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DeepSeek-R1 vs Gemini 2.0 Flash-Lite
Comprehensive side-by-side LLM comparison
Gemini 2.0 Flash-Lite offers 794.6K more tokens in context window than DeepSeek-R1. Gemini 2.0 Flash-Lite is $2.37 cheaper per million tokens. Gemini 2.0 Flash-Lite 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 Lite was created as an even more efficient variant of Gemini 2.0 Flash, designed for applications where minimal latency and maximum cost-effectiveness are essential. Built to bring next-generation multimodal capabilities to resource-constrained deployments, it optimizes for speed and affordability.
16 days newer
DeepSeek-R1
DeepSeek
2025-01-20
Gemini 2.0 Flash-Lite
2025-02-05
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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-Lite
Input:$0.07
Output:$0.30($2.37 cheaper)
Performance Metrics
Context window and performance specifications
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Knowledge Cutoff
Training data recency comparison
Gemini 2.0 Flash-Lite
2024-06-01
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-Lite
Avg Score:0.0%
Providers:1