is $2.24 cheaper per million tokens. Gemini 2.0 Flash 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 was developed as a reasoning-focused language model, designed to combine chain-of-thought reasoning with reinforcement learning techniques. Built to excel at complex problem-solving through trial-and-error learning and deliberate analytical processes, it demonstrates the power of efficient training methods in open-source model development.
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Gemini 2.0 Flash was developed as the next generation of Google's fast multimodal model, designed to provide improved performance while maintaining the speed and efficiency that defines the Flash family. Built with enhanced reasoning and generation capabilities, it serves applications requiring both quality and responsiveness.
1 month newer
Gemini 2.0 Flash
2024-12-01
DeepSeek-R1
DeepSeek
2025-01-20
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Pricing Comparison
Cost per million tokens (USD)
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DeepSeek-R1
Input:$0.55
Output:$2.19
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Gemini 2.0 Flash
Input:$0.10
Output:$0.40($2.24 cheaper)
Performance Metrics
Context window and performance specifications
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Knowledge Cutoff
Training data recency comparison
Gemini 2.0 Flash
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
Avg Score:0.0%
Providers:1